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Whole-genome resequencing reveals melanin deposition candidate genes of Luning chicken

BMC Genomics volume 25, Article number: 858 (2024) Cite this article

Abstract

Background

Melanin in the black-bone chicken’s body is considered the material basis for its medicinal effects and is an economically important trait. Therefore, improving the melanin content is a crucial focus in the breeding process of black-bone chickens. Luning chickens are black-bone chickens, with black beaks, skin, and meat. To investigate the genetic diversity and molecular mechanisms of melanin deposition in Luning chickens, we conducted whole-genome resequencing to analyze their breeding history and identify candidate genes influencing their black phenotype, along with transcriptome sequencing of dorsal skin tissues of male Luning chickens.

Results

Population structure analysis revealed that Luning chickens tend to cluster independently and are closely related to Tibetan chickens. Runs of homozygosity analysis suggested potential inbreeding in the Luning chicken and Tibetan chicken population. By combining genetic differentiation index (Fst) and nucleotide diversity (θπ) ratios, we pinpointed selected regions associated with melanin deposition. Gene annotation identified 540 genes with the highest Fst value in LOC101750371 and LOC121108313, located on the 68.24–68.58 Mb interval of chromosome Z. Combining genomic and transcriptomic data, we identified ATP5E, EDN3, and LOC101750371 as candidate genes influencing skin color traits in black-bone chickens.

Conclusions

This study characterized the evolutionary history of Luning chickens and preliminarily excavated candidate genes influencing the genetic mechanism of pigmentation in black-bone chickens, providing valuable insights for the study of animal melanin deposition.

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Background

Domestication is a mutually beneficial co-evolutionary process that enhances the survival adaptations of both domesticators and the domesticated. It marks a crucial shift in human lifestyle from hunting to pastoralism [1]. This process leads to genetic differentiation and diversification, increasing the breeds’ productivity and adaptability to the environment, and promoting phenotypic diversity in terms of morphology and color [2, 3].

The chicken (Gallus Gallus domesticus) has been an early domesticated agricultural economic animal, providing a stable source of animal protein for humans. They exhibit significant phenotypic diversity, with different breeds distinguished by phenotypic characteristics such as body size, plumage or skin color, and crown type [4]. The black-bone trait is an important indicator in the chicken domestication process. According to the Compendium of Materia Medica and the Medicinal Fauna of China, black-bone chickens process various medicinal properties and has been widely used in pharmaceuticals [5], nutritional supplements [6], and immune support [7]. The melanin content determines the shade of color and affects the economic benefits of black-bone chicken in China. Therefore, breeders aim to achieve melanin coloration during the breeding of black-bone chickens.

The Luning chicken originates from Mianning County, Sichuan Province, China. It has well-developed pectoral/leg muscles and high egg-laying performance, characterized by black beaks, skin, and meat. Situated in the mountainous terrain of Liangshan Yi Autonomous Prefecture in Sichuan Province, Mianning County has promoted the Luning chicken as a valuable economic resource to boost local livestock production. Despite its popularity among consumers, the Luning chicken faced extinction in the past due to various factors, and its breeding history remains poorly documented. Therefore, exploring the Luning chicken at the genomic level is crucial to understanding the genetic mechanisms of melanin deposition. Whole-genome resequencing (WGRS) stands out for its precision, reproducibility, and accuracy, making it a preferred method for identifying candidate genes associated with essential traits in livestock and poultry species like chickens [8, 9], pigs [10, 11], cattle [12], sheep [13, 14], ducks [15] and geese [16].

In this study, the whole genomes of 5 Luning chickens were sequenced. Based on the genomic data of Luning chickens, we employed WGRS to uncover the actual composition of the Luning chicken population. This revealed insights into population structure, evolutionary history, and genomic diversity for characterization, as well as the identification of genes that have undergone positive selection in the evolutionary journey of the Luning chicken based on genomic diversity. Furthermore, we delved into the genetic foundation of melanin deposition through an integrated analysis of genomic and transcriptome sequencing (RNA-seq) data. This study contributes to the preservation of the indigenous Luning chicken breeds and the advancement of germplasm resources, offering fundamental research data for the selection and enhancement of Luning chicken breed.

Results

Genetic variation in Luning chicken

We performed WGRS of 5 Luning chickens. Utilizing the pre-processing tool fastp to filter low-quality sequences from the raw data, we obtained a total of 185 Gb of high-quality genomic data, with each chicken contributing over 30 Gb per chicken (Supplementary Table S1). The Q30 scores exceeded 84%, and the average GC content was 41.46%. When aligning the obtained sequences to the chicken reference genome (GRCg7b), the mapping rate was over 99.78%, with an average sequencing depth of 34× per individual (Supplementary Table S2). Following accurate quality filtering of the identified SNPs and InDels, we discovered 35,436,078 SNPs and 4,734,764 InDels (Supplementary Table S3 & Supplementary Table S4). Figure 1b illustrates the Circos map integrating the SNPs and InDels loci of LNC1, while the Circos map of the remaining 4 Luning chicken individuals are displayed in Supplementary Fig. S1. To visualize the distribution of SNPs more effectively, we categorized them into 11 groups based on their context (Fig. 1c). The majority of these SNPs were located in intergenic (36.71%) and intronic (52.12%) regions, with only 1.63% in exonic regions. Additionally, 4.19% were found in 5´and 3´UTRs, and 5.15% in upstream and downstream regions. A smaller proportion of SNPs were within ncRNA genes (0.21%) and splicing regions (0.01%).

