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. 2025 Jun 6;104(9):105403. doi: 10.1016/j.psj.2025.105403

Metabolome and transcriptome analyses reveal the mechanism underlying the differences in skin development between the two duck breeds during embryonic stage

Zhigang Hu a, Yingjie Cai a, Chang Cao a, Hua He b, Shun Guo c, Na Li d, Aiguo Xin e, Xiaolin Liu a,⁎
PMCID: PMC12179713  PMID: 40499236

Abstract

Skin color of poultry, an important economic trait, is related to breed, feed, environment, and other factors. In recent years, China’s duck industry has developed rapidly, and duck products are welcomed by consumers. Different skin colors of ducks have different cooking methods. Black skinned duck, such as Yulin black duck, is more popular in China because they are considered more suitable for making soup, while other skin colors, such as Pekin duck, is used for roasting. In order to gain a deeper understanding of the genetic factors associated with differences in duck skin color, the transcriptomes and metabolomes of skin between Yulin black duck and Pekin duck from 15 (BSE15 vs. PSE15), 21 (BSE21 vs. PSE21) and 27 (BSE27 vs. PSE27) days of incubation were compared and analyzed. The transcriptome results showed that a total of 187 (118 up-regulated and 69 down-regulated), 417 (91 up-regulated and 326 down-regulated) and 137 (55 up-regulated and 82 down-regulated) differentially expressed genes (DEGs) were identified from BSE15 vs. PSE15, BSE21 vs. PSE21 and BSE27 vs. PSE27, respectively. The significantly enriched GO terms of biological process were positive regulation of melanin biosynthetic process, melanin biosynthetic process, cuticle development, melanin biosynthetic process from tyrosine, and melanocyte differentiation, which were potentially related to skin growth and development. Eleven significant pathways, highly enriched by DCT, TYR, ASIP, TYRP1, KIT, PHOSPHO2, CERS3, SGPP2, SPTLC3, DEGS2, PATJ, RBP7, AOX1, ETNPPL, HPGDS, and GAD1, were melanogenesis, tyrosine metabolism, vitamin B6 metabolism, sphingolipid metabolism, protein digestion and absorption, tight junction, alpha-linolenic acid metabolism, arachidonic acid metabolism, linoleic acid metabolism, nicotinate and nicotinamide metabolism, and alanine, aspartate and glutamate metabolism, which participated in regulating the development of duck skin during embryonic stage. The significantly different metabolites (SDMs) were mainly organoheterocyclic compounds, lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, including L-tyrosine, N-arachidonyl maleimide, glycerophospho-N-palmitoyl ethanolamine, LPE 22:4, and PC(0:0/18:0). which were mainly enriched in glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, alpha-linoleic acid metabolism, and melanogenesis in metabolome, suggesting that these pathways may play important roles in skin development of duck during embryonic stage. Besides, the analysis of integrated transcriptome and metabolome indicated that the pathways, including glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism, could contribute to regulating skin development in embryonic duck. Our findings could help elucidate the genetic mechanisms underlying the development differences in duck skin color. Furthermore, the candidate genes and metabolites can be used to provide a valuable breeding strategy for the selection of specific duck breeds with ideal skin coloration.

Key words: Duck, Skin, Metabolomics, Transcriptomics, Regulatory pathways

Introduction

Skin tissue, as the largest biological organ with important functions including physical protection from injury and infection, thermal insulation, and providing the substrate for growing hairs and feathers. There are three types of stem cell populations, including epidermal stem cells (maintaining the stratified epidermis); hair follicle stem cells (providing power for the periodic growth of hair follicles); and melanocyte stem cells (which regenerate melanocytes that produce pigments, coloring the skin and hair), maintain and repair the body's protective surface (Hsu and Fuchs, 2022). The coat colour in vertebrates, including hair, feathers and skin, is primarily determined by the amount and distribution of two pigments, eumelanin (black or brown) and pheomelanin (red or yellow), which are secreted by mature melanocytes at the base of the epithelium (Galván and Solano, 2016; Naik and Farrukh, 2022). Besides, some skin-related factors, such as keratin, also influence skin color (Gautam et al., 2015). Skin pigmentation is a complex biological process, and the color of skin can be influenced by environmental factors, nutrition and heredity. Bird embryos develop independently without the mother’s body, relying on a constant incubation temperture and egg yolk to provide continuous nutrition, which are beneficial for studying the development of avian skin.

In recent years, the omics technology has provided a convenient method to reveal the mechanism of molecular differences at the biological level. Among them, transcriptomics can identify and quantify the levels of gene expression, uncovering potential mechanisms of regulation between organisms under different developmental conditions. Wu et al. (2021) found that some DEGs (TLR2B, IYD, SMOC1, ALDH1A3, CYP11A1, FHL2, TECRL, ACACB, TYR, PMEL, and GPR143) may play an important role in yellow pigment deposition in chicken skin. Khumpeerawat et al. (2021) showed that the genes, MC1R, DCT, TYRP1, TYR and ASIP, could be used as candidate markers for use as a selection tool to improve the unique black skin color in the Black-bone chicken population. Metabolomics has brought great hope for discovering biochemical alterations and related pathways, which can identify changes in organismal metabolites under various environmental conditions. Therefore, metabolomics, as an effective means of studying skin metabolites, can reveal changes in skin metabolites caused by gene regulation. In recent years, non-targeted metabolomics based on LC-MS has been widely used to study the mechanisms of skin development in animals. For example, the impact of environment, genetics, nutrition, disease, and breed on animal skin development has been explored (Ren et al., 2022; Zeng et al., 2024; Zhang et al. 2024; Li et al., 2025). The combined analysis of metabolomics and transcriptomics can greatly contribute to the understanding of the regulatory mechanisms of animal body development. Therefore, understanding the differential expression of genes and metabolites determining skin development is important to developing genetic markers to be used in selection programs designed genetically improve duck skin color.

