
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00838-1
10.1016/j.psj.2024.104259
104259
GENETICS AND MOLECULAR BIOLOGY
Single-nucleus RNA sequencing reveals cell types, genes, and regulatory factors influencing melanogenesis in the breast muscle of Xuefeng black-bone chicken
Li Peng *†1
Wei Xu *†1
Zi Qiongtao *†1
Qu Xiangyong *†
He Changqing *†
Xiao Bing ‡
Guo Songchang guo_ast@126.com
*†2
⁎ College of Animal Science and Technology, Hunan Agricultural University, Hunan 410128, China
† Hunan Engineering Research Center of Poultry Production Safety, Hunan Agricultural University, Hunan 410128, China
‡ Hunan Yunfeifeng Agricultural Co. Ltd, Hunan, 418200, China
2 Corresponding author: guo_ast@126.com
1 Peng Li and Xu Wei contributed equally to this work.

27 8 2024
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27 8 2024
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© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The black-bone chicken, known for its high melanin content, holds significant economic value due to this unique trait. Particularly notable is the prominent melanin deposition observed in its breast muscle. However, the molecular mechanisms governing melanin synthesis and deposition in the breast muscle of black-bone chickens remain largely unknown. This study employed a single-nucleus transcriptome assay to identify genes associated with melanin deposition in the breast muscle of black-bone chickens, which are presumed to influence pigmentation levels. A comprehensive analysis of the nuclear transcriptome was conducted on the breast muscle of Xuefeng black-bone chickens, encompassing 18 distinct cell types, including melanocytes. Our findings revealed that STIMATE, LRRC7, ENSGALG00000049990, and GLDC play pivotal regulatory roles in melanin deposition within the breast muscle. Further exploration into the molecular mechanisms unveiled transcription factors and protein interactions suggesting that RARB, KLF15, and PRDM4 may be crucial regulators of melanin accumulation in the breast muscle. Additionally, HPGDS, GSTO1, and CYP1B1 may modulate melanin production and deposition in the breast muscle by influencing melanocyte metabolism. Our findings also suggest that melanocyte function in the breast muscle may be intertwined with intercellular signaling pathways such as PTPRK-WNT5A, NOTCH1-JAG1, IGF1R-IGF1, IDE-GCG, and ROR2-WNT5A. Leveraging advanced snRNA-seq technology, we generated a comprehensive single-cell nuclear transcriptome atlas of the breast muscle of Xuefeng black-bone chickens. This facilitated the identification of candidate genes, regulatory factors, and cellular signals potentially influencing melanin deposition and melanocyte function. Overall, our study provides crucial insights into the molecular basis of melanin deposition in chicken breast muscle, laying the groundwork for future breeding programs aimed at enhancing black-bone chicken cultivation.

Key words

Xuefeng black-bone chicken
breast muscle
melanin deposition
single-nucleus RNA sequencing
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pmcINTRODUCTION

The black-bone chicken, an uncommon fowl breed, holds considerable economic significance within the genetic repository of Chinese livestock and poultry. It is recognized for its medicinal properties, including the enhancement of immune function (Yu et al., 2021) and the treatment of various diseases (Lin and Chen, 2005; Jian et al., 2021; Liu et al., 2013). The high muscular melanin content, which imparts a distinctive black coloration, is a preferred trait among consumers due to its association with the quantity of melanin deposited. Therefore, it is imperative to explore the complexities of melanin deposition in the muscle tissues of black-bone chickens to understand the genetic and biochemical mechanisms that underlie this characteristic.

Presently, research on melanin accumulation in the muscle tissue of black-bone chickens primarily focuses on investigating the differential expression of mRNAs in the muscle and analyzing metabolites. Through transcriptome sequencing, crucial genes associated with muscle melanin have been identified, including ankyrin repeat domain 1 ANKRD1, myozenin 2 (MYOZ2), myogenic differentiation 1 (MYOD1), tyrosinase (TYR), dopachrome tautomerase (DCT), solute carrier family 24 member 5 (SLC24A5), G protein-coupled receptor 143 (GPR143), and glycoprotein nmb (GPNMB), (Xu et al., 2023; Huang et al., 2024). A ceRNA network, encompassing 13 DElncRNAs, 4 DEcircRNAs, 17 DEmiRNAs, and 5 DEmRNAs, associated with melanin deposition in the muscles of black-bone chickens, has been meticulously constructed through a comparative transcriptome analysis between the black and white muscle tissues (Li et al., 2024). An examination of gene expression profiles and metabolite levels in the breast muscles of black-bone chickens and their white-fleshed counterparts has disclosed a pronounced upregulation of the tyrosinase-related proteins1 (TYRP1), DCT, Proteasomal protein (PMEL), Melan-A (MLANA), and PDZK1 genes in the black muscle tissue (Dou et al., 2022).

The investigation of melanin deposition mechanisms in the breast muscle of the black-bone chicken represents a sophisticated and intricate area of research. Although the aforementioned studies have contributed valuable data and established a research foundation, the reliability of their findings may be compromised by uncertainties in sample processing and metabolomic analysis, as noted by Ortmayr et al. (2016). Traditional bulk RNA sequencing, while providing population-level gene expression profiles, has limitations in capturing the cellular heterogeneity and plasticity inherent in muscle tissue, as highlighted by Stuart and Satija (2019). To address these limitations, single-nucleus RNA sequencing (snRNA-seq) has been introduced as a more refined method, enabling a detailed understanding of the transcriptional landscape at the individual cell level within muscle tissue. The applications of snRNA-seq have been extensive, encompassing studies of neurogenic cell trajectories in humans and rhesus monkeys, the elucidation of molecular mechanisms behind specific tissue formations, identification of lineage and types of bovine placental trophoblasts, and the characterization of cholinergic neuron types (Alkaslasi et al. 2021; Franjic et al. 2022; Wang et al. 2023; Davenport et al. 2023). However, the utilization of snRNA-seq in the context of melanin deposition in avian muscle remains largely uncharted territory. Building upon this foundation, researchers have conducted snRNA-seq and integrated it with WGCNA to uncover potential genes and regulatory factors implicated in melanin deposition and melanocyte function in the breast muscles of the Xuefeng black-bone chicken, a breed indigenous to the Hunan Province Xuefeng Mountains. This integrative study is poised to broaden our comprehension of avian muscle cell diversity and shed light on the genetic mechanisms underlying melanin deposition in the breast muscle of the black-bone chicken, thereby facilitating informed selection and breeding strategies for this species.

Building upon this foundation, we have conducted snRNA-seq and integrated it with WGCNA to uncover potential genes and regulatory factors implicated in melanin deposition and melanocyte function in the breast muscles of the Xuefeng black-bone chicken, a breed indigenous to the Hunan Province's Xuefeng Mountains. This research aims to significantly enhance our comprehension of the diversity within avian muscle cell populations and to unravel the genetic mechanisms underlying melanin deposition in the breast muscle of the black-bone chicken.

MATERIALS AND METHODS

Laboratory Animal

Hunan Agricultural University Animal Ethics Committee (Changsha, China) reviewed and approved all experimental protocols (approval number: 2019022). The Xuefeng black-bone chickens, aged 24 d, utilized in this research were sourced from Hunan Yunfeifeng Agricultural Co. Ltd in Hongjiang District, Huaihua City, Hunan Province.