Fig. 1
figure 1

Experiment design and variant statistic. (a) Geographical origin of the 8 chicken breeds used in this study. The map is made based on the standard map with the review number GS (2016) 1569 downloaded from the standard map service station of the Ministry of Natural Resources, and the base map is unmodified. (b) Circos map of genomic variation. Chromosomes are represented in different colors in the outermost circle, purple loci indicate the distribution of genomic SNP densities, and blue loci indicate the distribution of genomic InDel densities. (c) SNP distribution pie chart. Different colors represent SNPs within various functional regions, this pie chart represents the total SNPs. (d) SNPs distribution map of 40 samples. Heat map of the differential distribution of SNPs based on genomic data from 40 chickens of 8 breeds, colors represent pairwise genetic distance

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Phylogeny and population genetic structure as well as migration events

We analyzed genome-wide and evolutionary relationships between Luning chickens and other breeds based on 40 individual genomic-wide SNPs (Fig. 1a, Supplementary Table S5). The heat map revealed two primary genetic clusters, with SC forming a separate category and the remaining seven varieties clustered together. Cluster analysis revealed that the JBC samples exhibited more variation among themselves, while the other 7 breeds showed a smaller degree of variation and were more closely related evolutionarily (Fig. 1d). We employed the NJ algorithm to calculate genetic distances between populations based on genome-wide polymorphic SNPs in 8 breeds and construct a phylogenetic tree (Fig. 2a). LNC clustered within the same branch as TBC, RJF, MBC, and XBC, showing the nearest genetic distance to TBC. Interesting, JBC is divided into three branches. The JBC population from PCA analysis showed individuals mixed with MBC and RJF, respectively, with RJF further divided into two parts. Additionally, the other 6 breeds formed distinct clusters with a high level of genetic differentiation (Fig. 2b). The four principal components PC1, PC2, PC3, and PC4 each accounted for 7.41%, 5.22%, 4.75%, and 4.05% of the total variability.

To estimate the ancestral proportions of the eight chicken breeds, we performed Admixture analysis on 40 sampled chickens, with K representing the number of assumed ancestral populations (Fig. 2c). The optimal choice for the current 40 samples was determined to be is K = 2, based on the lowest Cross Validation error (Supplementary Fig. S2). At K = 2, LNC exhibited a similar pedigree composition to TBC, while SC tended to cluster independently, and the other 5 populations genetically fell into one cluster. Furthermore, at K = 2, different genetic clusters were clearly formed within the JBC population, consistent with the NJ tree and PCA results. Based on the above analysis, the correlation between the JBC samples was low, potentially affecting the accuracy of the results, leading to their exclusion from subsequent analyses. ROH refers to a class of genomes exhibiting continuous uninterrupted pure heterozygosity, characterized by the absence of heterozygotes in one chromosomal region [17]. By comparison of ROH, we revealed higher ROH values and lower heterozygosity among LNC and TBC (Fig. 2d, Supplementary Fig. S3). This result is consistent with the phenomenon that TBC and LNC as local breeds tend to be inbred in relatively closed areas. We divided the populations into black-bone (LNC, XBC, MBC, and SC) and non-black-bone (TBC, PYC, and RJF) groups for the LD analysis. As shown in Fig. 2e, there was no significant difference in the decay rate between the black-bone and the non-black-bone.

Fig. 2
figure 2

Analysis of population variation and structure of the LNC and their relatives. (a) NJ tree was constructed using polymorphisms detected in the 40 individual chickens. (b) Three-way principal component plots of the 40 individuals. (c) Population structure of samples from 40 individuals examined via the program ADMIXTURE. K is the number of presumed genetic clusters, and K = 2 is the best. (d) The frequency of ROH in each breed. (e) Decay of black-bone and non-black-bone chickens measured by r2. The horizontal coordinate is the physical distance (Kb) and the vertical coordinate is the LD r2, which takes a value in the range of 0 to 1, with higher values indicating a higher degree of linkage disequilibrium between loci

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Analysis of demographic history

The historical effective population size (Ne) was estimated based on SNPs in the whole genome of LNC and other Chinese chicken breeds using MSMC2, a successor to the MSMC. The results indicated highly similar patterns of population trajectories among the seven breeds (Fig. 3). The population sizes of the seven chicken breeds appeared to increase slowly at 10 Mya, reaching a peak at about 1 Mya, showing a slow decline up to the Last Interglacial (LIG; 130 − 115 kya), followed by a sharp decline. The sudden decrease in population size may be linked to extreme temperature and aridity disaster that occurred during the Last Glacial Maximum (LGM; 20-26.5 kya) [18, 19].