Duck is one of the most important animals in poultry, coming second only to chicken in terms of production and consumption. Domestic duck originated from the mallard duck, and have diverged considerably by natural and artificial selection over thousands of years of domestication. Skin color is one of the key traits the poultry possesses which is used to determine the market value of bird because it affects the consumer’s purchasing decisions (Zhang et al., 2015). In recent years, people have tended to evaluate the nutritional and medicinal value of black-skinned poultry. Therefore, the selection and breeding of ducks with uniform black skin coloration is a key issue that can significantly increase the economic value of the poultry market. Yulin black duck, which is mainly distributed in Yulin city, Guangxi Province and adjacent areas, is one of the most important duck breeds used for the production of egg and meat and famous for its black skin and feathers. This duck is characterized by an all black body, including its feathers, skin, bill, and web. In the local area, Yulin black duck has been considered highly nutritional food and medicinal material, and people prefer to purchase duck with very black skin and feathers to make soup. Unlike black-skinned duck, Pekin duck is mainly used for roasting. Both Pekin duck and Yulin black duck represent different aspects of Chinese food culture. The production of melanin is related to mutations in related genes, mainly affecting the color of feathers and skin. The quantity and distribution of melanin in ducks vary by breed (Zhou et al., 2018). There is now convincing evidence that the melanin in the hair follicle is located in the barb ridge and hair matrix of the black-feathered duck, and the TYRP1, TYR and SOX10 genes expressed mainly in the duck skin tissue are significantly different between Liancheng white duck and Pekin duck (Wang et al., 2024). Studies have shown that mutations in the SOX10 gene (ZJU1.0 Chr1. g.54065419C>T and g.54070844C>T) affected duck plumage color and reproductive traits (Sarjana and Zhang, 2022). Some of their characteristic traits have attracted many researchers to conduct in-depth research, but no one has conducted a comparative study on the skin differences between the two duck embryos.

To reveal the molecular regulatory mechanism underlying differences in skin development, it is necessary to clarify the the expression patterns of target genes and metabolites among different duck breeds during embryonic period. In the present study, the differences in metabolome and transcriptome between the skin tissues of Pekin duck and Yulin black duck were investigated, and some metabolites, candidate genes and metabolic pathways involved in skin development in ducks were identified. It is hoped to make a breakthrough in research on the mechanism of color development in duck skin. The obtained results not only have important scientific significance, but also have the potential to promote the application of improved ducks in production and create higher economic value. This study will provide a reference for investigating the underlying molecular mechanisms of differences in skin development between Yulin black duck and Pekin duck during embryonic stages.

Materials and methods

Ethics statement

All procedures including handing the duck samples were approved and implemented by the Institutional Animal Care and Institutional Ethic Committee of Northwest A&F University. Necessary efforts were undertaken to minimize animal suffering.

Animal materials and treatments

A total of 100 fertilized eggs of Pekin duck and Yulin black duck were selected for the same batch of incubation and incubated in an incubator at a suitable temperature and humidity according to the procedure. The Pekin duck eggs were purchased from Shaanxi Fuqiang Hongtu Husbandry Co., Ltd., and the Yulin black duck eggs were purchased from Guangxi Yinyuan Agriculture and Animal Husbandry Technology Co., Ltd. in China. Skin samples from the breast of duck embryos were sampled at embryonic day 15 (BSE15: skin sample from Yulin black duck at embryonic day 15; PSE15: skin sample from Pekin duck at embryonic day 15. The same below), the period of secondary muscle fiber formation and also the middle stage of duck embryo incubation; embryonic day 21 (BSE21 and PSE21), the period when duck embryos begin to undergo muscle fiber atrophy; embryonic day 27 (BSE27 and PSE27), the last period of duck embryo muscle fiber atrophy; were used for experimental analysis. The breast muscles were also collected for gender identification. Samples from three stages were immediately frozen in liquid nitrogen and then stored at -80°C in a refrigerator. Genomic DNA of breast muscle was extracted, according to the phenol-chloroform protocol. The PCR with sex identification primers was performed, and the product was determined by 1.5% agarose gel electrophoresis to determine the sex of duck embryos (Hu et al., 2020).

Total RNA isolation, library construction, and sequencing

Total RNA of the duck skin was extracted with Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer instructions. The RNA concentration was detected by Nanodrop 2000 (Thermo Fisher Scientific, Waltham, CA, USA) and RIN (RNA Integrity Number) values were determined by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then, RNA integrity and quality were measured using 1% agarose electrophoresis.

Eighteen libraries were constructed according to standard procedures, and then sequencing was performed on an Illumina NovaseqTM 6000 platform (LC Bio Technology Co., Ltd. Hangzhou, China), and 150 bp paired-end reads were generated.