Sample Collection and Sequencing

In our study, we employed a colorimeter (KONICA MINOLTA) to meticulously assess the melanin content in the breast muscle tissue of 100 Xuefeng black-bone hens at 24 d of age. Given the robust negative correlation between the L-value—a measure of lightness—and the degree of muscle blackness (Shi et al., 2022), we elected to utilize L-value as the principal metric for classification purposes. We constructed a box and whisker plot from the L-values obtained, which visually represents the spectrum of melanin pigmentation (Figure 1). The interquartile range was employed to delineate the thresholds for high and low melanin content. Accordingly, we curated eight samples each of breast muscle tissue exhibiting high and low melanin levels from the female hens. These samples were then pooled in equal ratios of 2-3:1 to ensure homogeneity, resulting in 3 composite samples rich in melanin content, designated as High Blackness (HB). Concurrently, we prepared 3 new samples with Low Blackness (LB), composed of tissues with minimal melanin pigmentation. The statistical analysis of L-values among the groups with high and low melanin content is detailed in Table S1, providing a quantitative comparison of the melanin deposition across the sampled breast muscle tissues. By employing this rigorous and systematic approach, we aimed to ensure the reliability and reproducibility of our findings, thereby enhancing the validity of our subsequent analyses and conclusions regarding melanin deposition in the breast muscle of Xuefeng black-bone hens.Figure 1 Sample L-value box plot.

Figure 1

Single-Nucleus Suspension Preparation

The Dounce homogenizer was employed to create nuclear suspensions by combining lysate with the tissue sample. Afterward, the suspension was subjected to washing, filtration, and staining with Trypan Blue. The total number of nucleuses, their concentration, and the proportion with intact nuclear membranes were assessed using a microscope cell counting plate, which was used as a criterion for acceptable suspensions. In addition, fluorescence-assisted nucleus sorting (FANS) was used to remove cellular debris, and then an extra filtration step was performed to produce nucleus of better quality. Following the addition of DAPI (4′,6-diamidino-2-phenylindole) to the cell suspension, the nucleus was separated by flow sorting. Subsequently, the sample was diluted to a concentration of 1000 nucleus/μL, which was calculated based on the DAPI signal and nucleus size. The diluted sample was then utilized for library creation on the 10× Genomics platforms.

10× snRNA-seq Library Preparation

The snRNA-seq libraries were generated using the 10 × Genomics methodology. The process involved isolating nucleus from individual cells using microspheres and then performing reverse transcription operations. The microspheres were then stirred to amplify the reverse transcription products using PCR. The products were enzymatically fragmented and then subjected to addition and ligation of sequencing adapters, which included end repair, A addition, and junction sequence addition. Sequencing was performed with the 10× Genomics Chromium platform using the Illumina Novaseq 6,000 sequencer.

Single-Nucleus RNA Sequencing Analysis

The snRNA-seq data underwent a meticulous analysis process. The process of ensuring the accuracy and reliability of the data was carried out using Cell Ranger 5.0.1, which is the authorized analysis software provided by 10X Genomics. The FASTQ file obtained from the quality control procedure was subsequently matched with the chicken genome sequence (Gallus (red jungle fowl) (GRCg6a), and the reads were annotated with specific genes. Afterwards, the Unique Molecular Identifier (UMI) data were analyzed and tallied to generate the feature barcode matrix, which was subsequently improved to differentiate between cytosolic and non-cytosolic nucleus. The utilization of the UMI data and the categorization of nucleus allowed for precise quantification of genes, resulting in the generation of cytosolic expression matrices for later investigation. The matrices were subsequently imported into Seurat for a thorough examination of individual cell nucleus, encompassing quality control, cell grouping, cell subpopulation annotation, differential gene expression analysis, and visualization. Subsequently, the target gene sets underwent gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Analysis (KEGG) analyses to assess the disparities among comparable cell subpopulations.

Cellular Subpopulation Annotation

We annotated cellular subpopulations within the breast muscle tissues of Xuefeng black-bone chickens by leveraging the elevated gene expression profiles and the identification of marker genes, which were extrapolated from studies conducted on humans, mice, and other species (refer to Table S6 for details).

Weighted Gene Coexpression Network Analysis

We employed the R package to conduct a gene co-expression network analysis using weighting. Firstly, we conducted data preprocessing to remove any unwanted interference and variations in batches. Afterwards, we created gene co-expression networks by doing pairwise comparisons of genes. During the third stage, we identified gene modules that displayed robust associations. Correlation coefficients were calculated to determine the relationship between these modules and cellular subpopulations. Facilitating the connection between the annotation specifics of cellular subpopulations and gene modules. During the fourth phase, we selected module genes by evaluating their TOM value. Afterwards, we utilized Cytoscape v3.8.2 to examine the interconnection of genes inside each module. The identification of core genes was based on their degree value, and a co-expression network was constructed using Cytoscape software. During the fifth phase, we performed a functional enrichment analysis to determine the biological processes and pathways linked to each module. Lastly, in the sixth stage, we made predictions about module transcription factors and binding locations, and analyzed protein interactions.

RESULTS

Single-Nucleus RNA Sequencing Map of the Xuefeng Black-Bone Chicken Breast Muscle

We performed snRNA-seq on breast muscle samples from the Xuefeng black-bone chicken, building on the fundamental work of We et al. (2023), as shown in Figure 2A. Our research includes the entire dataset of 49,178 isolated breast muscle cells. After quality control procedures, the single-cell nuclear transcriptome profiles of muscle cells from the high bone density (HB) group, which contained 22,279 cells, and the low bone density (LB) group, which included 26,899 cells, were investigated. After this thorough analysis, 24 distinct cellular clusters were identified (Figures 2B, 2C, and 2D and are reported in Table S2). In our analysis, we meticulously classified cellular clusters into specific cell types by discerning the genes that were selectively expressed within each cluster (Figure 3). This characterization was accomplished by examining the signature expression patterns of marker genes associated with these clusters (as depicted in Figures 4C, 4D, and detailed in Table S6). Our comprehensive assessment detected a total of eighteen unique cell clusters within the melanocytes of the breast muscle in Xuefeng black-bone chickens, as illustrated in Figure 4, Figure 4B. The myogenic cells, characterized by the expression of MYF5, MYOD1, and MYOG (cited in Li et al., 2020; Hang et al., 2021), were categorized into clusters C1, C5, C6, C10, and C16. Clusters C1 and C16 were identified as satellite cells endowed with stem cell properties, marked by the distinctive expression of PAX7 (Jankowski et al., 2020). Cluster C11 was distinctively recognized as an ACAT1+ cell, defined by its exclusive expression of ACAT1, a hallmark of mature myoblasts (Li et al., 2020). Cluster C21 was identified as melanocytes, marked by the presence of DCT, TYR, and TYRP1 (Zhou et al., 2021). Further delineation identified additional cell types: clusters C0, C13, and C19 as myoblast subpopulations marked by MYOM1 and MYOM2 (Hang et al., 2021); cluster C23 as a MYH15+ myoblast subpopulation marked by MYH15 (Praud et al., 2020); cluster C8 as mesenchymal stem cells marked by CD44 (Svoradova et al. 2021); clusters C12 and C15 as ADIPOQ+ and APOA1+ adipocyte subpopulations, respectively, marked by ADIPOQ and APOA1 (Li et al., 2020); cluster C3 as a fibroblast subpopulation marked by COL1A1 (Deng et al., 2021); and clusters C2 and C18 as fibro-adipose generating progenitor cell subpopulations marked by PDGFRA (Muhl et al., 2020). Vascular and immune cell types were also characterized: cluster C7 as an endothelial cell subpopulation marked by APLN, FABP5, and TM4SF18 (Mantri et al., 2021); cluster C20 as another endothelial cell subpopulation marked by NPR3, CDH5, and PECAM (Mantri et al., 2021); clusters C14 and C4 as LCP1+ and CSF1R+ macrophage subpopulations, respectively, marked by LCP1 and CSF1R (Mantri et al., 2021); cluster C17 as the dendritic cell subpopulation marked by IFI6 (Mantri et al., 2021); and cluster C9 as the erythroid subpopulation marked by IHBBA and HBAD (Zhang et al., 2021). Additionally, cluster C22 was identified as a subpopulation of Schwann cells marked by PLP1 and MPZ (De Micheli et al., 2020). Our successful characterization of the cell types in the breast muscle tissue of Xuefeng black-bone chickens at single-cell resolution has yielded a wealth of potential biological markers.Figure 2 Cellular composition analysis of Xuefeng black-bone chicken breast muscle via single-nucleus RNA sequencing (snRNA-seq). (A) Schematic representation of the experimental workflow. Cellular suspensions were extracted from breast muscle samples categorized into high and low pigmentation groups, followed by snRNA-seq and subsequent analytical procedures. (B) Summary of the pre-filtered nuclei, detailing the initial stage of sample preparation prior to further refinement. (C) Characterization of the filtered cell nuclei, with annotations indicating the presence of doublets (red dots) and singlets (black dots), which are crucial for ensuring the accuracy of the sequencing data. (D) UMAP (Uniform Manifold Approximation and Projection) clustering outcome, illustrating the distribution patterns of the sampled groups, indicative of the cellular heterogeneity within the breast muscle tissue. (E) Detailed depiction of the cell clustering within the UMAP analysis.