Fig. 3
figure 3

Demographic history of the chicken population. Demographic history of the 35 individuals with MSMC2 analysis based on SNP distribution. Generation time (g) = 1 year and neutral mutation rate per generation (µ) = 1.91 × 10− 9

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Genome-wide selective sweep signals and functional analysis

To enhance the detection of genome-wide selection signatures linked to melanin deposition, we divided populations into black-bone and non-black-bone groups based on previous classification. Fst value and θπ Ratios were calculated for SNP variant loci and displayed as Manhattan plots (Fig. 4b and c). Moreover, the top 5% of the Fst value (≥ 0.066) and the top 5% of the θπ ratios (< -0.071 or > 0.366) were utilized as criteria for classifying selective sweeps (Fig. 4a). Within these sweeps, gene annotation identified 540 genes with the highest Fst value in LOC101750371 and LOC121108313 located on the 68.24–68.58 Mb interval of chromosome Z (chrZ) (Supplementary Table S6). Besides, numerous genes associated with pigmentation were detected in the top 200 regions, including ATP synthase, H + transporting, mitochondrial F1 complex, epsilon subunit (ATP5E), Endothelin3 (EDN3), engulfment and cell motility 2 (ELMO2), GNAS complex locus (GNAS), tubulin beta 1 class VI (TUBB1), methylthioadenosine phosphorylase (MTAP), cyclin dependent kinase inhibitor 2 A (CDKN2A), cyclin dependent kinase inhibitor 2B (CDKN2B), tripartite motif containing 36 (TRIM36), neuregulin 1 (NRG1), cathepsin V (CTSV), FER tyrosine kinase (FER). Furthermore, the θπ ratio in the black-bone group (θπ = 0.00383) was lower than the non-black-bone group (θπ = 0.00395), suggesting that a sweep has been positively selected in the black-bone group (Supplementary Table S7). Genes undergoing selection between black-bone and non-black-bone chickens were subjected to GO functional enrichment analysis. The top 30 of the analysis revealed 10 GO terms enriched in biological processes, 10 terms in cellular components, and 10 terms in molecular functions (Supplementary Fig. S4). In addition, 28 GO terms (P < 0.05) associated with melanin deposition were filtered out (Table 1). A total of 24 genes were enriched for these terms, including G protein subunit alpha q (GNAQ), KIT ligand (KITLG), melanin-concentrating hormone receptor 1 (MCHR1), and tyrosinase (TYR) known to play a crucial role in the regulation of melanin deposition.

Fig. 4
figure 4

Identification of genomic regions in Luning chickens with positive selection scans. (a) The horizontal coordinates represent the log2θπ ratio (θπ non-black-bone / θπ black-bone) and the vertical coordinates represent the Fst values, which correspond to the top and right density distribution. Regions selected for the non-black-bone are shown using blue points, while black-bone are shown in red. (b) Population differentiation distribution map of Fst. The dashed line indicates the Fst top5% threshold, the horizontal coordinate indicates the chromosome and the vertical coordinate indicates the Fst value. (c) Analysis of θπ between non-black-bone group and black-bone group of chicken

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Table 1 GO terms associated with melanin deposition in GO enrichment analysis of WGRS (P < 0.05)
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RNA-seq data

To compare skin color differences, we individually determined the L-values of the dorsal skin of four male Luning chickens. The L-values of black skin (BS) averaged around 46, while the L-value of white skin (WS) was 64, showing a highly significant difference (Supplementary Table S8). To understand the genetic influence on melanin deposition in Luning chickens, we performed a differential expression analysis of mRNAs, lncRNAs, and pre-miRNAs in the dorsal skin tissues of WS and BS chickens. The results revealed significant differences in expression levels with 2045 mRNAs (1140 upregulated and 905 downregulated), 2762 lncRNAs (1995 upregulated and 767 downregulated), and 110 pre-miRNAs (22 upregulated and 88 downregulated) in WS and BS tissues (Fig. 5a, b and c). Moreover, 1506 target genes were identified from 2762 DE lncRNAs, 66 of which were involved in both cis-acting and trans-acting. Furthermore, 110 DE pre-miRNAs targeted a total of 7767 target genes. GO analysis of the DE mRNAs and DE lncRNAs target genes revealed 11 and 12 enriched GO terms related to pigmentation, respectively, while no pigmentation related terms were found in the enrichment results for DE pre-miRNAs target genes (Tables 2 and 3).