Analysis of sequencing data

Raw reads of fastq format were first processed using in-house perl scripts, and clean reads were obtained by removing reads containing adapters, poly-N and low-quality from the raw data. Besides, the Q20, Q30 and GC content were also calculated. All the downstream analyses were performed based on clean data with high quality.

Then the clean reads were mapped to the Anas platyrhynchos reference genome sequence by TopHat v2.0.12 (https://daehwankimlab.github.io/hisat2/), and only reads with a perfect match or one mismatch were analyzed and annotated based on the reference genome. The read numbers mapped to each gene in each sample were counted using Stringtie (v1.3.3), and the expression level of each gene in the sample was represented by calculating FPKM (number of reads per thousand base fragments per million mapped reads). The differential gene expression analysis between groups was employed by DESeq2, and genes with false discovery rate (FDR)<0.05 and |log2(fold change)|≥1 were identified as DEGs. The resulting p-values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate.

Functional enrichment analysis of DEGs

To further understand the putative function of the DEGs, pathway annotation and enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. The terms and pathways with a corrected p-value<0.05 were considered to be significantly enriched.

QRT-PCR validation

Total RNA, extracted from the samples that were consistent with the sequenced samples, was reverse transcribed to cDNA using a PrimeScriptTM RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China), and the amplification was performed with an SYBR Premix Ex Taq II (Takara, Kyoto, Japan) by a CFX Opus 96 Real-Time PCR System (Bio-Rad, California, USA). The duck β-actin was used as the housekeeping gene, and the quantification of gene expression was calculate using 2−∆∆CT method. Each sample was subjected to three technical replicates for accurate analysis. The primer sequences can be found in Table S1. Data was presented as mean±standard deviation (SD), and statistical significance between two groups was determined using one-way analysis of variance (ANOVA) followed by Dunnet’s t-test and Tukey’s test. A p-value of less than 0.05 was considered statistically significant.

Metabolite extraction and LC-MS/MS analysis

The collected samples were ground with liquid nitrogen and extracted with 120 μL of precooled 50% methanol, vortexed for 1 min, and allowed to stand at room temperature for 10 min. Then the extraction mixture was centrifuged at 4000 g for 20 min after overnight incubation at -20°C, and the supernatant was transferred to a 96-well plate. Metabolic samples were stored at -80°C prior to LC-MS analysis, and the quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants (10 μL) from all of the samples.

All chromatographic separations were performed using an UltiMate 3000 UPLC System (ThermoFisher Scientific, Bremen, Germany), and the reversed phases were separated by an ACQUITY UPLC T3 column (100mm*2.1mm, 1.8µm, Waters, Milford, USA). The column temperature was 40°C, and the flow rate was 0.3 mL/min. The mobile phase consisted of solvent A (water, 0.1% formic acid) and solvent B (Acetonitrile, 0.1% formic acid), and the solvent gradient was set as follows: 0∼0.8 min, 2% B; 0.8∼2.8 min, 2% to 70% B; 2.8∼5.6 min, 70% to 90% B; 5.6∼6.4 min, 90% to 100% B; 6.4∼8.0 min, 100% B; 8.0∼8.1 min, 100% to 2% B; 8.1∼10 min, 2% B. The injection volume for each sample was 4 µL.

A high-resolution tandem mass spectrometer Q-Exactive (Thermo Scientific), operating in both positive and negative ion modes, was used to detect metabolites eluted from the column. The precursor spectra of metabolic ion (70-1050 m/z) was collected at a resolution of 70000 to achieve the AGC of 3e6. The maximum inject time was set to 100 ms, and the top 3 configuration for data acquisition was set in DDA mode. Fragment spectra were collected at 17500 resolution to achieve an AGC target of 1e5 with a maximum inject time of 80 ms. To evaluate the stability of LC-MS throughout the entire collection process, a quality control sample (mixed sample) was acquired every 10 samples.

Processing and analysis of metabolome data

The acquired LC-MS data pretreatments, including peak picking, peak grouping, retention timecorrection, second peak grouping, and annotation of isotopes and adducts, was performed using XCMS software. LC−MS raw data files were converted into mzXML format, and then the metabolites were processed by the XCMS, CAMERA and metaX implemented with the R software (version 4.0.0). Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peaks were recorded, and a three dimensional matrix containing arbitrarily assigned peak indices (retention time-m/zpairs), sample names (observations) and ion intensity information (variables) was generated. The metabolites were annotated using the databases of KEGG and HMDB (Human Metabolome Database) by matching precise molecular weight data (m/z) of samples with data in the database. If the mass difference between the observed value and the database value was less than 10 ppm, the metabolite will be annotated and the molecular formula of the metabolite will be further identified and validated through isotope distribution measurements. Moreover, the identification of metabolites were validated by an internal metabolite fragment spectral library.

Multivariate statistical analysis was conducted to visualize metabolic differences between different data groups, including unsupervised principal component analysis (PCA) and supervised partial least square discriminant analysis (PLS-DA). Univariate analysis of fold-change and student’s t-test was performed, and metabolites with VIP (variable important for the projection)≥1, p-value<0.05 and fold change≥1.2 or ≤1/1.2 were considered as SDMs. Pathway enrichment analysis of SDMs was performed using the KEGG database (http://www.kegg.jp/).