Figure 2

Figure 3 UMAP plots of marker genes expression value in per cluster.

Figure 3

Figure 4 Comprehensive characterization of cell subsets in the breast muscle of Xuefeng black-bone chickens via snRNA-Seq (A) Cellular annotation delineates the distinct identities of various cell types within the breast muscle, providing a detailed classification based on the transcriptional profiles captured by snRNA-Seq. (B) The proportional distribution of cell subpopulations across different groups is illustrated, highlighting the relative abundance of each cell type and offering insights into the cellular composition of the breast muscle tissue. (C) Expression patterns of marker genes characteristic to each cell subpopulation are presented, enabling the assessment of cell-specific gene activity and the functional roles these cells may play within the muscle microenvironment. (D) A correlation heat diagram is depicted, mapping the relationships between different cell subpopulations.

Figure 4

Functional Enrichment of Up-Regulated Genes in Breast Muscle Melanocytes

In our study, we delved into the functional roles of genes that demonstrated elevated expression levels within specific melanocyte subpopulations. Utilizing the KEGG and GO databases, we performed a systematic analysis to classify these differentially expressed genes into their respective GO categories.

The cellular component (CC) classification revealed that these genes are associated with components such as "periplasmic," "plasma membrane," "cell front," "melanosomes," and "pigment granules," providing insights into their spatial distribution and function within the cellular architecture. The molecular function (MF) classification identified genes implicated in key interactions, including "enzyme binding," "protein binding," and "kinase binding," which are essential for mediating enzymatic activities and molecular signaling processes. Within the biological process (BP) category, the genes were linked to processes such as "movement of cellular or subcellular components," "regulation of cellular communication," "regulation of signaling," "movement," and "cell migration" (Figure 5A–C). These findings suggest that the up-regulated genes are involved in the dynamic processes of melanosome transport and melanocyte migration, which are critical for melanocyte function and pigmentation patterns.Figure 5 Functional Enrichment Analysis of Melanocyte Subpopulations in the Breast Muscle. (A) Cellular Component Enrichment: The GO enrichment analysis of upregulated genes specific to melanocyte subpopulations reveals significant associations with various cellular components. (B) Molecular Function Enrichment: This panel presents the molecular functions enriched among the upregulated genes of melanocyte subpopulations. (C) Biological Process Enrichment: The biological processes enriched for upregulated genes in melanocyte subpopulations are depicted here. (D) KEGG Pathway Enrichment: The KEGG enrichment analysis of upregulated genes within melanocyte subpopulations identifies the metabolic and signaling pathways that are significantly represented.

Figure 5

Additionally, the KEGG pathway enrichment analysis uncovered 64 active pathways that are significantly enriched among the up-regulated genes. These pathways exhibited a high correlation with melanin biosynthesis and were predominantly associated with "adhesion junctions," "lysosomes," and the "melanogenesis" pathway itself (Figure 5D). Overall, our study provides a nuanced understanding of the molecular mechanisms underlying melanocyte subpopulations and offer a foundation for further exploration into the genetic determinants of pigmentation and melanocyte behavior.

Analysis of the Diffential Genes in Melanocytes Between the HB Group and the LB Group

In our study, we conducted a comparative analysis to examine the molecular variance across distinct melanocyte subpopulations, employing the LB group as a reference point. The comprehensive dataset of 179 differentially expressed genes is depicted in Figure 6A, with 98 genes exhibiting up-regulation and 81 genes showing down-regulation.Figure 6 Dissection of differential gene expression in melanocyte subpopulations. (A) Gene expression landscape: (a) Volcano Plot: This panel presents a volcano plot illustrating the variance in gene expression across groups, with significant differentially expressed genes pinpointed for their statistical relevance and magnitude of change. (b) gene expression statistics: A tabular summary enumerates the differentially expressed genes, detailing the quantitative aspects of their deregulation, including fold-changes and statistical measures. (B) Cellular Component Enrichment: The GO enrichment analysis dissects the cellular components associated with the differentially expressed genes (DEGs) (C) Molecular Function Enrichment: This section of the GO enrichment analysis focuses on the molecular functions encapsulated by the DEGs, revealing the enzymatic activities, binding affinities, and structural roles that are overrepresented among the genes exhibiting differential expression. (D) Biological Process Enrichment: The biological processes enriched for DEGs are delineated, highlighting the pathways and physiological mechanisms influenced by the differential expression of genes within melanocyte subpopulations. (E) KEGG Pathway Analysis: The KEGG enrichment analysis is depicted, mapping the DEGs onto known metabolic and signaling pathways to infer their systemic roles in melanocyte biology (F) Venn Diagram of Gene Regulation: A Venn diagram intersects the up-regulated genes with those showing intergroup differential expression, providing a visual synthesis of the shared and distinct genetic responses across melanocyte subpopulations. (G) Melanin Deposition Gene Expression: Integration analysis of up-regulated genes and intergroup differential genes in melanocyte subpopulation.

Figure 6

We subsequently subjected these differentially expressed genes (DEGs) to a rigorous analysis using the GO and KEGG database analytical frameworks. Throughout this process, we identified 474 statistically significant GO terms. A substantial number of these terms indicated an enrichment of genes associated with functional categories such as rhabdomyoconstriction, phosphatidylinositol N-acylglucosaminyltransferase activity, and cytoplasmic components (Figure 6B–6D). Additionally, the KEGG pathway enrichment analysis revealed 14 significant signaling pathways, including those involved in oxidative phosphorylation and cardiac muscle contraction (Figure 6E). To deepen our understanding, we integrated the analysis of upregulated genes specific to various melanocyte subsets with our investigation of genes exhibiting intergroup expression variation. Utilizing this integrative approach, we identified six genes from the up-regulated group that were specifically upregulated in intergroup comparisons, as well as ten genes from the down-regulated group (Figure 6F). By merging and analyzing both the up-regulated genes and the intergroup differentially expressed genes within the melanocyte subpopulations, we generated these insights. Further investigation highlighted that, among the intergroup down-regulated genes, only CLMN, ENSGALG00000037596, and CLN5 were specifically upregulated for the melanocyte subpopulation (Figure 6G). Our research paves the way for future research aimed at elucidating the complex regulatory mechanisms governing melanocyte heterogeneity and function.

