Microevolutionary mechanism of high-altitude adaptation in Tibetan chicken populations from an elevation gradient.

As an indigenous breed, the Tibetan chicken is found in highland regions and shows physiological adaptations to high altitude; however, the genetic changes that determine these adaptations remain elusive. We assumed that the microevolution of the Tibetan chicken occurred from lowland to highland regions with a continuous elevation range. In this study, we analyzed the genome of 188 chickens from lowland areas to the high-altitude regions of the Tibetan plateau with four altitudinal levels. Phylogenetic analysis revealed that Tibetan chickens are significantly different from other altitude chicken populations. Reconstruction of the demographic history showed that the migration and admixture events of the Tibetan chicken occurred at different times. The genome of the Tibetan chicken was also used to analyze positive selection pressure that is associated with high-altitude adaptation, revealing the well-known candidate gene that participates in oxygen binding (HBAD), as well as other novel potential genes (e.g., HRG and ANK2) that are related to blood coagulation and cardiovascular efficiency. Our study provides novel insights regarding the evolutionary history and microevolution mechanisms of the high-altitude adaptation in the Tibetan chicken.

Dynamic Expression and Regulatory Network of Circular RNA for Abdominal Preadipocytes Differentiation in Chicken (Gallus gallus).

Circular RNA (circRNA), as a novel endogenous biomolecule, has been emergingly demonstrated to play crucial roles in mammalian lipid metabolism and obesity. However, little is known about their genome-wide identification, expression profile, and function in chicken adipogenesis. In present study, the adipogenic differentiation of chicken abdominal preadipocyte was successfully induced, and the regulatory functional circRNAs in chicken adipogenesis were identified from abdominal adipocytes at different differentiation stages using Ribo-Zero RNA-seq. A total of 1,068 circRNA candidates were identified and mostly derived from exons. Of these, 111 differentially expressed circRNAs (DE-circRNAs) were detected, characterized by stage-specific expression, and enriched in several lipid-related pathways, such as Hippo signaling pathway, mTOR signaling pathway. Through weighted gene co-expression network analyses (WGCNA) and K-means clustering analyses, two DE-circRNAs, Z:35565770|35568133 and Z:54674624|54755962, were identified as candidate regulatory circRNAs in chicken adipogenic differentiation. Z:35565770|35568133 might compete splicing with its parental gene, ABHD17B, owing to its strictly negative co-expression. We also constructed competing endogenous RNA (ceRNA) network based on DE-circRNA, DE-miRNA, DE-mRNAs, revealing that Z:54674624|54755962 might function as a ceRNA to regulate chicken adipogenic differentiation through the gga-miR-1635-AHR2/IRF1/MGAT3/ABCA1/AADAC and/or the novel_miR_232-STAT5A axis. Translation activity analysis showed that Z:35565770|35568133 and Z:54674624|54755962 have no protein-coding potential. These findings provide valuable evidence for a better understanding of the specific functions and molecular mechanisms of circRNAs underlying avian adipogenesis.

Candidate gene screening for lipid deposition using combined transcriptomic and proteomic data from Nanyang black pigs.

BACKGROUND: Lower selection intensities in indigenous breeds of Chinese pig have resulted in obvious genetic and phenotypic divergence. One such breed, the Nanyang black pig, is renowned for its high lipid deposition and high genetic divergence, making it an ideal model in which to investigate lipid position trait mechanisms in pigs. An understanding of lipid deposition in pigs might improve pig meat traits in future breeding and promote the selection progress of pigs through modern molecular breeding techniques. Here, transcriptome and tandem mass tag-based quantitative proteome (TMT)-based proteome analyses were carried out using longissimus dorsi (LD) tissues from individual Nanyang black pigs that showed high levels of genetic variation. RESULTS: A large population of Nanyang black pigs was phenotyped using multi-production trait indexes, and six pigs were selected and divided into relatively high and low lipid deposition groups. The combined transcriptomic and proteomic data identified 15 candidate genes that determine lipid deposition genetic divergence. Among them, FASN, CAT, and SLC25A20 were the main causal candidate genes. The other genes could be divided into lipid deposition-related genes (BDH2, FASN, CAT, DHCR24, ACACA, GK, SQLE, ACSL4, and SCD), PPARA-centered fat metabolism regulatory factors (PPARA, UCP3), transcription or translation regulators (SLC25A20, PDK4, CEBPA), as well as integrin, structural proteins, and signal transduction-related genes (EGFR). CONCLUSIONS: This multi-omics data set has provided a valuable resource for future analysis of lipid deposition traits, which might improve pig meat traits in future breeding and promote the selection progress in pigs, especially in Nanyang black pigs.