Fig. 5
figure 5

Differentially expressed RNAs of black and white skin Luning chickens. Volcano plot of differentially expressed mRNAs (a), lncRNAs (b), and pre-miRNAs (c) in WS and BS. Red points represent upregulation, gray points represent insignificant differences, and blue points represent downregulation in volcano plots. Differentially expressed RNAs with Padj values too small to be presented in the volcano plots were plotted uniformly on -Log10 (Padj) = 300. (d) Integration analysis of WGRS and RNA-seq. Venn diagram of strongly selected genes in WGRS and differentially expressed genes (DE mRNAs, DE lncRNAs target genes, and DE pre-miRNAs target genes) in RNA-seq

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Table 2 GO terms associated with melanin deposition in GO enrichment analysis of DE mRNAs (P < 0.05)
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Table 3 GO terms associated with melanin deposition in GO enrichment analysis of DE lncRNAs (P < 0.05)
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Candidate genes for melanin deposition

Based on the above results, we performed a combined analysis of WGRS and RNA-seq to identified candidate genes influencing melanin deposition in chickens. In the WGRS analysis, we compared black-bone chickens and non-black-bone chickens to detect candidate genes associated with melanin deposition. A total of 412 genes out of 540 genes in the selected region were strongly selected (Padj < 0.05). These selected genes overlapped 50 genes with DE mRNAs, 37 DE lncRNAs target genes, and 199 DE pre-miRNAs target genes in the RNA-seq, respectively. Notably, 29 genes were found to be co-occurring in these four datasets, includes candidate genes from the previous WGRS analysis: ATP5E, EDN3, and LOC101750371 (Fig. 5d). Particularly, LOC101750371 was most strongly selected in the selective sweep. Consequently, ATP5E, EDN3, and LOC101750371 were selected as potential candidate genes influencing melanin deposition in chickens.

Validation of RNA-seq results by qRT-PCR

To validate the expression levels of DE mRNAs, DE lncRNAs, and DE pre-miRNAs observed in RNA-seq, we performed qRT-PCR on 5 randomly selected transcripts from each group. The results demonstrated that the expression trends from both qRT-PCR and RNA-seq were consistent, validating the reliability and accuracy of the RNA-seq results (Fig. 6).

Fig. 6
figure 6

Verification of the RNA-seq via qRT-PCR. The expression levels of five DE mRNAs (a), five DE lncRNAs (b), and five DE pre-miRNAs (c) were validated with qRT-PCR in the dorsal skin of Luning chickens. WS represents the white skin, and BS represents the black skin. *P < 0.05, **P < 0.01

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Cloning and analysis of the Luning chicken LOC101750371 gene

Successful cloning of the CDS sequence of Luning chicken LOC101750371 gene (2055 bp) revealed that the gene is predicted to encode 684 amino acids (Supplementary Fig. S5). Bioinformatics analysis of the LOC101750371 protein was conducted and shown in Supplementary Fig. S6.

The mRNA expression of LOC101750371 was detected in 8 tissues (heart, liver, spleen, lung, kidney, pectoral muscle, leg muscle, and dorsal skin) of black-skin Luning chickens using qRT-PCR (Fig. 7). The results indicated that LOC101750371 was expressed in all tissues, with significantly higher expression in the dorsal skin compared to the other 7 tissues, followed by the pectoral muscle and leg muscle. To further explore the molecular characteristics of LOC101750371 gene, qRT-PCR was utilized to compare its expression in all 8 tissues of white-skin Luning chickens and black-skin Luning chickens (Fig. 8). The findings revealed that the expression difference in the pectoral muscle, leg muscle, dorsal skin and kidney tissues between white-skin and black-skin Luning chickens was significant.

Fig. 7
figure 7

Relative expression of LOC101750371 gene in different tissues of black skin Luning chicken. Different lowercase letters represent significant difference (P < 0.05)

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Fig. 8
figure 8

Comparative expression of LOC101750371 gene in different tissues of Luning chickens with white skin and black skin. WS represents the white skin, and BS represents the black skin. *P < 0.05, **P < 0.01

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Discussion

The Luning chicken, a unique breed identified in the 1980s and listed in the National Breed List of Livestock and Poultry Genetic Resources, has evolved into an excellent local breed for meat and eggs through artificial and natural selection. However, there has been little research on their phenotypic traits and genetic improvement. In this research, we sequenced the genomes of 5 Luning chickens to explore their population structure, analyze genetic differences, and examine their population history in comparison to other local chicken breeds.

Genetic evolutionary analysis using SNP sites of eight breeds revealed that geographically proximate chickens tended to cluster together, consistent with previous studies [20, 21]. Population structure analysis through PCA, NJ tree, and Admixture analysis indicated diverse affinities among JBC breeds, possibly due to the different domestication venues or multiple genetic origins [22, 23]. PCA and NJ tree analyses showed consistent results, with all varieties clustering independently except for JBC. Admixture analysis was consistent with the results of cluster analysis, with SC being the first to be distinguished from the other varieties. The ROH results indicated that LNC had the highest long ROH values, possibly due to active conservation efforts in recent years, which may explain the low genetic diversity of LNC [17, 24]. LD decay analysis indicated no significant difference in attenuation rates among black-bone chickens and non-black-bone chickens, suggesting similar selection intensities. MSMC2 analysis revealed that the effective population of seven chicken breeds is influenced by environmental changes, and the correlation with environmental conditions indicated that the effective population size is related to the breeds´ adaptation to the environment [25].