Weighted Gene Coexpression Network Analysis

In our research, we meticulously constructed a comprehensive gene co-expression network (WGCNA) utilizing a weighted value system, selecting 12,890 high-quality genes to deeply investigate the functional roles of breast melanocytes, as demonstrated in Figure 7A. Our analysis successfully delineated 27 distinct co-expression modules, as shown in Figures 7B–7F. By aligning these modules with cellular subunits, we identified 22 unique modules associated with specific cell subpopulations, with the results of the module annotation extensively detailed in Table S3.Figure 7 Weighted gene co-expression network analysis in black-bone chicken breast muscle tissue. (A) Scale Indep. (A) scale independence and average connectivity: This panel depicts the scale independence and average connectivity of the gene co-expression network, illustrating the network's robustness against the choice of the soft-thresholding power and the overall connectivity within the network. (B) Clustering Diagram of Module Level: The clustering diagram showcases the dendrogram resulting from hierarchical clustering of genes based on topological overlap, with the Dynamic Tree Cut indicating preliminary module divisions and Merged dynamic reflecting the final consolidated modules. (C) Statistics of the Number of Genes in Each Module: A tabular or graphical summary provides the count of genes within each module, offering insights into the size and composition of the identified gene clusters. (D) Heat Plot of Correlation Between Module Genes: This heat plot visualizes the pattern of gene expression correlations within each module, with color intensity representing the degree of correlation, highlighting the co-expression relationships among genes. (E) Characteristic Gene Neighborhood Heatmap: The Characteristic Gene Neighborhood Heatmap displays the strength of gene correlations in the vicinity of characteristic genes, utilizing a color scale where redder hues signify stronger correlations, thus identifying key genes with central roles within their respective modules (F) Correlation heat diagram between co-expression modules and cell subpopulations: this correlation heat diagram maps the relationships between the co-expression modules and different cell subpopulations, providing a visual representation of the association between module eigengenes and specific cellular characteristics or phenotypes

Figure 7

Melanocyte subpopulations exhibited a significant association with the Yellow module, leading us to enrich the genes within this module for further analysis. The genes within the Yellow module were found to be significantly enriched in 43 GO terms related to CC, including "cytoplasm," "cytoplasmic region," and "organelle-boundary membrane" (Figure 8A). The MF within this module comprised "oxidoreductase activity," "transferase activity," and "catalytic activity" (Figure 8B). In terms of BP, the genes were implicated in processes such as "pigmentation cell differentiation," "pigmentation," "developmental pigmentation," "melanocyte differentiation," and "pigment granule organization" (Figure 8C). KEGG pathway analysis indicated that the majority of genes within the Yellow module are linked to pathways involved in amino acid production or catabolism, as illustrated in Figure 8D. Our gene co-expression network analysis identified 11 genes potentially involved in melanin synthesis, including TRPM1, TYRP1, GPNMB, EDNRB, PMEL, TYR, PAX3, RAB38, MLANA, KIT, and SLC24A5 (Figure 8E). These findings suggest that these genes may play a pivotal role in melanin accumulation within the breast muscle. To further elucidate the mechanisms of melanin deposition and to identify additional genes associated with this process, we integrated a set of highly related genes to construct an extensive gene co-expression network. Through rigorous investigation, we identified 16 genes highly associated with melanin candidate genes, including CYP1B1, HPGDS, CRP, DGKB, B4GALT6, PTPN5, ENSGALG00000049990, C12orf75, STIMATE, NOX3, GLDC, TMSB4, GSTO2, P2RX6, LRRC7, and PCDH10. Notably, 4 genes—LRRC7, STIMATE, ENSGALG00000049990, and GLDC—were centrally located within the co-expression network (Figure 8E), indicating their potential involvement in melanin deposition.Figure 8 Co-expression network analysis of the yellow module. (A) cellular component GO enrichment: A bubble plot illustrates the gene distribution across the top 20 gene ontology (GO) terms classified under cellular components. (B) molecular function GO enrichment: This bubble plot represents the gene count corresponding to the top 20 GO terms in molecular function. (C) biological process GO enrichment: The bubble plot in this panel displays the gene numbers associated with the top 20 biological processes enriched in the Yellow module. (D) KEGG Pathway Enrichment: The bubble plot showcases the gene distribution within the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. (E) Co-expression Network of Yellow Module Genes: This network diagram presents an interconnected map of genes within the Yellow module, with the innermost layer highlighting the top 10% of genes by connectivity. The second layer represents genes with high connectivity (degree ≥ 50), and the outermost layer depicts genes with lower connectivity (degree < 50), providing a hierarchical view of gene interaction strength within the module. (F) co-expression network of melanin deposition-related genes: The co-expression network in this panel focuses on genes related to melanin deposition. The innermost layer specifies candidate genes implicated in melanin deposition. The second layer encompasses genes with high connectivity (degree = 11), and the outermost layer illustrates genes with lower connectivity (degree < 11), revealing the central and peripheral players in the melanin deposition process.

Figure 8

Analysis of Transcription Factor and Protein Interactions

In our study, an in-depth examination of the transcription factors within the melanocyte-associated module of the co-expression network was conducted, revealing the presence of 43 transcription factors (Figure 7C). Notably, six of these factors—SNAI2, PAX3, ZBTB24, TOX3, ESRRB, and CSRNP3—were concurrently identified in the gene co-expression network of the module. Our analysis indicated a significant co-expression of 88 genes alongside PAX3, as illustrated in Figure 9A. In contrast, the remaining five transcription factors exhibited variable correlations with potential melanin deposition-related genes. For instance, only 601 binding sites were identified in the TYRP1 gene, while 827 binding sites were observed in the SLC24A5 gene (Table S4). The transcription factors predicted for each melanin deposition candidate gene demonstrated considerable overlap with those from the Yellow module (Figure 9B). Specifically, RARB, KLF15, and PRDM4 were found in the list of predicted transcription factors for each candidate gene (Figure 9C). This suggests that these 3 transcription factors may exert regulatory effects on melanin deposition by modulating the expression of genes associated with this process.Figure 9 Transcriptional factor analysis within the yellow module. (A) Co-expression network of transcription factor-coding genes: this panel illustrates a co-expression network encompassing six transcription factor (TF) coding genes identified within the Yellow module. The network diagram visually represents the interconnectivity between these TFs, suggesting potential regulatory relationships and shared regulatory pathways. The strength of the connections indicates the degree of co-expression, providing insights into coordinated gene regulation within this module (B) Venn Plots of Predicted Transcription Factors: The Venn plots displayed here compare the transcription factors predicted by candidate genes to those transcription factors that are part of the Yellow module. This comparison allows for the identification of overlapping TFs, which may play crucial roles in the regulation of gene expression within the module. (C) Petal Plot of Overlapping Transcription Factors: The petal plot offers a visual representation of the intersection between different sets of transcription factors. This innovative visualization technique provides a comprehensive overview of the transcriptional factors that are common or unique to various groups, in this case, the candidate genes and the Yellow module.

Figure 9

Further analysis was performed to explore the potential genes related to melanin deposition, their associated genes (Figure 10A), and their functional roles (Table S5), as well as their protein interactions. The results revealed two distinct protein interaction networks. The first network, termed the melanin-related network, encompassed all putative genes implicated in melanin deposition and focused on TYR and TYRP1. The second network comprised CYP1B1, a member of the cytochrome P450 family (CYP), which plays a pivotal role in the metabolism of exogenous substances (Figure 10C), and HPGDS, GSTO1, and CYP1B1, all of which are integral to the glutathione metabolic pathway (Figure 10B). Our findings pave the way for future research aimed at dissecting the complex genetic and molecular interactions governing melanogenesis and pigmentation in avian species.Figure 10 Protein-Protein Interaction and Signaling Pathway Analysis Pertaining to Melanin Deposition. (A) Protein Interaction Analysis of Melanin Deposition Candidate Genes: This panel presents an analysis of the protein-protein interactions among candidate genes implicated in melanin deposition and their associated genes. (B) Glutathione Metabolism Signaling Pathway: The glutathione metabolism signaling pathway is depicted with a focus on the role of HPGDS and GSTO1 in melanin deposition. The red highlighted portion indicates the strategic position of these enzymes within the pathway, underscoring their potential influence on the glutathione-dependent processes related to melanin dynamics. (C) Cytochrome P450 Metabolism Exogenous Signaling Pathway: This component of the figure illustrates a segment of the cytochrome P450 metabolism exogenous signaling pathway, emphasizing the role of CYP1B1 (Cytochrome P450 Family 1 Subfamily B Member 1). The red section highlights the location of CYP1B1, indicating its involvement in the metabolic processes that may intersect with melanin deposition.

