Hair follicles are the basis of the formation of Hu sheep pattern. This study was to employ whole transcriptome sequencing to screen differentially expressed long non-coding RNAs (lncRNAs) between three wave patterns in lambskin. In this study, three groups of 2-day-old Hu sheep were selected from full-sib individuals that included small, medium, and large waves, and hair follicle tissues were collected from dorsal side of Hu sheep. LncRNA and mRNA expression profiles were analyzed by whole transcriptome sequencing technology. 33, 31, and 41 differentially expressed lncRNAs were selected between large and medium, medium and small, and large and small, respectively. 458, 481, and 498 differentially expressed mRNAs were found between large and medium, medium and small, and large and small, respectively, by RNA-seq analysis. qRT-PCR results of 16 randomly selected lncRNAs and mRNAs were similar to the sequencing results. Correlation analysis of lncRNA and mRNA expression showed that, several lncRNAs may be enriched for hair follicle such as Wnt, mTOR, Notch signaling pathways. Our results aid in excavation of mRNAs and lncRNAs in hair follicle, and providing a basis for future study on pattern formation mechanisms.
Hu sheep is famous for its white lambskin in the word, and the lambskin from 2-day-old Hu lambs is called soft gem in China. The quality of lambskin is affected by many factors, especially the type of pattern. Wave patterns in lambskin have three types, including small waves, medium waves, and large waves. The quality of small waves is excellent, while large wave is poor. The type of wave pattern was determined by fineness, density, and curvature of the hair follicles1,2,3,4. Therefore, wool is the basic element to form different wave pattern, and hair follicles are determined to hair growth. Hair follicle growth and development is regulated by various types of cell, and hair follicle cyclical development is affected by several molecules and regulatory pathways5,6, such as Wnt7, TGF-β8,9, MAPK10, and Notch11 are generally involved in the periodic growth and morphogenesis of hair follicles.
The long non-coding RNA(lncRNA) is a non-translated RNA longer than 200 bp. Although, litter is known regarding how hair follicle development is regulated by lncRNA. Yue et al.12 identified 15 significant differentially lncRNAs, and he found that XLOC005698 might be involved in hair follicle development, which could be compete for oar-miR-3955-5p. Si et al.13 found that LncRNA PlncRNA-1could regulate proliferation and differentiation of hair follicle stem cells through TGF-β1-mediated Wnt/β-catenin signal pathway. Furthermore, Zhu et al.14 successfully constructed the regulatory network of lncRNA-H19 in secondary hair follicle of Liaoning cashmere goat. In addition, they found that lncRNA-H19 has the highest level of expression at the anagen phase, which speculates that lncRNA-H19 is closely related to hair follicle growth and development of Liaoning cashmere goat.
However, there are currently very few reports on lncRNA in hair follicle growth and development, and no relevant lncRNA studies on wave pattern of lambskin. Therefore, the molecular mechanisms of wave patterns formation are unknown. Whole transcriptome sequencing was used To study the molecular mechanism of waves pattern formation in Hu sheep and screen the differentially expressed lncRNAs between three wave patterns, which participated in the hair follicle cell biological process of growth and apoptosis, proliferation and differentiation. This study can provide basis for the mechanism of hair follicle growth and development between different wave pattern.
Whole transcriptome sequencing results
Firstly, we extracted the total RNA from small waves, medium waves, and large waves, and then constructed the cDNA libraries for sequencing using the Illumina sequencer. Three pairs of full-sib individuals were divided to three groups, including Group 1(L1, M1, S1), Group 2(L2, M2,S2), and Group 3(L3, M3, S3). From the several cDNA libraries, the number of clean reads in Group 1(L1, M1, S1) were 96,961,910, 97,159,054, and 97,402,430 (Table 1), with GC contents of 52.00%, 52.50% and 52.00% (Fig. 1A), respectively. The number of clean reads in Group 2(L2, M2,S2) were 115,775,290, 97,345,832, and 96,934,824, with GC contents of 52.00%, 51.50% and 51.50%, respectively. The number of clean reads in Group 3(L3, M3, S3) were 98,295,984, 97,460,938, and 97,228,364, with GC contents of 51.50%, 52.00% and 52.50%, respectively. In addition, the valid reads in the clean reads were mapped to the Ovis aries genome.