Integrative analysis of metabolomics and transcriptomics

The common pathways of significant differentially enriched genes and metabolites were selected using the Venn diagram intersection method, and clarify the main biochemical pathways and signal transduction pathways involved by DEGs and SDMs.

Results

Visual observation of duck embryos

The body size of the two duck breeds gradually increases during the embryonic stage, and there are obvious differences in their skin, fins, beak, and feathers (Fig. 1). This indicates that there are differences in the molecular regulatory mechanisms of the skin between the two ducks during embryonic development.

Fig. 1.

Fig. 1

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Visual observation of skin and feathers development of duck during embryonic stage. (A-C) Visual observation of skin and feathers development of Yulin black duck at E15, E21 and E27, respectively. (D-F) Visual observation of skin and feathers development of Pekin duck at E15, E21 and E27, respectively.

The PCR results are consistent with the expected target band size. Lanes 1, 2, 7, 8 and 10 are single band, about 467 bp in size, which can be considered as male ducks, and lanes 3, 4, 5, 6, 9, 11 and 12 are two bands with 326 bp and 467 bp, which can be confirmed as female duck embryos (Fig. S1). Female ducks were selected for subsequent experiments because they were predominant in the farm, and can also avoid data errors caused by individual differences between males and females.

Overview of transcriptome data

After the transcriptional analysis of the 18 samples, a total of 100.18 Gb clean data was obtained by the quality control. The Q20 and Q30 percentages were greater than 99.69% and 96.95%, and the GC content reached between 47.50% to 48.50%.

On average, 87.68% clean reads (32,538,247 bp) were mapped to the duck reference genome using HISAT2, including 5.58% multiple mapped clean reads and 82.10% uniquely mapped clean reads (Table S2). Only used the uniquely mapped clean reads were used for further analysis.

Identification of differentially expressed genes

A total of 741 DEGs across the BSE15 vs. PSE15, BSE21 vs. PSE21, and BSE27 vs. PSE27 comparisons. Among these DEGs, 187 DEGs were detected in skin from BSE15 vs. PSE15 group, of which 118 were up-regulated genes and 69 were down-regulated genes, while among 417 DEGs in BSE21 vs. PSE21 group, the number of up-regulated genes were 91 and the down-regulated genes were 326. In addition, 137 DEGs from BSE27 vs. PSE27 were identified, including 55 up-regulated genes and 82 down-regulated genes (Fig. 2A and 2B). Principal component analysis (PCA) was conducted to reveal significant separation among samples from different experimental groups, reflecting their notable differences in gene expression levels (Fig. 2C), and the heatmap showed clear patterns of gene expression, further highlighting the similarities and differences among the experimental groups (Fig. 2D). Among the comparative groups, a total of 82 DEGs overlap in pairs, with 12 DEGs co-expressing across three developmental time points during embryonic stage between the 2 duck breeds (Fig. 2E). The top 20 up- and down-regulated genes in each comparison group were listed in Table S3.

Fig. 2.

Fig. 2

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Analysis of DEGs sequencing. (A) Volcano maps of DEGs. (B) Statistical map of DEGs. (C) PCA map of DEGs. (D) Cluster analysis of DEGs. (E) Venn map of DEGs.

The comprehensive transcriptome analysis to reveal genes associated with the regulation of skin development in this study, and some known genes involved in skin development were identified, including DCT, TYR, ASIP, KIT, etc. (Table S4). This finding suggests that these genes have a potential role in influencing duck skin growth, indicating important implications in the regulation of skin development.

GO enrichment and KEGG pathway analyse

The GO annotations were analysed to further reveal the molecular characterization of these DEGs, which were divided into 3 main GO categories: biological process (BP), molecular function (MF), and cell composition. In total, there were 80, 274, and 153 significantly enriched GO terms (P<0.05) identified in BSE15 vs. PSE15, BSE21 vs. PSE21, and BSE27 vs. PSE27, respectively. Moreover, the cell composition categories, including membrane, extracellular region, plasma membrane, and cytoplasm, were highly enriched. In the MF categoryies, protein binding, DNA binding, and molecular_function accounted for a significant proportion. The enriched terms of biological process were potentially related to skin growth and development, including positive regulation of melanin biosynthetic process, melanin biosynthetic process, cuticle development, melanin biosynthetic process from tyrosine, and melanocyte differentiation (Fig. 3A). A total of 110 KEGG signaling pathways were obtained across three comparison groups by KEGG enrichment analysis of DEGs (Table S5). In BSE15 vs. PSE15, the significantly enriched pathways related to growth and development of skin were identified, including melanogenesis, tyrosine metabolism, and vitamin B6 metabolism. In BSE21 vs. PSE21, the DEGs were significantly enriched in sphingolipid metabolism, tyrosine metabolism, protein digestion and absorption, melanogenesis, tight junction, alpha-linolenic acid metabolism, arachidonic acid metabolism, and linoleic acid metabolism pathways. Besides, the significant enrichment pathways were detected in BSE27 vs. PSE27, including nicotinate and nicotinamide metabolism, alanine, aspartate and glutamate metabolism, tyrosine metabolism, melanogenesis, vitamin B6 metabolism pathways (Fig. 3B).