Figure 10

Potential Intercellular Communication in Melanocytes of the Breast Muscle

We used CellPhoneDB to map the communication channels between cells in the breast muscle of a Xuefeng black-bone chicken using the snRNA-seq. Our attention was focused on genes linked to 511 ligand-receptor pairings, as Figure 11A illustrates. The results of the investigation showed that APOA1+ adipocytes, fibroblasts, and fibro-adipogenic progenitors are important players in intercellular communication, especially when melanocytes are the receiving cells (Figure 11B). This prompted us to examine the ligand-receptor relationships that melanocytes have with different biological entities. High-scoring connections including PTPRK-WNT5A, NOTCH1-JAG1, IGF1R-IGF1, IDE-GCG, and ROR2-WNT5A were found during the research, indicating that these signals may control the distribution of intracellular melanin. Additionally, our data showed a substantial link between melanocytes and fibroblast progenitor cells, APOA1+ adipocytes, and fibroblasts.Figure 11 Cell-cell communication analysis in the context of melanocytes (A) Comprehensive cell-cell communication network: this panel illustrates the cell-cell communication network encompassing all cell types within the tissue. (B) melanocyte-centric communication network: the melanocyte-centric communication network is presented, with melanocytes at the focal point. (C) Dot Plot Overview of Melanocyte Interactions: A dot plot provides an overview of the cell-cell interactions involving melanocytes and 3 other associated cell types. Each dot represents a specific interaction, with annotations indicating the nature and strength of the communication (D) Heat Plot of Ligand Activity (Melanocyte vs. APOA1+ Adipocyte): This heat plot displays the ligand activity between melanocytes and APOA1+ adipocytes. The color intensity reflects the level of activity, revealing the specific signaling molecules and their interactions at the interface of these two cell types. (E) Heat Plot of Ligand Activity (Melanocyte vs. Fibroadipogenic Progenitor): Similarly, this heat plot focuses on the ligand activity between melanocytes and fibroadipogenic progenitors. (F) Heat Plot of Ligand Activity (Melanocyte vs. Fibroblast): The final heat plot in the series examines the ligand activity between melanocytes and fibroblasts.

Figure 11

We selected 10 signaling pathways from the KEGG database that are implicated, either directly or indirectly, in melanin deposition. This selection was made to explore the interplay between melanin deposition and cell-cell interactions within the breast muscle of a Xuefeng black-bone chicken. These pathways were considered as a distinct set of genes for our investigation of ligand cell activity. The signaling pathways are illustrated in Figures 11D–11F, and include the TGF-beta signaling pathway, the JAK-STAT signaling pathway, the Hippo signaling pathway, the Wnt signaling pathway, the Notch signaling pathway, the MAPK signaling pathway, the PI3K-Akt signaling pathway, the cAMP signaling pathway, the NF-kappa B signaling pathway, and melanogenesis itself. Our findings indicate that cell-to-cell interactions potentially contribute to melanin formation in the breast muscle of black-bone chickens.

DISCUSSION

The black-bone chicken serves as an exemplary model for the study of melanin deposition due to its high melanin content (Nganvongpanit et al., 2020). Key genes, including TYR, TYRP1, MITF, PMEL, RAB29, and SLC5A6, as well as certain competing endogenous RNAs (ceRNAs), have been identified as pivotal in the melanin deposition process within the breast muscle of this breed (Yu et al., 2018; Li et al., 2024). Despite the preliminary insights offered by previous bulk RNA studies, there remains a substantial void in our comprehension of the genetic determinants of melanin deposition in the muscle tissue of black-bone chickens. Specifically, no research has yet explored melanin deposition in the breast muscle of black-bone chickens at the single-cell level. This study is designed to address this gap by elucidating the melanin accumulation process in the breast muscle of Xuefeng black-bone chickens through single-nucleus RNA sequencing (snRNA-seq) analysis. We have meticulously generated a cellular nuclear map from a single cell within the breast muscle tissue of the Xuefeng black-bone chicken. This map has unveiled the presence of 18 distinct cell types, predominantly myogenic cells, interspersed with stem cells and immune cells. Strikingly, melanocytes were found to account for only 0.18% of the total cellular composition, underscoring their rarity within the breast muscle of the Xuefeng black-bone chicken. By scrutinizing gene expression patterns that are exclusive to specific cell subpopulations, our study has garnered invaluable insights. These insights are expected to guide future detailed investigations into the differential melanin deposition within the breast muscle of black-bone chickens. Moreover, this research will aid in the selection and breeding processes, enabling the enhancement of chickens with the desirable trait of increased blackness.

Melanin deposition is a multifaceted biological phenomenon that entails the intricate and simultaneous regulation of multiple genes throughout the synthetic pathway (Barsh, 1996). Despite the preliminary understanding garnered from studies on the differential melanin deposition in black-bone chicken tissues, there is an acknowledged necessity for more in-depth research to refine our insights. Our research has indicated that the variance in melanocyte populations within the breast muscle of Xuefeng black-bone chickens is predominantly linked to muscle contraction and energy metabolism. Through a comparative genomic analysis of melanocytes isolated from breast muscle tissues of diverse Xuefeng black-bone chickens, we propose that the observed heterogeneity in melanin deposition may be attributed to the complex interactions between myogenic cells and melanocytes. In our analyses, we have identified CLMN, ENSGALG00000037596, and CLN5 as genes that exhibit down-regulation across the groups and are significantly expressed within melanocyte subpopulations.These genes may play a role in the variable melanin deposition seen in the breast muscle of Xuefeng black-bone chickens. Specifically, Marzinke et al. (2010); Marzinke and Clagett-Dame (2012) reported that CLMN primarily affects neuronal responses to atRA. However, to the best of our knowledge, there is no existing evidence to suggest that CLMN directly affects melanocytes or melanin deposition. CLN5, a soluble protein predominantly localized within the endo-lysosomal compartment, has been identified as crucial for its normal functioning. Mutations in CLN5 have been associated with an abnormal accumulation of lipofuscin, which, according to Kim's research, correlates negatively with the expression of melanin-eligible proteins(Mancini et al., 2015; Ge et al., 2018; Kim and Kim, 2021; Yasa et al., 2021). This correlation implies a potential inhibitory effect of CLN5 on melanin production within the organism). ENSGALG00000037596, which demonstrates higher expression levels in melanocytes compared to CLMN and CLN5, remains a gene whose function in melanocytes is not yet fully understood. It is imperative that future studies concentrate on deciphering the functional role of ENSGALG00000037596 in melanocytes.

Furthermore, our findings introduce an innovative approach to the classification of melanocyte subpopulations through the combined assessment of CLN5 and TYR, a key enzyme in melanin synthesis. The identification of cells co-expressing both CLN5 and TYR as high melanocyte-content cells presents a promising method for future research aimed at understanding the functional attributes of melanocytes and their contribution to pigmentation.