In order to Identify the transcripts in sheep hair follicle, non-coding RNA candidates from the unknown transcripts were categorized using four coding potential analysis methods, namely, CPC15, CNCI16, Pfam17, and PLEK18 (Fig. 1B). After mapping the reference sequence, we identified 2,057 lncRNAs from transcripts. The length of the lncRNAs was mainly distributed within the range of 200 bp-5,000 bp, and the average length was 2,109.99 bp (Fig. 1C). Additionally, the lncRNA types mainly include intergenic lncRNAs (character u) and intronic lncRNAs (character i) (Fig. 1D), containing 2 to 3 exons (Fig. 1E).
Profiling and verification of differentially expressed lncRNA and mRNA of different wave patterns
The expression levels of lncRNA and mRNA transcripts were estimated using the FPKM values (Reads Per Kilobase per Million mapped reads). We found that the expression level of the lncRNAs transcripts was relatively lower than those of the mRNA (Fig. 2A,B). Based on adjusted P-value threshold of <0.001 and |log2(fold change)| > 1, a total of 21, 16, and 29 upregulated and 12, 15, and 12 downregulated lncRNA, and 222, 239, and 236 upregulated and 236, 242, and 262 downregulated mRNA between large and medium waves, small and medium waves, and large and small waves, respectively (Fig. 3A,B).
To further verify the reliability of whole transcriptome sequencing results, 7 differentially expressed lncRNAs and mRNAs were randomly selected for RT-PCR and their relative expression levels in the three wave patterns were confirmed. Compared with RNA-seq data, the expression trends were the same (Fig. 4), thus indicating that our RNA-seq data was reliable.
GO and pathway analysis
After obtaining differentially expressed mRNA, we performed gene ontology (GO) enrichment analysis on these mRNAs and described its function19. GO analysis showed that significant enrichment of 658, 704, and 682 GO items with these mRNAs were in medium and large waves, small and medium waves, and large and small waves, respectively. Between large and medium waves, a total of 87, 136, and 435 GO were enriched in cellular component(CC), molecular function(MF), and biological processes(BP), respectively. Between small and medium waves, 70, 129, and 505, GO were enriched in CC, MF, and BP, respectively. Between large and small waves, 74, 152, and 456 GO were enriched in CC, MF, and BP, respectively Fig. 5 showed part of GO items.
GO function analysis of DE lncRNA showed that significant enrichment of 68, 73, and 119 GO items with these mRNAs were in small and medium waves, large and small waves and medium and large waves, respectively. Between large and medium waves, a total of 26, 29, and 63 GO were enriched in cellular component(CC), molecular function(MF), and biological processes(BP), respectively. Between small and medium waves, 18, 12, and 26, GO were enriched in CC, MF, and BP, respectively. Between large and small waves, 25, 13, and 34 GO were enriched in CC, MF, and BP, respectively. Figure 6 showed part of GO items.
Comparison between the DE mRNAs with the KEGG Pathway database showed that 287, 290, and 294 pathways in medium and large waves, small and medium waves, and large and small waves, respectively20. We found Wnt, Hippo, TGF-β, and Notch signaling pathway, which were related to hair follicle growth and development. In addition, some differentially expressed mRNAs were enriched in these pathways. Figure 7 showed part of KEGG items.
Comparison between the DE lncRNAs with the KEGG Pathway database indicated that a total of 4, 15, 3 KEGG pathways in medium and large waves, small and medium waves, and large and small waves, respectively (Table 2).