In recent years, WGRS technology has been extensively used in biological studies to detect variations in genomes, aiming to identify potential functional genes associated with traits and reveal the genetic mechanisms of natural and artificial selection in livestock and poultry. Currently, numerous candidate genes related to traits have been identified using genome-wide variation information, such as duck beak color [26], sheep litter size [27], and goose wing length [28], among others. Chicken skin color is an economically important trait, with consumers favoring black-bone chickens due to their suggested nutritional and medicinal value [29]. There is not insufficient scientific evidence to suggest that sex directly contributes to differences in melanin deposition. Our review of the literature revealed two reports with contrasting results: one study reported higher expression levels of DCT, TYRP1, and TYR (enzymes regulating enzymes involved in melanin synthesis) genes in male chickens compared to female chickens [30], and another study reported lower expression levels of TYRP1 gene in male black-bone chickens than in females [31]. Thus at present, more investigation is needed to explore the role of sex in determining skin color. In order to avoid the error caused by sex, we selected male Luning chicken samples for both WGRS and RNA-seq. Several studies have shown that chromosome 20 (chr20) and chrZ play vital roles in skin color pigmentation. Li et al. [32] conducted a genome-wide association study, refining the dermal melanin inhibitor gene (Id) location and identifying 3 significant SNPs located at 78.5–79.2 Mb on chrZ associated with dermal calf pigmentation in Tibetan chickens. Hou et al. [33] employed a combination of genome-wide association study and Fst scan to identify 68 SNPs on chr20, mapping to 10 genes associated with dermal hyperpigmentation. Moreover, another study SNPs significantly linked to chicken skin color pigmentation in a ~ 2.94 Mb region on chrZ and ~ 3.58 Mb region on chr20 [34]. A recent study exploring the mechanisms controlling eggshell and shank pigmentation in Italian local chickens by genome-wide association analysis [35]: revealed that concerning shank color, a SNP with an exceptionally low P value was identified in the 31 Mb region of chrZ, and another SNP from the same genomic region was also located in NFIB (near the TYRP1 gene), a region known to be associated with dark skin color and shank [30, 36]. This study utilized the intersection of the top 5% Fst and θπ ratio to determine the genome region under selection and analyzed the screened candidate genes for enrichment. The results aligned with previous findings, indicating that SNPs significantly associated with skin color were mainly concentrated in chr20 and chrZ. A number of genes that have been reported to play important regulatory functions in melanin synthesis and deposition were identified on these regions: ATP5E, EDN3, ELMO2, GNAS, TUBB1, MTAP, CDKN2A, CDKN2B, TRIM36, NRG1, CTSV, and FER. Additionally, the previously unreported LOC101750371 and LOC121108313 were identified in the 68.24–68.58 Mb interval where chrZ is most strongly selected. These genes, along with genes enriched for GO terms associated with pigmentation such as GNAQ, KITLG, MCHR1, and TYR, were considered candidate genes for WGRS screens (Fig. 4b).

Transcriptome sequencing has a wide range of applications in exploring poultry characterization mechanisms, disease research, and nutrition [37,38,39]. To better investigate the mechanism of pigmentation, we performed RNA-seq of a phenotypically mutated white skin Luning chicken along with three other black skin Luning chickens. We identified 2045 DE mRNAs, 2763 DE lncRNAs, and 110 DE pre-miRNAs between WS group and BS group. By predicting the target genes of lncRNAs, it is possible to gain insights into how lncRNAs affect cellular functions by regulating the expression of these genes, which helps to reveal the roles and mechanisms of action of lncRNAs in the regulatory network of gene expression [40]. miRNAs affect post-transcriptional regulation of mRNAs mainly through interactions with the target genes, causing degradation or transcriptional repression of the mRNAs [41]. This study was to further screen the key genes affecting skin pigmentation by predicting the target genes of DE lncRNAs and DE pre-miRNAs. Then, we predicted the target genes of DE lncRNAs and DE pre-miRNAs, and the target genes and DE mRNAs were analyzed by GO enrichment analysis, and identified 12 genes enriched in pigmentation associated GO terms. These includes genes that have been verified to be involved in regulating coat color and skin color: agouti signaling protein (ASIP), TYR, tyrosinase related protein 1 (TYRP1), premelanosome protein (PMEL), melan-A (MLANA), solute carrier family 45 member 2 (SLC45A2), and KIT proto-oncogene, receptor tyrosine kinase (KIT) [42,43,44].

This study combines WGRS and RNA-seq to uncover the genetic factors associated with the coloring process in chickens and screen for candidate genes affecting skin color traits in black-bone chickens. 50 DE mRNAs, 37 DE lncRNAs target genes, and 199 DE pre-miRNAs target genes were found to overlap with WGRS strongly selected genes. Additionally, 29 genes were present in all four data sets (Fig. 5d). Three of these genes, ATP5E, EDN3, and LOC101750371, were also part of the previously identified WGRS candidates. Li et al. [9] integration of whole-genome and transcriptome data identified SLC45A2, SLMO2, ATP5E and EDN3 as candidate genes for melanin deposition. Endothelin (EDN) is a family of active peptides produced by endothelial cells, including three endothelin peptides: EDN1, EDN2 and EDN3. EDN3 plays a role in promoting the proliferation and differentiation of early neural crest cell precursors that generate melanocytes [45]. Previous studies have suggested that EDN3 is a candidate gene for pigmentation in various animals such as chickens [34], ducks [46], and mice [47]. Additionally, the uncharacterized LOC101750371 in candidate genes is not only a differentially expressed RNA in RNA-seq but also highly selected in WGRS. However, further investigation is needed to determine its regulatory role in pigmentation.