In our study, we seamlessly integrated a gene table module with the melanocyte subpopulation of the breast muscle in black-bone chickens, employing a weighted gene co-expression network analysis (WGCNA). This integrative approach allowed us to identify a cohort of 11 genes—TRPM1, TYRP1, GPNMB, EDNRB2, TYR, PMEL, PAX3, RAB38, MLANA, KIT, and SLC24A5—that demonstrated significant associations with genes implicated in melanin deposition. These genes are theorized to play a pivotal role in the melanin deposition process within the breast muscle of black-bone chickens, a notion supported by a wealth of existing literature (Fernandez et al., 2009; Wiriyasermkul et al., 2020; Hu et al., 2021; Xu et al., 2023; Yu et al., 2023). Our research also highlighted the importance of 4 genes—STIMATE, LRRC7, ENSGALG00000049990, and GLDC—that are integral components of the melanin subgroup's gene co-expression module, exhibiting robust co-expression patterns with genes related to melanin. A thorough literature review indicates that STIMATE, a known regulator of calcium influx in vertebrates, interacts with STIM1, facilitating its conformational change (Jing et al., 2015). STIM1 is recognized for its role in interacting with ADCY6 to regulate melanogenesis and with ORAI1 to mediate the influx of extracellular calcium ions (Hogan, 2015). The inhibition of ORAI1 expression has been correlated with reduced melanin production and tyrosinase activity (Stanisz et al., 2012), pointing to a potential link between melanogenesis and calcium ion signaling pathways.

Our hypothesis posits that STIMATE may modulate melanin production through its interaction with STIM1. Conversely, LRRC7, while not previously linked to melanin production, interacts with calcium channel components CACNA1C, CACNA1D, and CAMKII, potentially influencing melanogenesis by modulating calcium channel activity. The enzyme glutathione is known to impede melanin synthesis by inhibiting tyrosinase activity and by competitively binding to dopaquinone. However, elevated expression of GLDC has been shown to decrease glutathione levels and intracellular reactive oxygen species, implying that GLDC could indirectly modulate melanin synthesis (Lu et al., 2021; Jog et al., 2021; Jog et al., 2021). The precise role of ENSGALG00000049990 in melanin synthesis is yet to be determined and awaits further investigation to reveal its relationship with melanogenic processes.

Within the melanocyte gene subgroup module, our investigation identified six transcription factors: SNAI2, PAX3, ZBTB24, TOX3, ESRRB, and CSRNP3. A substantial body of literature has underscored the indispensable role of PAX3 in melanocyte function and the orchestration of melanin deposition (Berlin et al., 2012; Yu et al., 2018; Li et al., 2022). Deficiency in SNAI2 has been shown to impede melanocyte migration and maturation, precipitating auditory pigmentation syndromes attributed to melanocyte scarcity (Shirley et al., 2012; Huang et al., 2024). This highlights the critical regulatory function of SNAI2 in melanocyte biology and pigmentation. At present, the roles of ZBTB24, TOX3, ESRRB, and CSRNP3 in melanocyte function or melanin deposition remain enigmatic. Further inquiry is essential to elucidate their contributions to melanocytes and the melanogenesis process. Our transcription factor prediction analysis has uncovered potential binding sites for RARB, KLF15, and PRDM4 within the promoter regions of 11 candidate genes, as identified in our screening process. A thorough literature review indicates that the specific inhibition of RARB results in elevated glycolysis rates and attenuated melanocyte sensitivity (Abildgaard et al., 2017). Moreover, RARB is implicated in the suppression of cell proliferation while concurrently augmenting melanin biosynthesis through the upregulation of tyrosinase activity (Lotan and Lotan, 1981; Kishi et al., 2001), thereby exerting a regulatory influence on melanocyte activity. Conversely, KLF15 and PRDM4 are predominantly implicated in adipose tissue and lipid metabolism, with no established links to melanin synthesis or melanocyte function. Further research is necessary to explore any potential indirect effects these factors may have on melanogenesis. In summary, our study has nominated 3 transcription factors—RARB, KLF15, and PRDM4—for subsequent investigation into their involvement in melanin deposition within breast muscle. This research may yield novel insights into the molecular mechanisms of pigmentation and inform therapeutic strategies for pigmentary disorders.

In our study, we conducted a comprehensive analysis of the protein-protein interaction network among genes associated with melanin deposition, revealing a complex interplay that is central to melanin biosynthesis. We identified a specific protein interaction network involving HPGDS, GSTO1, and CYP1B1, enzymes that are significantly implicated in cytotoxic metabolism. Notably, HPGDS, GSTO1, and CYP1B1 are actively engaged in detoxification processes. Upon the entry of an exogenous toxic substance into the organism, it is subjected to a series of metabolic reactions facilitated by Phase I and Phase II metabolizing enzymes. Within this detoxification cascade, CYP enzymes and glutathione S-transferases are recognized as essential components, playing a crucial role in safeguarding cells from the detrimental effects of toxicants. The synthesis of melanin, as posited by existing literature (Mohania et al., 2017; Solano, 2020; Cheng et al., 2022), may be responsive to environmental stimuli in the Xuefeng black-bone chicken, potentially analogous to the skin's reaction to UV radiation. This hypothesis is supported by the emerging view that melanin serves a protective function beyond pigmentation, also acting as a biological shield against environmental stressors. Our findings pave the way for future research to delve into the intricate relationship between environmental factors, detoxification pathways, and melanin deposition. This investigation will contribute to a more sophisticated comprehension of the regulatory mechanisms that govern melanogenesis in avian species, with implications for understanding pigmentary responses in various biological contexts. Further research has underscored the significance of these interactions in the context of melanoma. For instance, the interplay between PTPRK and WNT has been shown to mitigate the proliferative and migratory behaviors of melanoma cells, as reported by Easty et al. (2011). Similarly, the JAG1-NOTCH1 signaling axis has been implicated in the transformation of skin melanocytes into melanoma, a finding supported by Lian et al. (2024). Additionally, the migration of melanoma cells is modulated by the ROR2-WNT5A interaction, as evidenced by O'Connell et al. (2010).

Recent research has delineated a sophisticated intercellular communication network between melanocytes and other cellular constituents within the pectoral muscle. This discovery significantly augments our understanding of the intricate mechanisms that regulate the development and physiological role of melanocytes in this tissue. The elucidation of cellular communication has identified pivotal ligand-receptor interactions critical to melanocyte signaling, specifically PTPRK-WNT5A, NOTCH1-JAG1, IGF1R-IGF1, IDE-GCG, and ROR2-WNT5A, complemented by the involvement of five additional signaling pathways. Subsequent studies have underscored the relevance of these interactions in melanoma biology. Notably, the PTPRK-WNT5A interaction has been shown to diminish the proliferative and migratory capabilities of melanoma cells, as reported by Easty et al. (2011). The JAG1-NOTCH1 signaling axis has been implicated in the transformation of cutaneous melanocytes into melanoma, a phenomenon corroborated by Lian et al. (2024). Moreover, the migration of melanoma cells is modulated by the ROR2-WNT5A interaction, a finding supported by O'Connell et al. (2010). Given the common lineage between melanocytes and melanoma cells, these signaling pathways predominantly influence the latter. This correlation hints at a potential link between variations in melanin deposition within the pectoral muscle and the aforementioned signaling molecules. However, further research is imperative to fully elucidate the underlying regulatory mechanisms and their implications in melanocyte biology.