Fig. 3.

Fig. 3

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GO and KEGG analysis of DEGs. (A) GO enrichment terms of DEGs. (B) Top 20 KEGG pathways of DEGs.

Validation of DEGs by qRT-PCR

A total of 12 DEGs, including ETNPPL, HPGDS, DCT, CACNG5, HPGDS, MC1R, ENTPD3, HDC, TLR3, NPFFR2, HSD17B2, and ASIP, were randomly selected to validate the results of RNA-Seq. Notably, qRT-PCR was performed using the same RNA samples as used for RNA-Seq. The trend of relative mRNA levels of the selected genes was consistent with the results of transcriptome analysis (Fig. 4).

Fig. 4.

Fig. 4

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The qRT-PCR Validation of DEGs. (A) BSE15 vs. PSE15; (B) BSE21 vs. PSE21; (C) BSE27 vs. PSE27. “*” was considered significant difference (P<0.05); “**” was considered extremely significant difference (P<0.01).

The overview of total metabolites

The extracted substances from each sample were analyzed by LC-MS of the untargeted metabolome, and the positive and negative ion modes were tested. Each QC sample is mixed with an equal amount of prepared samples and repeated throughout the experiment as an experimental technique. Pearson’s Correlation Coefficient was analyzed and plotted according to the abundance values of QC samples, and the repeatability of metabolite detection was obtained. The repeatability between QC samples in this study was greater than 0.97, indicating that the instrument was in a stable state during the whole sample detection and analysis process, and the data quality was reliable (Fig. 5A). The PCA results showed that the QC samples were tightly clustered in the middle of all samples, indicating that the data from this experiment has high quality. PLS-DA, a supervised differential discriminant analysis method used to screen potential SDM, showed that the samples were significantly divided into a BSE group and a PSE group (Fig. 5B). Then the permutation test of PLS-DA model was performed (200 test times). The value of R2 was close to 1 and Q2<0, indicating that the model did not have overfitting, and the SDMs can be screened and analyzed according to the model (Fig. 5C).

Fig. 5.

Fig. 5

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The Overview of Metabolites. (A) Pearson’s correlation coefficients among samples. The greater the correlation between samples, the larger the value and the redder the color. (B) PLS-DA score plots in duck. Each point represents a sample, and the degree of dispersion of the two colors represents the distribution trend of the two groups on the PC1 and PC2 axes. (C) Permutation test of the PLS-DA model. The number of random permutation tests is 200.

Analysis of differential metabolites

The SDMs were screened based on the criteria of fold change≥1.2 or ≤1/1.2, p-value<0.05 and VIP≥1. The accumulation patterns of SDMs were evaluated by hierarchical cluster analysis (Fig. 6A). Compared with the metabolites in the BSE15 group, 97 SDMs were significantly altered in the PSE15 group, of which 58 were up-regulated, and 39 were down-regulated. Among 104 SDMs in skin from BSE21 vs. PSE21 group, 43 were up-regulated metabolites and 61 were down-regulated metabolites. Of the SDMs identified between BSE27 and PSE27, 60 and 62 metabolites were up-regulated and down-regulated, respectively (Fig. 6B). The top 10 up- and down-regulated SDMs from three comparison groups were showed in Table S6. The number of metabolites among 3 comparison groups were showed in the Venn diagram, and 5 metabolites were screened as SDMs existing among all comparison groups (Fig. 6C). The significantly different metabolites L-tyrosine, N-arachidonyl maleimide, glycerophospho-N-palmitoyl ethanolamine, LPE 22:4, PC(0:0/18:0), belonging to the categories of organoheterocyclic compounds, lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, were mainly enriched in these pathways, suggesting that these SDMs may play important roles in skin development of duck during embryonic stage.

Fig. 6.

Fig. 6

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Analysis of SDMs sequencing. (A) Heatmap of SDMs. (B) Volcano of SDMs. (C) Venn of SDMs. (D) KEGG analysis of SDMs.

To further elucidate the biological functions of metabolites, the significance analysis of pathway enrichment was performed based on KEGG annotation. In BSE15 vs. PSE15, 33 SDMs were significantly annotated into 35 pathways, including glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, and melanogenesis. A total of 38 SDMs, identified in BSE21 vs. PSE21, were significantly enriched into 22 metabolic pathways, most of which were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, and arachidonic acid metabolism. Besides, 27 significant enrichment pathways were detected (a total of 44 SDMs) in BSE27 vs. PSE27, such as glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, and arachidonic acid metabolism (Fig. 6D, Table S7). The SDMs, including L-tyrosine, N-arachidonyl maleimide, glycerophospho-N-palmitoyl ethanolamine, LPE 22:4, PC(0:0/18:0), belonged to the categories of organoheterocyclic compounds, lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, and were mainly enriched in these pathways, suggesting that these metabolites may play important roles in skin development of duck during embryonic stage.

