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Additional file 3: Table S2. List of primer used in this study.

Additional file 6: Fig. S3. Go and KEGG network diagram during the hair follicle morphogenesis. (a-f) represents the comparison group of G1-G6. Circle is represented gene, square is represented KEGG; triangle is represented GO term.

Additional file 3: Figure S1. The number of proteins identified at various molecular weight ranges.

Additional file 8: Figure S6. Venn plot indicated the overlapped annotated proteins of GO, KEGG, and KOG database.

Additional file 5: Figure S3. The number of peptide fragments for protein identification.

Additional file 2: Table S1. Bacterial strains, plasmids and cells used in this study.

Additional file 7: Figure S5. The KEGG annotation of all expressed proteins.

Additional file 4: Figure S2. The distribution of protein���s sequence coverage.

Additional file 7: Fig. S4. K-means clustering analysis of differentially expressed mRNAs among the six comparison groups.

Additional file 10: Figure S7. The top 10 TF families detected in all expressed proteins.

Additional file 1: Table S1. RNA-Seq quality control result. Table S2. Reference genome alignment read statistics. Table S3. Primer information. Table S4. Sequence information of FA2H gene interference fragments. Fig. S1 Comparison of FPKM density distribution of each sample. Different colors in the figure represent different samples, the abscissa represents the logarithm of the FPKM of the corresponding sample, and the ordinate represents the probability density. Fig. S2 FPKM boxplot of each sample. The abscissa in the figure represents different samples; the ordinate represents the logarithm of the FPKM expression of the samples. Fig. S3 Untrimmed original image of Figure 7B. A. Anti-beta-Actin Polyclonal Antibody; B. FAAH1 Polyclonal Antibody. Different lanes 1-5 are represented as Blank, si-NC, si-444, si-594, and si-771 respectively. Fig. S4 Untrimmed original image of Figure 7D. A. Anti-beta-Actin Polyclonal Antibody; B. FAAH1 Polyclonal Antibody. Different lanes 1-5 are Blank, si-NC, si-594, FA2H-NC, and FA2H respectively.

Additional file 2: Fig. S1. Heatmap of DEGs during the hair follicle morphogenesis. The x-axis represents the sample number, where S1 to S3 is G1; S4 to S6 is G2; S7 to S9 is G3; S10 to S12 is G4; S13 to S15 is G5; S16 to S18 is G6.

BackgroundCharacterization of the molecular mechanisms underlying hair follicle development is of paramount importance in the genetic improvement of wool-related traits in sheep and skin-related traits in humans. The Merino is the most important breed of fine-wooled sheep in the world. In this study, we systematically investigated the complexity of sheep hair follicle development by integrating transcriptome and methylome datasets from Merino sheep skin.ResultsWe analysed 72 sequence datasets, including DNA methylome and the whole transcriptome of four gene types, i.e. protein-coding genes (PCGs), lncRNAs, circRNAs, and miRNAs, across four embryonic days (E65, E85, E105, and E135) and two postnatal days (P7 and P30) from the skin tissue of 18 Merino sheep. We revealed distinct expression profiles of these four gene types across six hair follicle developmental stages, and demonstrated their complex interactions with DNA methylation. PCGs with stage-specific expression or regulated by stage-specific lncRNAs, circRNAs, and miRNAs were significantly enriched in epithelial differentiation and hair follicle morphogenesis. Regulatory network and gene co-expression analyses identified key transcripts controlling hair follicle development. We further predicted transcriptional factors (e.g. KLF4, LEF1, HOXC13, RBPJ, VDR, RARA, and STAT3) with stage-specific involvement in hair follicle morphogenesis. Through integrating these stage-specific genomic features with results from genome-wide association studies (GWAS) of five wool-related traits in 7135 Merino sheep, we detected developmental stages and genes that were relevant with wool-related traits in sheep. For instance, genes that were specifically upregulated at E105 were significantly associated with most of wool-related traits. A phenome-wide association study (PheWAS) demonstrated that candidate genes of wool-related traits (e.g.SPHK1,GHR,PPP1R27,CSRP2,EEF1A2, andPTPN1) in sheep were also significantly associated with dermatological, metabolic, and immune traits in humans.ConclusionsOur study provides novel insights into the molecular basis of hair follicle morphogenesis and will serve as a foundation to improve breeding for wool traits in sheep. It also indicates the importance of studying gene expression in the normal development of organs in understanding the genetic architecture of economically important traits in livestock. The datasets generated here are useful resources for functionally annotating the sheep genome, and for elucidating early skin development in mammals, including humans.

Background Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW). Results Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from − 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans. Conclusions Our findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.

Granular cell tumors (GCTs) are rare soft-tissue neoplasms. GCT immunohistochemistry is positive for S-100, NSE, and CD68. This report describes the case of a 10-year-old male who presented with a dorsal nodule. A biopsy revealed aggregates and sheets of large epithelioid and spindle cells. The cells had abundant eosinophilic granular cytoplasm. Immunohistochemical analysis was positive for CD68, NKI/C3, and synaptophysin; weakly positive for NSE; and negative for S-100, SOX10, HMB45, Melan A, cytokeratin, SMA, EMA, and CD163. The Ki-67 index was less than 1%. A diagnosis of an S-100 negative, cutaneous, benign GCT was determined.

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992&ndash, 2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

Objective The purpose of this study was to investigate the genetic effects of six keratin (KRT) genes on the wool traits of 418 Chinese Merino (Xinjiang type) (CMXT) individuals. Methods To explore the effects and association of six KRT genes on sheep wool traits, The polymerase chain reaction-based single-strand conformation polymorphism (PCR-SSCP), DNA sequencing, and the gene pyramiding effect methods were used. Results We report 20 mutation sites (single-nucleotide polymorphisms) within the six KRT genes, in which twelve induced silent mutations; five induced missense mutations and resulted in Ile→Thr, Glu→Asp, Gly→Ala, Ala→Ser, Se→His; two were nonsense mutations and one was a same-sense mutation. Association analysis showed that two genotypes of the KRT31 gene were significantly associated with fiber diameter (p