CONCLUSIONS

This study presents the inaugural single-cell nuclear transcriptome profiles of Xuefeng black-bone chicken breast muscle, revealing the presence of 18 distinct cell types, including melanocytes. Comparative analyses hint that STIMATE, LRRC7, ENSGALG00000049990, and GLDC as potential key genes governing precise melanin deposition in breast muscle. Furthermore, RARB, KLF15, and PRDM4 emerge as potential transcription factors regulating breast melanin deposition, while HPGDS, GSTO1, and CYP1B1 play crucial roles in melanocyte metabolism. Cell signaling pathways, such as PTPRK-WNT5A, NOTCH1-JAG1, IGF1R-IGF1, IDE-GCG, and ROR2-WNT5A may influence the function of breast melanocytes. Our findings offer fresh perspectives on the molecular mechanism underlying melanin deposition in the breast muscle tissue of the black-bone chicken, providing invaluable insights for conserving and utilizing poultry genetic resources.

DISCLOSURES

The authors declare no conflicts of interest.

Declaration of Generative AI and AI-assisted technologies in the writing process: During the preparation of this work, the author(s) only used these technologies to improve readability and language.

Appendix Supplementary materials

Image, application 1

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (32072711 ), the Key Project of Postgraduate Research Innovation in Hunan Province (CX20230712 ).

Ethics Approval and Consent to Participate: All sample collection and treatment procedures were conducted in strict accordance with the ethical guidelines and protocol approved by the experimental animals were treated by the regulations of the National Research Council of China and approved by the Animal Welfare Committee of Hunan Agricultural University, Changsha, China (Standard No. 2019022).

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.104259.
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REFERENCES