Integrated analysis of metabolomics and transcriptomics

In order to gain a deeper understanding of the diversity of skin colors, a comprehensive analysis by integrating transcriptomic and metabolomic data was performed. By combining two sets of omics data through the KEGG pathway, genes and metabolites that undergo significant changes in the same biological process can be screened. A total of 17 co-enriched KEGG pathways were found in 3 comparison groups, of which 2 pathways in BSE15 vs. PSE15, 14 pathways in BSE21 vs. PSE21, and 8 pathways in BSE27 vs. PSE27, respectively (Fig. 7A, Table S8). It is worth noting that these pathways mainly involved in lipid metabolism, including glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism, which regulated the development of duck skin. The genes ETNPPL, HPGDS, LOC101794396, LOC101794764, LOC101792932, and LOC113844055, and the metabolites L-tyrosine, N-arachidonyl maleimide, glycerophospho-N-palmitoyl ethanolamine, play an important regulatory role in these metabolism pathways (Fig. 7B). These results may indicate that the pathways of lipid metabolism have an important impact on the skin development of different duck breeds during embryonic period.

Fig. 7.

Fig. 7

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Correlation analysis of SDMs and DEGs related to skin development. (A) Correlation analysis for KEGG of SDMs and DEGs. (B) Interaction networks of SDMs and DEGs. The red squares represent DEGs, and the purple circles represent SDMs.

Discussion

Yulin black duck, with a unique appearance and taste characteristics, has been a delicacy on the dining table and a traditional tonic for rehabilitating patients for many years in China. The structure of dermis and epidermis of Pekin duck skin is very important to its biological function (Li et al. 2025), and Pekin duck is an ideal object for skin development research. In this study, the transcriptomic and metabolomic changes of skin development between Yulin black duck and Pekin duck during embryonic stage were tracked, which can provide a unique opportunity for us to gain insight into the candidate genes and metabolites in the process of avian skin development.

A total of 741 DEGs were identified from the transcriptional profiles of skin across three developmental time points during embryonic stage between Yulin black duck and Pekin duck, which were mainly associated with amino acid metabolism, lipid metabolism, and endocrine system. The GO analysis in the BP category indicated enrichment of many significant items, such as positive regulation of melanin biosynthetic process, melanin biosynthetic process, cuticle development, melanin biosynthetic process from tyrosine, and melanocyte differentiation. In the pairwise KEGG analysis, the main enriched pathways including melanogenesis, tyrosine metabolism, vitamin B6 metabolism, sphingolipid metabolism, protein digestion and absorption, tight junction, alpha-linolenic acid metabolism, arachidonic acid metabolism, linoleic acid metabolism, nicotinate and nicotinamide metabolism, and alanine, aspartate and glutamate metabolism in the 3 comparison groups were found. Additionally, 14 genes (DCT, TYR, ASIP, TYRP1, KIT, PHOSPHO2, CERS3, SGPP2, SPTLC3, DEGS2, PATJ, RBP7, AOX1, ETNPPL, HPGDS, and GAD1) were also identified, which were likely to be involved in duck skin development and were either up- or down-regulated with more than several-fold changes.

Metabolites can be used as biomarkers for the skin differences and provide a direction for further research into skin development mechanisms and biological breeding. In this study, the differences in the skin of Yulin black duck and Pekin duck during embryonic period were detected using metabolome sequencing technology. Metabolites in the 3 comparison groups of samples varied, as was to be expected. The main metabolic pathways involved in duck skin development were glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, alpha linoleic acid metabolism, and melanogenesis. Some SDMs, such as L-tyrosine, N-arachidonyl maleimide, glycerophospho-N-palmitoyl ethanolamine, were found to be involved in the regulation of duck skin development.

Melanogenesis is a complex process with melanin production and distribution (Videira et al., 2013). Melanin is the primary determinant of skin, hair, and eye color, and insufficient or excessive pigmentation may occur when the number of melanocytes changes (Lin and Fisher, 2007). Melanogenesis in animal melanocytes is controlled by paracrine factors produced by the surrounding tissue environment and their own genetic factors, namely numerous melanogenesis-related genes (coat color genes) (Yardman-Frank and Fisher, 2021). Similarly, tyrosine metabolism is involved in the synthesis of melanosomes and the deposition of melanin (Slominski et al., 2004). In this study, the expression of feather color-related genes, L-dopachrome tautomerase (DCT), agouti signaling protein (ASIP), KIT proto-oncogene, receptor tyrosine kinase (KIT) and tyrosinase protein families (TYR, TYRP1) were significantly up-regulated or down-regulated during the duck embryonic period, suggesting that these genes play an important role in regulating duck skin development, especially in feather color.

The barrier function of the skin is primarily provided by the corneocytes, which are connected by corneodesmosomes and lamellar lipids in the stratum corneum. Tight, gap, and adherens junctions also help protect the skin barrier (Draelos, 2012; Jung et al., 2019). Among them, tight junction (formed by complexes of adhesive and scaffolding proteins) is an important component of cell junctions, which forms a barrier for ions and molecules to pass through the cells and regulates the movement of proteins and lipids between the apical and basolateral domains of the plasma membrane (Niessen, 2007; Hartsock and Nelson, 2008). Tight junctions are involved in the regulation of cell proliferation, differentiation, and migration, as well as actin assembly (González-Mariscal et al., 2007). In the skin, a strong epidermal barrier is maintained through the joint action of tight junctions and the stratum corneum (Sugawara, et al. 2013). Deficiency of tight junctions causes a loss of water and solutes in the skin and allows allergens to enter, resulting in dry skin and an overreaction to allergens (Zhao, et al. 2021). This indicates the importance of tight junction in the skin development of different duck breeds. PALS1-associated tight junction protein (PATJ) is not only membrane-associated but is also localized primarily to tight junctions (Lemmers et al., 2002). PATJ contains ten PDZ domains through which it mediates interactions with other proteins, such as components of the TJs zonula occludens 1 (ZO-1) and claudin-11, or the proteins of angiomotin (AMOT) and KIBRA (Roh et al., 2002; Duning et al., 2008; Ernkvist et al., 2009). Meanwhile, PATJ inhibits histone deacetylase 7 to control tight junction formation and cell polarity (Fiedler et al., 2023). It also participates in regulating the directed migration, tight junction formation and polarity of mammalian epithelial cells (Shin et al., 2005; Shin et al., 2007).