This study initially explored the structure of the protein, but the function of the gene requires further experimental verification. By detecting the expression of LOC101750371 in different tissues of black-skin Luning chickens, it was found that its expression in the dorsal skin was significantly higher than that in the other 7 tissues, followed by the pectoral and leg muscles. This result is consistent with the fact that Luning chicken is an black-bone chicken with black beak, skin and meat, and provides theoretical basis for the role of LOC101750371 in melanin deposition. We also found that the expression of LOC101750371 in the pectoral muscle, leg muscle and dorsal skin tissues of black-skin Luning chickens was highly significantly higher than that of white-skin Luning chicken. This result also further demonstrated the existence of a regulatory effect of LOC101750371 on the mechanism of melanin deposition in Luning chickens.

Conclusion

In summary, we performed WGRS on five individual Luning chickens to evaluate genomic diversity, evolutionary history, and signal selection in various local breeds. Our findings indicated that the 68.24–68.58 Mb genomic region on chrZ was significantly associated with pigmentation traits in black-bone chicken. Through integrated genomic and transcriptomic analysis, we identified ATP5E, EDN3 and LOC101750371 as potential influencers of pigmentation in chickens. And preliminary evidence for the existence of a regulatory effect of LOC101750371 on the mechanism of melanin deposition in Luning chickens. This study has the potential to accelerate the breeding process of Luning chickens, and the identified candidate genes can contribute to understanding the molecular mechanism of pigmentation.

Methods

Sample collection and resequencing

In this experiment, all the Luning chicken samples in this experiment were provided by Sichuan Luning chicken genetic resources breeding farm. Whole blood samples were collected from five 94-day-old male black-bone chickens in Luning chickens. Approximately 2.0 ml of blood was collected from the inferior wing vein and stored in a cryovial with EDTA, then promptly placed in a liquid nitrogen tank. Genomic DNA was isolated using TIANamp Genomic DNA Kit. Non-contaminated DNA samples (Supplementary Table S9 & Supplementary Fig. S7) were used for library construction using the TruSeq DNA LT Sample Prep kit. The DNA fragments underwent end repaired, A-tailed, ligated, purification, PCR amplification, and other steps to finalize the library construction. Sequencing was performed using the DNBSEQ-T7 sequencer to generate raw data. During the experiment, it was observed that one male Luning chicken (150-day-old) exhibited a phenotypic mutation to white color. Three black-bone Luning chickens of the same age were selected to assess the dorsal skin luminance values (L) of the four individuals. Heart, liver, spleen, lung, kidney, pectoral muscle, leg muscle and dorsal skin tissues were collected for RNA extraction, and dorsal skin tissue for RNA-seq; cDNA synthesis using PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time). All four individuals were euthanized and samples were collected in a sterile environment. The pentobarbital was used for euthanization by intraperitoneal injection at dose of 40 mg/kg of chicken body weight [48].

Including the data from current research, we also obtained sequence data from the NCBI database for 35 individuals from other chicken breeds (Supplementary Table S10) [8, 9, 21, 49, 50]. Thus, there were 40 chickens in total (5 individuals per breed), including Luning chickens (LNC), Tibetan chickens (TBC), Pengxian yellow chickens (PYC), Jiuyuan black-bone chickens (JBC), and Muchuan black-bone chickens (MBC) all from Sichuan, Xichuan black-bone chickens (XBC) from Henan, Silkie chickens (SC) from Jiangsu, and Red junglefowl (RJF) from Yunnan.

Quality control processing and variant calling

Quality filtering of sequencing reads was performed using the default parameters of fastp [51] software, quality filtering standards: remove adaptors, trim reads with bases ≥ 5 (non AGCT), trim nucleotides having an average base correct recognize rate less than 99% within a window of 4 nucleotides. Lastly, reads with lengths less than 75 bp or average quality score less than 15 were removed. Clean reads were aligned to the reference genome (https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/016/699/485/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b_genomic.fna.gz) using BWA [52], after converting the comparison result format using SAMtools [53], Picard (https://broadinstitute.github.io/picard/) was used to remove redundancy, and the results were compared and analyzed using Qualimap [54]. Single nucleotide polymorphisms (SNPs) and insertion/deletion sequences (InDels) were identified using GATK [55]. Gene annotation of SNPs and InDels deletion results was performed using Annovar [56]. The parameters of GATK for SNPs were set as follows: QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0, and the parameters for InDels were set as follows: QD < 2.0 || MQ < 40.0 || FS > 200.0 || SOR > 10.0 || ReadPosRankSum < -20.0. The detected SNPs and InDels of LNCs are integrated into Circos map for visual presentation of genomic data. Cluster analysis has proceeded via the R package (ComplexHeatmap) [57].

