Xing Guo

Spurs, which mainly appear in roosters, are protrusions near the tarsometatarsus on both sides of the calves of chickens, and are connected to the tarsometatarsus by a bony core. As a male-biased morphological characteristic, the diameter and length of spurs vary significantly between different individuals, mainly related to genetics and age. As a specific behavior of hens, egg-laying also varies greatly between individuals in terms of traits such as age at first egg (AFE), egg weight (EW), and so on. At present, there are few studies on chicken spurs. In this study, we investigated the inheritance pattern of the spur trait in roosters with different phenotypes and the correlations between spur length, body weight at 18 weeks of age (BW18), shank length at 18 weeks of age (SL18), and the egg-laying trait in hens (both hens and roosters were from the same population and were grouped according to their family). These traits related to egg production included AFE, body weight at first egg (BWA), and first egg weight (FEW). We estimated genetic parameters based on pedigree and phenotype data, and used variance analysis to calculate broad-sense heritability for correcting the parameter estimation results. The results showed that the heritability of male left and right spurs ranged from 0.6 to 0.7. There were significant positive correlations between left and right spur length, BW18, SL18, and BWA, as well as between left and right spur length and AFE. We selected 35 males with the longest spurs and 35 males with the shortest spurs in the population, and pooled them into two sets to obtain the pooled genome sequencing data. After genome-wide association and genome divergency analysis by F_ST, allele frequency differences (AFDs), and XPEHH methods, we identified 7 overlapping genes (CENPE, FAT1, FAM149A, MANBA, NFKB1, SORBS2, UBE2D3) and 14 peak genes (SAMD12, TSPAN5, ENSGALG00000050071, ENSGALG00000053133, ENSGALG00000050348, CNTN5, TRPC6, ENSGALG00000047655,TMSB4X, LIX1, CKB, NEBL, PRTFDC1, MLLT10) related to left and right spur length through genome-wide selection signature analysis and a genome-wide association approach. Our results identified candidate genes associated with chicken spurs, which helps to understand the genetic mechanism of this trait and carry out subsequent research around it.

Inosine monophosphate (IMP) is a substance that enhances flavor and plays a crucial role in the umami taste of chicken muscle. It is also an influential factor in determining chicken’s economic value. However, the molecular regulatory network underlying the IMP content in muscle remains unclear. To address this issue, we performed transcriptome sequencing on 20 pectoralis major muscle samples from 120-day-old Guangde feathered-leg chicken and used weighted gene co-expression network analysis (WGCNA) to identify key regulatory factors that influence IMP content. The weighted gene co-expression network was constructed using a total of 16,344 genes, leading to the identification of 20 co-expression gene modules. Among the modules that were identified, it was observed that the purple module (R = −0.51, p = 0.02) showed a significant negative correlation with the IMP content. This suggests that the genes within the purple module had the ability to regulate the IMP content. A total of 68 hub genes were identified in the purple module through gene significance (GS) > 0.2 and module membership (MM) > 0.8. The STRING database was used for a protein–protein interaction (PPI) network of hub genes. Furthermore, troponin I type 1 (TNNI1), myozenin 2 (MYOZ2), myosin light chain 2 regulatory cardiac slow (MYL2), and myosin light chain 3 regulatory cardiac slow (MYL3) involved in the “ATP-dependent activity”, “cAMP signaling pathway” and “cGMP-PKG signaling pathway” were identified as central regulators that contribute to IMP content. These results offer valuable information into the gene expression and regulation that affects IMP content in muscle.

Feather follicle density plays an important role in appealing to consumers’ first impressions when making purchasing decisions. However, the molecular network that contributes to this trait remains largely unknown. The aim of this study was to perform transcriptome and weighted gene co-expression network analyses to determine the candidate genes relating to feather follicle density in Wannan male chickens. In total, five hundred one-day-old Wannan male chickens were kept in a conventional cage system. Feather follicle density was recorded for each bird at 12 weeks of age. At 12 weeks, fifteen skin tissue samples were selected for weighted gene co-expression network analysis, of which six skin tissue samples (three birds in the H group and three birds in the L group) were selected for transcriptome analysis. The results showed that, in total, 95 DEGs were identified, and 56 genes were upregulated and 39 genes were downregulated in the high-feather-follicle-density group when compared with the low-feather-follicle-density group. Thirteen co-expression gene modules were identified. The red module was highly significantly negatively correlated with feather follicle density (p < 0.01), with a significant negative correlation coefficient of −0.72. In total, 103 hub genes from the red module were screened. Upon comparing the 103 hub genes with differentially expressed genes (DEGs), it was observed that 13 genes were common to both sets, including MELK, GTSE1, CDK1, HMMR, and CENPE. From the red module, FOXM1, GTSE1, MELK, CDK1, ECT2, and NEK2 were selected as the most important genes. These genes were enriched in the DNA binding pathway, the heterocyclic compound binding pathway, the cell cycle pathway, and the oocyte meiosis pathway. This study suggests that FOXM1, GTSE1, MELK, CDK1, ECT2, and NEK2 may be involved in regulating the development of feather follicle density in Wannan male chickens. The results of this study reveal the genetic structure and molecular regulatory network of feather follicle density in Wannan male chickens, and provide a basis for further elucidating the genetic regulatory mechanism and identifying molecular markers with breeding value.

Meat color is one of the most important economic traits in chickens. However, the gene network and regulatory mechanisms contributing to meat color traits in chickens remain largely unknown. In the present study, we performed weighted gene co-expression network analysis (WGCNA) based on RNA-Seq datasets of 16 pectoralis major muscle samples from two yellow-feather chicken breeds to identify the modules and hub genes related to meat color in chickens. A total of 18,821 genes were used to construct the weighted gene co-expression network, and 29 co-expression gene modules were identified. Among these modules, five modules including blue, brown, steel blue, paleturquoise and orange modules were found to be significantly correlated with meat color traits. Furthermore, several genes within the association module involved in the regulation of mitochondrial activity (e.g., ATP5L, UQCR10 and COX7C) and lipid oxidation (e.g., CAV3, RBP4A and APOH) were identified as hub genes that may play a crucial role in the regulation of meat color. These results provide valuable information to improve our understanding of gene expression and regulation in relation to meat color traits and contribute to future molecular breeding for improving meat color in chickens.