Lipids, the major components of the cell membrane, which are involved in the regulation of cell proliferation, apoptosis, immunity, angiogenesis, inflammation, and other biological processes (Zeng et al., 2017). Sphingolipids are essential signalling molecules for the proper biological and structural functioning of cells, as well as an important constituent of the permeability barrier of the stratum corneum, and play an essential role in maintaining normal skin function (Feingold, 2007; Moskot et al., 2018). An increasing amount of evidence indicateds that sphingolipids have been regarded as regulators of various protein kinases and phosphatases, receptors, and ion transporters, as well as key regulators of a wide range of cellular processes, including cell growth, adhesion, migration, aging, apoptosis, and autophagy (Hannun and Obeid, 2008). Sphingolipid metabolism is considered crucial for fundamental cellular events such as membrane homeostasis, endocytosis, cell movement, nutrient transport, and protein synthesis under normal cellular homeostasis (Hannun and Obeid, 2008; Breslow and Weissman, 2010), and help maintain the homeostasis of the skin barrier (Bocheńska and Gabig-Cimińska, 2020). Changes in these genes might suggest the underlying connection between the duck skin development and lipid metabolism. The differentially expressed genes related to sphingolipid metabolism including ceramide synthase 3 (CERS), sphingosine-1-phosphate phosphatase 2 (SGPP2), serine palmitoyltransferase 3 (SPTLC3) and delta 4-desaturase, sphingolipid 2 (DEGS2) up- or down regulates the development of duck skin.

Nicotinamide is the amide form of water-soluble vitamin B3 (nicotinate) and a precursor of nicotinamide-adenine dinucleotide (NAD+), which regulates several redox and non-redox reactions that regulate cellular energy metabolism (Nikas et al., 2020). Nicotinamide has the same vitamin activity as nicotinic acid, but other pharmacological actions and side effects are different. The presence of Nicotinamide is crucial for cellular energy metabolism, and severe lack of Nicotinamide is associated with pellagra (Maiese et al., 2009). Studies have shown that the mixture of nicotinamide, vitamin C and PDRN can reduce melanogenesis by decreasing the MC1R/MITF, tyrosinase, TYRP1, and TYRP2 signaling pathways (Park et al., 2022). Retinoid-binding protein 7 (RBP7) positively regulates the process of preadipocyte differentiation and promotes the deposition of Intramuscular fat (IMF) in the pectoralis major muscle of fast-growing chickens compared to slower growing chickens (Liu et al., 2017). Aldehyde oxidase 1 (AOX1) may be related to the development of vertebrates and may control the homeostasis of certain types of tissues in adults (Kurosaki et al., 2004).

Vitamin B6 (VB6), also known as pyridoxine, is a coenzyme of enzymes involved in amino acid metabolism and nutrition, which helps maintain body homeostasis (Rosenberg, 2012). In the skin, depletion of VB6 can lead to diseases such as atopic dermatitis and seborrheic dermatitis (Seguchi et al., 1996). Studies have shown that VB6 has the potential to be an anti-skin pigmentation substance (Katsuyama et al., 2022). The function of PHOSPHO2 (Phosphatase, Orphan 2) has been attributed to the vitamin B6 pathway and plays an important role in it (Rolland et al., 2014). Amino acids are the building blocks of proteins and also serve many important metabolic functions. In this study, the pathways of protein digestion and absorption and amino acid metabolism were significantly enriched, speculating that they regulate the development of duck skin by adjusting protein (also including amino acid) levels, which is also one of the reasons for the differences in skin development between the two duck breeds. Glutamate, the most abundant amino acid in the human body, plays a crucial role in multiple physiological processes including cognition, learning, and behavior. Glutamate decarboxylase (GAD) is responsible for converting glutamate into the neurotransmitter C-aminobutyric acid (GABA), which is one of the main inhibitory neurotransmitters of glutamate and plays a key signaling role in the nervous system and many non-neuronal cell types. In humans, GAD1 encodes a 67 kDa protein called GAD67, which plays a critical role in synaptogenesis and neuronal development (Sgadò et al., 2011). Bi-allelic GAD1 variant can cause early-infancy onset epilepsy and developmental delay (Neuray et al., 2020).