Population structure

First, genome-wide SNPs were filtered by PLINK (set the parameters to: --indep-pairwise 50 5 0.5) and non-closely linked SNPs were selected for population structure analysis [58]. The IBS matrix was calculated using PLINK based on filtered genome-wide SNPs sites, and then a phylogenetic tree was constructed for all samples using neighbor-joining (NJ) in PHYLIP [59]. We made the principal component analysis (PCA) via the software EIGENSOFT [60]. The Admixture analysis was performed using ADMIXTURE [61] program according to K = 2 to K = 5. 10 different seeds were selected for 10 replicate analyses, and then pong [62] was used to cluster the 10 results, and representative results were selected for plotting. ADMIXTURE suggests determining the optimal K based on Cross-Validation error.

Runs of homozygosity and linkage disequilibrium analysis

The runs of homozygosity (ROH) were identified by the runs of the homozygosity tool in detectRUNS [63] to count individual-level and group-level ROH in the window-free case. Dividing the seven breeds of chickens into black-bone chickens (LNC, XBC, MBC, SC) and non-black-bone chickens (TBC, PYC, RJF) for linkage disequilibrium (LD) analysis. We conducted the correlation coefficient r2 with the software PopLDdecay [64].

Demographic history

To detect changes in the effective ancestral population sizes of the LNC, we first extracted all the typing sites for 28 individuals (samples sequenced to a depth greater than 20× and with relatively pure ancestral components) of seven populations from the BAM file and used the SNPable pipline (https://lh3lh3.users.sourceforge.net/snpable.shtml) to create a total breeds mask file. Moreover, we used shapeit4 [65] to dichotomize all samples, integrating all phased and unphased sites in the samples, and used the Multiple Sequentially Markovian Coalescent 2 (MSMC2) tool [66] to do a population dynamic history analysis with a generation time (g) of 1 year and a mutation rate (µ) of 1.91 × 10− 9 per generation [67].

Selective sweep analysis

To ascertain the candidate regions for melanin deposition after targeted selection in the LNC, the Fst and θπ ratios between black-bone chickens (LNC, XBC, MBC, SC) and non-black-bone chickens (TBC, PYC, RJF) were calculated by VCFtools [57]. We calculated the average Fst value and θπ ratios in 200 kb sliding windows with a 20 kb sliding step. The top 5% of the Fst value and θπ ratios were selected as the selected region. Finally, Gene Ontology (GO) functional annotation of the candidate genes in the selected region, followed by the corresponding enrichment analysis.

RNA-seq and data analysis

To better screen for melanin-related genes, dorsal skin from 4 chickens were constructed in specific libraries and performed RNA-seq. RNA-seq was performed using the Illumina sequencing platform and raw data were stored in fastq format. The data were compared to the chicken reference genome (GRCg7b) using HISAT2 software (http://ccb.jhu.edu/software/hisat2/index.shtml) [68] after quality control by fastp. The software StringTie (http://ccb.jhu.edu/software/stringtie/) [69] was used to assemble and splice the mapped reads and functionally annotate of potential new transcripts.

White skin Luning chicken (WS) was used as the control group and black skin Luning chickens (BS) were used as the treatment group, and the expression levels of the genes and transcripts were quantitatively analyzed using RSEM software, respectively. Then we calculated TPM (Transcripts Per Million reads) according to the gene length and read numbers. Differentially expressed mRNAs (DE mRNAs), differentially expressed lncRNAs (DE lncRNAs), and differentially expressed pre-miRNAs (DE pre-miRNAs) were detected with DEGseq using a threshold of Padj < 0.001 and |log2FC| ≥ 2. DE lncRNAs target gene prediction was performed, and neighboring protein-coding genes within 10 kb upstream and downstream of DE lncRNAs were identified as cis-acting target genes. On the other hand, DE lncRNAs and DE mRNAs correlation coefficients (r) were calculated, and DE lncRNAs with |r|>0.8 and Padj < 0.05 were identified as trans-acting target genes. Prediction of target genes for DE pre-miRNAs using the software miRanda [70] (https://github.com/hacktrackgnulinux/miranda), the parameters were set as follows: -sc 160 -en -20 -strict.

Validation of DE RNAs by qRT-PCR

To ensure the accuracy of RNA-seq, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to determine the expression level of randomly selected mRNAs, lncRNAs, and pre-miRNAs (5 each). Total RNA was reverse transcribed according to the reverse transcription kit (PrimeScript™ RT reagent Kit with gDNA Eraser) to obtain cDNA. The qRT-PCR primers were designed by Primer Premier 5 and are shown in Supplementary Table S11. The qRT-PCR 20-µL reaction consisted of 10 µL 2×Taq Pro Universal SYBR qPCR Master Mix, 0.4 µL of each primer, 2 µL of cDNA, 7.2 µL of ddH2O. The amplification conditions consisted of 95 ℃ for 30 s, followed by 30 cycles of 95 ℃ for 10 s, and 60 ℃ for 30 s. A melting curve procedure consisted of 95 ℃ for 15 s, 60 ℃ for 1 min, and 95 ℃ for 15 s. Three technical replicates were set for each sample, and the relative expression was calculated by the 2−∆∆Ct method using GAPDH (used for mRNA and lncRNA) and U6 (used for pre-miRNAs) as the internal reference gene. The results obtained are expressed using mean ± standard error. P < 0.05 were defined as significant and P < 0.01 were highly significant.