Abildgaard C. Dahl C. Abdul-Al A. Christensen A. Guldberg P. Inhibition of retinoic acid receptor β signaling confers glycolytic dependence and sensitization to dichloroacetate in melanoma cells Oncotarget 8 2017 84210 29137417
Alkaslasi M.R. Piccus Z.E. Hareendran S. Silberberg H. Chen L. Zhang Y. Petros T.J. Le Pichon C.E. Single nucleus RNA-sequencing defines the unexpected diversity of cholinergic neuron types in the adult mouse spinal cord Nat Commun 12 2021 2471 33931636
Barsh G.S. The genetics of pigmentation: from fancy genes to complex traits Trends Genet. 12 1996 299 305 8783939
Berlin I. Denat L. Steunou A.-L. Puig I. Champeval D. Colombo S. Roberts K. Bonvin E. Bourgeois Y. Davidson I. Phosphorylation of BRN2 modulates its interaction with the Pax3 promoter to control melanocyte migration and proliferation Mol. Cell Biol. 32 2012 1237 1247 22290434
Cheng B. Wang Y. Ayanlaja AA. Zhu J. Kambey PA. Qiu Z. Zhang C. Hu W. Glutathione S-Transferases S1, Z1 and A1 serve as prognostic factors in glioblastoma and promote drug resistance through antioxidant pathways Cells 11 2022 3232 36291099
Davenport K.M. Ortega M.S. Liu H. O'Neil E.V. Kelleher A.M. Warren W.C. Spencer T.E. Single-nucleus RNA sequencing (snRNA-seq) uncovers trophoblast cell types and lineages in the mature bovine placenta Proc. Natl. Acad. Sci. U S A 120 2023 e2221526120
De Micheli A.J. Laurilliard E.J. Heinke C.L. Ravichandran H. Fraczek P. Soueid-Baumgarten S. De Vlaminck I. Elemento O. Cosgrove B.D. Single-cell analysis of the muscle stem cell hierarchy identifies heterotypic communication signals involved in skeletal muscle regeneration Cell Rep. 30 2020 3583 3595 e3585 32160558
Deng C.-C. Hu Y.-F. Zhu D.-H. Cheng Q. Gu J.-J. Feng Q.-L. Zhang L.-X. Xu Y.-P. Wang D. Rong Z. Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases Nat. Commun. 12 2021 3709 34140509
Dou T. Yan S. Liu L. Wang K. Jian Z. Xu Z. Zhao J. Wang Q. Sun S. Talpur M.Z. Integrative analysis of transcriptomics and metabolomics to reveal the melanogenesis pathway of muscle and related meat characters in Wuliangshan black-boned chickens BMC Genomics 23 2022 1 15 34979896
Easty DJ. Gray SG. O'Byrne KJ. O'Donnell D. Bennett DC. Receptor tyrosine kinases and their activation in melanoma Pigment Cell Melanoma Res. 24 2011 446 461 21320293
Fernandez L.P. Milne R.L. Pita G. Floristan U. Sendagorta E. Feito M. Avilés J.A. Martin-Gonzalez M. Lázaro P. Benítez J. Pigmentation-related genes and their implication in malignant melanoma susceptibility Exp Dermatol 18 2009 634 642 19320733
Franjic D. Skarica M. Ma S. Arellano J.I. Tebbenkamp A.T. Choi J. Xu C. Li Q. Morozov Y.M. Andrijevic D. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells Neuron 110 2022 452 469 34798047
Ge L. Li H.Y. Hai Y. Min L. Xing L. Min J. Shu H.X. Mei O.Y. Novel mutations in CLN5 of Chinese Patients with neuronal ceroid lipofuscinosis J. Child Neurol. 33 2018 837 850 30264640
Hang C. Song Y. Li Y.n. Zhang S. Chang Y. Bai R. Saleem A. Jiang M. Lu W. Lan F. Knockout of MYOM1 in human cardiomyocytes leads to myocardial atrophy via impairing calcium homeostasis J. Cell Mol. Med 25 2021 1661 1676 33452765
Hogan P.G. The STIM1–ORAI1 microdomain Cell Calcium 58 2015 357 367 26215475
Hu S. Bai S. Dai Y. Yang N. Li J. Zhang X. Wang F. Zhao B. Bao G. Chen Y. Deubiquitination of MITF-M regulates melanocytes proliferation and apoptosis Front. Mol. Biosci. 8 2021 692724
Huang C. Wei Y. Kang Z. Zhang W. Wu Y. Research note: transcriptome analysis of skeletal muscles of black-boned chickens, including 2 types (wild and mutated) of Taihe black-boned silky fowl and 1 type (wild) of Yugan black-boned chicken Poult. Sci. 103 2024 103240
Jankowski M. Mozdziak P. Petitte J. Kulus M. Kempisty B. Avian satellite cell plasticity Animals (Basel) 10 2020 1322 32751789
Jing J. He L. Sun A. Quintana A. Ding Y. Ma G. Tan P. Liang X. Zheng X. Chen L. Proteomic mapping of ER–PM junctions identifies STIMATE as a regulator of Ca2+ influx Nat. Cell Biol. 17 2015 1339 1347 26322679
Jian H. Zu P. Rao Y. Li W. Mou T. Lin J. Zhang F. Comparative analysis of melanin deposition between Chishui silky fowl and Taihe silky fowl J. Appl. Anim. Res. 49 2021 366 373
Jog R. Chen G. Wang J. Leff T. Hormonal regulation of glycine decarboxylase and its relationship to oxidative stress Physiol. Rep. 9 2021 e14991 34342168
Kim J.H. Kim M.-M. The relationship between melanin production and lipofuscin formation in Tyrosinase gene knockout melanocytes using CRISPR/Cas9 system Life Sci. 284 2021 119915
Kishi H. Kuroda E. Mishima H.K. Yamashita U. Role of TGF-β in the retinoic acid-induced Inhibition of Proliferation and melanin synthesis in chickretinal pigmeent epithelial cells in vitro Cell. Biol. Lnt. 25 2001 1125 1129
Li J. Xing S. Zhao G. Zheng M. Yang X. Sun J. Wen J. Liu R. Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing BMC Genom. 21 2020 1 11
Li Z. Li Q. Xu C. Yu H. Molecular characterization of Pax7 and its role in melanin synthesis in Crassostrea gigas Comp. Biochem. Physiol. B Biochem. Mol. Biol. 260 2022 110720
Li R. Li D. Xu S. Zhang P. Zhang Z. He F. Li W. Sun G. Jiang R. Li Z. Whole-transcriptome sequencing reveals a melanin-related ceRNA regulatory network in the breast muscle of Xichuan black-bone chicken Poult. Sci. 103 2024 103539 38382189
Lian W. Xiang P. Ye C. Xiong J. Single-cell RNA sequencing analysis reveals the role of cancer-associated fibroblasts in skin melanoma Curr. Med. Chem. 31 2024 7015 7029
Lin L.-C. Chen W.-T. The study of antioxidant effects in melanins extracted from various tissues of animals Asian Austral J. Anim. 18 2005 277 281
Liu J. Huang Y. Tian Y. Nie S. Xie J. Wang Y. Xie M. Purification and identification of novel antioxidative peptide released from Black-bone silky fowl (Gallus gallus domesticus Brisson) Eur Food Res Technol 237 2013 253 263
Lotan R. Lotan D. Enhancement of melanotic expression in cultured mouse melanoma cells by retinoids J. Cell Physiol. 106 1981 179 189 6260817
Lu Y. Tonissen K.F. Di Trapani G. Modulating skin colour: role of the thioredoxin and glutathione systems in regulating melanogenesis Biosci Rep 41 2021 BSR20210427
Mancini C. Nassani S. Guo Y. Chen Y. Giorgio E. Brussino A. Di Gregorio E. Cavalieri S. Lo Buono N. Funaro A. Adult-onset autosomal recessive ataxia associated with neuronal ceroid lipofuscinosis type 5 gene (CLN5) mutations J. Neurol. 262 2015 173 178 25359263
Mantri M. Scuderi G.J. Abedini-Nassab R. Wang M.F. McKellar D. Shi H. Grodner B. Butcher J.T. De Vlaminck I. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis Nat. Commun. 12 2021 1771 33741943
Marzinke M.A. Henderson E.M. Yang K.S. See A.W.M. Knutson D.C. Clagett-Dame M. Calmin expression in embryos and the adult brain, and its regulation by all-trans retinoic acid Dev. Dyn 239 2010 610 619 20014094
Marzinke M.A. Clagett-Dame M. The all-trans retinoic acid (atRA)-regulated gene Calmin (Clmn) regulates cell cycle exit and neurite outgrowth in murine neuroblastoma (Neuro2a) cells Exp. Cell Res. 318 2012 85 93 22001116
Mohania D. Chandel S. Kumar P. Verma V. Digvijay K. Tripathi D. Choudhury K. Mitten S.K. Shah D. Ultraviolet radiations: skin defense-damage mechanism Adv Exp Med Biol 996 2017 71 87 29124692
Muhl L. Genové G. Leptidis S. Liu J. He L. Mocci G. Sun Y. Gustafsson S. Buyandelger B. Chivukula I.V. Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell identification and discrimination Nat. Commun. 11 2020 3953 32769974
Nganvongpanit K. Kaewkumpai P. Kochagul V. Pringproa K. Punyapornwithaya V. Mekchay S. Distribution of melanin pigmentation in 33 organs of Thai black-bone chickens (Gallus gallus domesticus) Animals (Basel) 10 2020 777 32365908
O'Connell M.P. Fiori J.L. Xu M. Carter A.D. Frank B.P. Camilli T.C. French A.D. Dissanayake S.K. Indig F.E. Bernier M. Taub D.D. Hewitt S.M. Weeraratna A.T. The orphan tyrosine kinase receptor, ROR2, mediates Wnt5A signaling in metastatic melanoma Oncogene 29 2010 34 44 19802008
Ortmayr K. Causon T.J. Hann S. Koellensperger G. Increasing selectivity and coverage in LC-MS based metabolome analysis Trac-Trend Anal Chem 82 2016 358 366
Praud C. Jimenez J. Pampouille E. Couroussé N. Godet E. Bihan-Duval E.Le Berri C. Molecular phenotyping of white striping and wooden breast myopathies in chicken Front. Physiol. 11 2020 633 32670085
Stanisz H. Stark A. Kilch T. Schwarz E.C. Müller C.S. Peinelt C. Hoth M. Niemeyer B.A. Vogt T. Bogeski I. ORAI1 Ca2+ channels control endothelin-1-induced mitogenesis and melanogenesis in primary human melanocytes J. Invest. Dermatol. 132 2012 1443 1451 22318387
Shi H. Fu J. He Y. Li Z. Kang J. Hu C. Zi X. Liu Y. Zhao J. Dou T. Hyperpigmentation inhibits early skeletal muscle development in Tengchong Snow Chicken breed Genes (Basel) 13 2022 2253 36553521
Shirley S.H. Greene V.R. Duncan L.M. Cabala C.A.T. Grimm E.A. Kusewitt D.F. Slug expression during melanoma progression Am. J. Pathol. 180 2012 2479 2489 22503751
Stuart T. Satija R. Integrative single-cell analysis Nat. Rev. Genet. 20 2019 257 272 30696980
Solano F. Photoprotection and skin pigmentation: melanin-related molecules and some other new agents obtained from natural sources Molecules 25 2020 1537 32230973
Svoradova A. Zmrhal V. Venusova E. Slama P. Chicken mesenchymal stem cells and their applications: a mini review Animals (Basel) 11 2021 1883 34202772
Wiriyasermkul P. Moriyama S. Nagamori S. Membrane transport proteins in melanosomes: regulation of ions for pigmentation Biochim Biophys Acta Biomembr 1862 2020 183318
Wang L. Zhao X. Liu S. You W. Huang Y. Zhou Y. Chen W. Zhang S. Wang J. Zheng Q. Wang Y. Shan T. Single-nucleus and bulk RNA sequencing reveal cellular and transcriptional mechanisms underlying lipid dynamics in high marbled pork NPJ Sci. Food 7 2023 23 37268610
We X. Bing X. Ya L. Li P. Qu X. He C. Guo S. Factors affecting melanin deposition in breast muscle of Xuefeng black-bone chicken based on difference of histomorphology and gene expression China Poultry 45 2023 1 9 (in Chinese)
Xu M. Tang S. Liu X. Deng Y. He C. Guo S. Qu X. Genes influencing deposition of melanin in breast muscle of the Xuefeng black bone chicken based on bioinformatic analysis Genome 66 2023 212 213 37094380
Yasa S. Sauvageau E. Modica G. Lefrancois S. CLN5 and CLN3 function as a complex to regulate endolysosome function Biochem. J. 478 2021 2339 2357 34060589
Yu F. Lu Y. Zhong Z. Qu B. Wang M. Yu X. Chen J. Mitf involved in innate immunity by activating tyrosinase-mediated melanin synthesis in Pteria penguin Front. Immunol. 12 2021 626493
Yu S. Wang G. Liao J. Shen X. Chen J. Integrated analysis of long non-coding RNAs and mRNA expression profiles identified potential interactions regulating melanogenesis in chicken skin Br. Poult. Sci. 64 2023 19 25 35979716
Yu F. Qu B. Lin D. Deng Y. Huang R. Zhong Z. Pax3 gene regulated melanin synthesis by tyrosinase pathway in Pteria penguin Int. J. Mol. Sci. 19 2018 3700 30469474
Zhang J. Lv C. Mo C. Liu M. Wan Y. Li J. Wang Y. Single-cell RNA sequencing analysis of chicken anterior pituitary: a bird's-eye view on vertebrate pituitary Front. Physiol. 12 2021 562817
Zhou S. Zeng H. Huang J. Lei L. Tong X. Li S. Zhou Y. Guo H. Khan M. Luo L. Epigenetic regulation of melanogenesis Ageing Res Rev 69 2021 101349