Glycerophospholipids are the major structural lipid components of eukaryote cellular membranes and participate in a variety of indispensable metabolic, neurological, and intracellular signaling processes, such as development, necrosis and apoptosis, trans port, DNA replication, neuronal signaling, or secretion (Guo et al., 2005; Castro-Gómez et al., 2015). Linoleic acid is the main omega-6 polyunsaturated fatty acid (PUFA) found in the epidermis and plays a vital role in maintaining the integrity of the skin's water permeability barrier (Hansen and Jensen, 1985; Imokawa et al., 1994). Linoleic acid is known to have a whitening effect on hyperpigmented skin (Shigeta et al., 2004). Lack of diet in linoleic acid can lead to skin diseases, such as atopic dermatitis in hairless mice (Fujii et al., 2015). Alpha-linolenic acid is an omega-3 essential fatty acid, which has the function of anti-radiation, anti-oxidation, anti pigmentation, protection of skin lubrication, and maintenance of skin elasticity. supplementing with alpha-linolenic acid in the culture medium can reduce the proliferation and restore their cell differentiation of psoriasis keratinocytes in a three-dimensional in vitro human psoriasis skin model (Simard et al., 2021), and alpha-linolenic acid exerts its beneficial effects by reducing the amount of inflammatory mediators released by T cells (Morin et al., 2022). Arachidonic acid, a polyunsaturated fatty acid, mainly esterified to phospholipids at the second carbon of the cell membrane phospholipid glycerol skeleton. When keratinocytes respond to skin irritation and injury, arachidonic acid is released from phospholipids under the action of phospholipase, which then activates arachidonic acid metabolism (Li et al., 2023). Studies have shown that arachidonic acid induces human MSC migration and promotes skin wound healing through MT3-MMP mediated degradation of fibronectin (Oh et al., 2015). Therefore, the function of the glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism may be essential in the development of duck skin during the embryonic stages.

In these metabolism pathways, the genes HPGDS, ETNPPL, LOC101794396, LOC101794764, LOC101792932, and LOC113844055 play an important regulatory role. Hematopoietic prostaglandin D synthase (HPGDS) evolved from the same ancestral gene as the members of the sigma-class glutathione S-transferase (GST) family (Kanaoka et al., 2000), and mainly located in mast cells and antigen-presenting cells, which converts prostaglandin H2 into anti inflammatory factors prostaglandin D2 (Bridges et al., 2012; Ouyang et al., 2022). Therefore, HPGDS may act as an anti-inflammatory barrier during the development of duck skin in this study. Ethanolamine-phosphate phospho-lyase (ETNPPL), also known as AGXT2L1, is a lipid metabolizing enzyme involved in the metabolism of phosphoethanolamine in the cell membrane (Schiroli et al., 2013; Leventoux et al., 2020). Moreover, ETNPPL may play an important function in the maintenance of cell membranes, metabolism of phosphoethanolamine (PEtN, a small amino acid that is involved in the metabolic pathways of several types of lipids), and control of its concentration within the CNS (Veiga-da-Cunha et al., 2012). In this study, the function of ETNPPL may be essential in the development of duck skin. In addition, LOC101794396, LOC101794764, LOC101792932, and LOC113844055 were also found to be involved in the regulation of differential metabolites such as PC (18:2/0:0), LysoPE (0:0/16:0), 1-heptadecanoyl-sn-glycero-3-phosphocholine, thereby regulating the development of duck skin. All these studies have identified that the DEGs and SDMs have important functions in skin development. Therefore, these genes, metabolites and related pathways that were in the skins of Yulin black ducks and Pekin ducks, may have an important regulatory effect on the mechanism of skin development.

In summary, this study demonstrated results of transcriptomic and metabolomic analysis of skin development in Yulin black ducks compared with Pekin ducks. The results provide novel insights into the differential gene expression associated with duck skin development and identify key genes and metabolites related to the glycerophospholipid metabolism, arachidonic acid metabolism, linoleic acid metabolism, and alpha-linolenic acid metabolism. These findings will help to better understand the molecular mechanisms of skin pigmentation in poultry and provide a valuable theoretical basis for animals to select natural color features.

Data availability statement

All data generated or analysed during this study are included in this article and its additional files. All RNA-seq data analysed during this study has been deposited in NCBI database (https://submit.ncbi.nlm.nih.gov/subs/sra/) under accession number PRJNA1269848 (PSE15), PRJNA1269761 (PSE21), PRJNA1270158 (PSE27), PRJNA1270175 (BSE15), PRJNA1270381 (BSE21), and PRJNA1270416 (BSE27).

Declaration of competing interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This work was supported by China Agriculture Research System of MOF and MARA (CARS-42-2), the Project of Technology Innovation Guidance Program in Shaanxi Province (No.2021QFY10-05, 2022QFY12-01, 2022QFY12-04), the National Key Research and Development Program of China (2023YFD1300303).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2025.105403.

Appendix. Supplementary materials

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mmc2.zip (125.3KB, zip)

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Associated Data

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

Supplementary Materials

mmc1.jpg (411.1KB, jpg)
mmc2.zip (125.3KB, zip)

Data Availability Statement

All data generated or analysed during this study are included in this article and its additional files. All RNA-seq data analysed during this study has been deposited in NCBI database (https://submit.ncbi.nlm.nih.gov/subs/sra/) under accession number PRJNA1269848 (PSE15), PRJNA1269761 (PSE21), PRJNA1270158 (PSE27), PRJNA1270175 (BSE15), PRJNA1270381 (BSE21), and PRJNA1270416 (BSE27).

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