Polymorphisms of AMY1A gene and their association with growth, carcass traits and feed intake efficiency in chickens.

Investigations on the association between chicken traits and genetic variations can provide basic information to improve production performance in chickens. In our previous work, we genotyped 450 male chickens with a 600 K SNP array [1] and found that several SNPs in the genomic regions of the amylase alpha 1A (AMY1A) gene were significantly associated with feed intake efficiency and carcass traits. Given the lower accuracy of the SNP array, we performed direct sequencing with male and female chickens to further test chicken AMY1A polymorphisms and investigate their association with 17 traits in chickens. The results showed that 7 SNPs in the 5' flanking region, exon, intron and 3' UTR (3' untranslated region) of AMY1A, were significantly associated with daily gain (DG), average daily feed intake (ADFI), leg muscle weight (LMW) and abdominal fat (AF) (p < 0.05). Additionally, the haplotypes based on three SNPs, rs15910189, rs314354067 and rs316026696, showed significant associations with DG (p < 0.01), ADFI and AF (p < 0.05). To better understand the transcriptional regulation of AMY1A, we cloned its 5' flanking region and found that the SNPs rs316436216 and rs314213090 which might change the transcriptional regulator binding sites, were in the suppressor and enhancer regions, respectively. In addition, luciferase assays revealed that the SNP rs314613110 in the 3' UTR influenced the binding of the miRNA gga-miR-1764-3p. To validate whether there is any copy number variation in AMY1A in our population, we performed a genome-wide assessment of CNVs through whole-genome resequencing data. However, no CNV was found in AMY1A in our population, which is different from the increased copy number of AMY1A found in humans who consume a high-starch diet. Therefore, the present study provides substantial evidence for the association of AMY1A polymorphisms with growth traits and feed intake efficiency, which might contribute to chicken breeding programs.

Transcriptome analysis of feather follicles reveals candidate genes and pathways associated with pheomelanin pigmentation in chickens.

Yellow plumage is common in chickens, especially in breeds such as the Huiyang Bearded chicken, which is indigenous to China. We evaluated plumage colour distribution in F1, F2, and F3 populations of an Huiyang Bearded chicken × White Leghorn chicken cross, the heredity of the yellow plumage trait was distinguished from that of the gold plumage and other known plumage colours. Microscopic analysis of the feather follicles indicated that pheomelanin particles were formed in yellow but not in white feathers. To screen genes related to formation of the pheomelanin particles, we generated transcriptome data from yellow and white feather follicles from 7- and 11-week-old F3 chickens using RNA-seq. We identified 27 differentially expressed genes (DEGs) when comparing the yellow and white feather follicles. These DEGs were enriched in the Gene Ontology classes 'melanosome' and 'melanosome organization' related to the pigmentation process. Down-regulation of TYRP1, DCT, PMEL, MLANA, and HPGDS, verified using quantitative reverse transcription PCR, may lead to reduced eumelanin and increased pheomelanin synthesis in yellow plumage. Owing to the presence of the Dominant white locus, both white and yellow plumage lack eumelanin, and white feathers showed no pigments. Our results provide an understanding of yellow plumage formation in chickens.