Effects of nonsynonymous single nucleotide polymorphisms of the KIAA1217, SNTA1 and LTBP1 genes on the growth traits of Ujumqin sheep.

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep. In this study, high-resolution resequencing data from four sheep breeds (Dorper sheep, Suffolk sheep, Ouessant sheep, and Shetland sheep) were analyzed. The nonsynonymous single nucleotide polymorphisms of three candidate genes (KIAA1217, SNTA1, and LTBP1) were also genotyped in 642 healthy Ujumqin sheep using MALDI-TOFMS and the genotyping results were associated with growth traits. The results showed that different genotypes of the KIAA1217 g.24429511T>C locus had significant effects on the chest circumferences of Ujumqin sheep. The SNTA1 g.62222626C>A locus had different effects on the chest depths, shoulder widths and rump widths of Ujumqin sheep. This study showed that these two sites can be used for marker-assisted selection, which will be beneficial for future precision molecular breeding.

Identification of the key proteins associated with different hair types in sheep and goats.

Animal-derived fiber has the characteristics of being light, soft, strong, elastic and a good thermal insulator, and it is widely used in many industries and traditional products, so it plays an important role in the economy of some countries. Variations in phenotypes of wool fibers among different species and breeds are important for industry. We found that the mean fiber diameter of cashmere was significantly smaller than that of sheep wool (p < 0.01), and sheep wool was significantly smaller than goat wool (p < 0.01). Compared with traditional proteomics technology, we analyzed cashmere, guard hair, and wool by Laber-free proteomics technology and detected 159, 204, and 70 proteins, respectively. Through the sequential windowed acquisition of all theoretical fragmentations (SWATH), 41 and 54 differentially expressed proteins were successfully detected in the cashmere vs. wool group and guard hair vs. wool group. Protein‒protein interaction network analysis of differentially expressed proteins revealed many strong interactions related to KRT85, KRTAP15-1 and KRTAP3-1. The final analysis showed that the proportion of KRT85, KRTAP15-1 and KRTAP3-1 might be the key to the difference in fiber diameter and could be used as a potential molecular marker for distinguishing different fiber types.

Identification of key pathways and genes that regulate cashmere development in cashmere goats mediated by exogenous melatonin.

The growth of secondary hair follicles in cashmere goats follows a seasonal cycle. Melatonin can regulate the cycle of cashmere growth. In this study, melatonin was implanted into live cashmere goats. After skin samples were collected, transcriptome sequencing and histological section observation were performed, and weighted gene co-expression network analysis (WGCNA) was used to identify key genes and establish an interaction network. A total of 14 co-expression modules were defined by WGCNA, and combined with previous analysis results, it was found that the blue module was related to the cycle of cashmere growth after melatonin implantation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the first initiation of exogenous melatonin-mediated cashmere development was related mainly to the signaling pathway regulating stem cell pluripotency and to the Hippo, TGF-beta and MAPK signaling pathways. Via combined differential gene expression analyses, 6 hub genes were identified: PDGFRA, WNT5A, PPP2R1A, BMPR2, BMPR1A, and SMAD1. This study provides a foundation for further research on the mechanism by which melatonin regulates cashmere growth.

Identification of body size characteristic points based on the Mask R-CNN and correlation with body weight in Ujumqin sheep.

The measurements of body size data not only reflect the physical fitness, carcass structure, excellent growth condition, and developmental relationship among tissues and organs of animals but are also critical indicators to measure the growth and development of sheep. Computer vision-based body size identification is a non-contact and stress-free method. In this study, we analyzed different body size traits (height at wither, body slanting length, chest depth, chest circumference, shank circumference, hip height, shoulder width, and rump width) and the body weight of 332 Ujumqin sheep and significant correlations (P < 0.05) were obtained among all traits in Ujumqin sheep. Except for shoulder width, rump width, and shank circumference, all were positively correlated, and the effect of sex on Ujumqin sheep was highly significant. The main body size indexes affecting the body weight of rams and ewes were obtained through stepwise regression analysis of body size on body weight, in order of chest circumference, body slanting length, rump width, hip height, height at wither, and shoulder width for rams and body slanting length, chest circumference, rump width, hip height, height at wither and shoulder width for ewes. The body slanting length, chest circumference, and hip height of ewes were used to construct prediction equations for the body weight of Ujumqin sheep of different sexes. The model's prediction accuracy was 83.9% for the rams and 79.4% for ewes. Combined with a Mask R-CNN and machine vision methods, recognition models of important body size parameters of Ujumqin sheep were constructed. The prediction errors of body slanting length, height at wither, hip height, and chest circumference were ~5%, chest depth error was 9.63%, and shoulder width, rump width, and shank circumference errors were 14.95, 12.05, and 19.71%, respectively. The results show that the proposed method is effective and has great potential in precision management.