Cloning and analysis of the Luning chicken LOC101750371 gene

The dorsal skin tissues of the black-skin Luning chicken were used as a template to clone the LOC101750371 gene, and the amino acid sequence was predicted using DNAMAN software. SOPMA was used to predict the secondary structure of LOC101750371 protein, SWISS-MODEL was used to predict the tertiary structure, ExPASy ProtScale was used to predict the hydrophilicity of the protein, SignalP-4.1 was used to predict the protein phosphorylation site, NetNGlyc-1.0 was used to predict the protein N-glycosylation site, the YinOYang-1.2 was used to predict protein O-glycosylation sites.

Using GAPDH as an internal reference gene, qRT-PCR was used to detect the relative expression of LOC101750371 gene in heart, liver, spleen, lung, kidney, pectoral muscle, leg muscle and dorsal skin tissues of black-skin Luning chickens, and to compare the relative expression of LOC101750371 gene in different tissues of white-skin and black-skin Luning chickens. The qRT-PCR reaction system and reaction conditions were consistent with those of “Validation of DE RNAs by qRT-PCR”. Three technical replicates were set for each sample, and the relative expression was calculated by the 2−∆∆Ct method. The primers for quantitative detection are shown in Supplementary Table S11.

Data availability

The raw data for the 5 Luning chickens used in the whole-genome resequencing, and for the 4 Luning chickens used in the transcriptome are accessible at NCBI under BioProject accession numbers PRJNA954999 and PRJNA1059763, respectively. The other 35 chickens used in this study were downloaded from NCBI with BioProject accession numbers PRJNA642410, PRJNA241474, PRJNA800119, PRJNA597842, and PRJNA306389.

Abbreviations

WGRS:

Whole-genome resequencing

RNA-seq:

Transcriptome sequencing

LNC:

Luning chicken

TBC:

Tibetan chicken

PYC:

Pengxian yellow chicken

XBC:

Xichuan black-bone chicken

JBC:

Jiuyuan black-bone chicken

MBC:

Muchuan black-bone chicken

SC:

Silkie chicken

RJF:

Red junglefowl

SNPs:

Single nucleotide polymorphisms

InDels:

Insertion/deletion sequences

NJ:

Neighbor-joining

PCA:

Principal component analysis

ROH:

Runs of homozygosity

LD:

Linkage disequilibrium

L:

Luminance

BS:

Black skin

WS:

White skin

DE mRNAs:

Differentially expressed mRNAs

DE lncRNAs:

Differentially expressed lncRNAs

DE pre-miRNAs:

Differentially expressed pre-miRNAs

GO:

Gene Ontology

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Acknowledgements

Not applicable.

Funding

This work was supported by the Science and Technology Support Program of Sichuan Province (2021YFYZ0031 and 2023YFSY0048), the Natural Science Foundation of Sichuan Province (2023NSFSC1142), and the Fundamental Research Funds for the Central Universities of Southwest Minzu University (ZYN2023095).

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Authors and Affiliations

  1. Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization (Southwest Minzu University), Ministry of Education, Chengdu, China

    Chuwen Chen, Jie Li, Zhiyi Li, Yi Nong, Jiayan Wang, Zi Wang & Zhixiong Li

  2. Key Laboratory of Animal Science of National Ethnic Affairs Commission of China, Southwest Minzu University, Chengdu, China

    Chuwen Chen, Jie Li, Zhiyi Li, Yi Nong, Jiayan Wang, Zi Wang & Zhixiong Li

  3. College of Animal & Veterinary Sciences, Southwest Minzu University, Chengdu, China

    Chuwen Chen, Jie Li, Zhiyi Li, Yi Nong, Jiayan Wang, Zi Wang & Zhixiong Li

Authors
  1. Chuwen Chen
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  2. Jie Li
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  3. Zhiyi Li
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  4. Yi Nong
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  6. Zi Wang
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  7. Zhixiong Li
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Contributions

CWC and ZXL conceived and designed the research. CWC performed the experiments. JL, ZYL, YN, JYW, and ZW collected the samples and analyzed the data. CWC wrote the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Zhixiong Li.

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Ethics approval and consent to participate

All animal studies were approved by the Institutional Animal Care and Use Committee of Southwest Minzu University (Certification No. 2022MDLS44). The authors declare that all animal experiments were carried out in compliance with the ARRIVE guidelines for the reporting of animal experiments. The study involved only farm owned chickens, and informed consent was obtained from the chicken farm for their participation in this study.

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The authors declare no competing interests.

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Chen, C., Li, J., Li, Z. et al. Whole-genome resequencing reveals melanin deposition candidate genes of Luning chicken. BMC Genomics 25, 858 (2024). https://doi.org/10.1186/s12864-024-10774-5

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Keywords

BMC Genomics

ISSN: 1471-2164

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