Co-expression analysis of differentially expressed lncRNA-mRNA
In general, lncRNAs play a part of transcriptional or post-transcriptional regulation by silencing or suppressing some genes. In order to preferentially filter lncRNAs that regulate hair follicle development, correlation analysis of lncRNAs and mRNAs was done. Between large and medium waves, we found 25 differentially expressed lncRNAs were associated with 377 differentially expressed mRNAs. Between medium and small waves, we found 29 differentially expressed lncRNAs were associated with 435 differentially expressed mRNAs. Between large and small waves, we found 27 differentially expressed lncRNAs were associated with 333 differentially expressed mRNAs (Supplementary Table S1). The network pattern suggested that there were not one-to-one correspondence between lncRNAs and mRNAs. We chose some mRNAs that enriched in the pathways related to hair follicleand and co-expression lncRNAs to verify their expression. From the Fig. 8, XR_001436817.1 and PHLPP2, XR_001433856.1 and CUL1, and XR_001434203.1 and MAP2K4 have the same expression trend. In contrast, XR_001433856.1 and PRKACB, XR_001041211.2 and PRKACB have opposite trend. Then, the expression of XR_001435723.1 in small waves higher than large waves, and PPP3CC has no changes. Therefore, we speculate that XR_001435723.1 and PPP3CC may not have relationship, and others may have some correlations. These results indicated that some lncRNAs and mRNAs might be have a close relationship, and they were screened from different patterns. So they could remaine the topics for future investigations.
Although there were many studies on the morphological changes of hair follicles, the specific molecular mechanisms were poorly understood. Nowadays, studies on the hair follicles mainly focused on human, mouse, and other models. More importantly, the molecular mechanisms of different types of hair follicle development are not exactly the same. For example, mouse acicular substrate formation is dependent on the Eda-A1/Edar/NF-kB pathway21, but conic hair substrate formation is not required for Eda -A1/Edar/NF-kB pathway but the Noggin/Lef-1 pathway22. Therefore, based on the molecular mechanism of hair follicle development in human, mouse, and other models, it is necessary to deeply study the molecular mechanism of hair follicle morphogenesis in Hu sheep due to the economic value of its lambskin. In order to screened differentially expressed mRNAs between three different patterns and investigate the possible role of lncRNA in hair follicle development., we collected hair follicle tissue from 9 two-day-old Hu lambs included three pairs of full-sib individuals with small, medium, large waves for high-throughput sequencing.
Due to the rapid development of transcriptome analysis, lncRNAs have received widespread attention over the past several years as novel regulators of cell development23. At present, the research on LncRNA mainly focuses on cancer in human24,25,26. The research on lncRNA mainly focuses on muscle growth, reproductive function, and so on in animal27. In the NONCODE database, there are only a few lncRNAs in sheep (antiPeg11, MEG3, MEG9, Rian, Xist)28,29. In our study, a total of 33, 31, 41 significant differentially expressed lncRNAs between medium and large waves, medium and small waves, and small and large medium. Although many lncRNAs have been screened in hair follicles by high-throughput sequencing, whether they are related to hair follicle development requires further analysis. At the same time, a total of 458, 481, 498 significant differentially expressed mRNAs between medium and large waves, medium and small waves, and small and large medium. In order to verify the RNA-seq results, we selected 7 lncRNA and mRNA for qRT-PCR, and the results were consistent with RNA-seq.
To further understand the function of differential expression mRNAs, GO and KEGG database were used to annotate their function. GO analysis indicated that some mRNAs such as RBPJ were enriched with regulation of hair cycle (GO:0042634), hair follicle development (GO:0001942), and hair follicle maturation (GO:0048820). Moreover, KEGG Pathway analysis showed that PHLPP2, CUL1, MAP2K4, PPP3CC enriched in Wnt(path:oas04310), Hippo(path:oas04390), MAPK(path:oas04010), mTOR(path:oas04150), Notch(path:oas04330) signaling pathways, and these mRNAs may be potentially involved in hair follicle growth and development process. Interestingly, we found that RPBJ found that it is involved in the biological process of hair follicle development, and RPBJ is in the Notch signaling pathway, which is one of the classic hair follicle development pathways. However, the mechanisms of these genes regulation in hair follicle growth and development are still unclear. The two PHLPP, PHLPP1 and PHLPP2, have emerged as critical regulators of cellular homeostasis, and were identified in a search for phosphatases that dephosphorylate Akt, and thus suppress growth factor signaling30. CUL1 is a scaffold protein of the ubiquitin E3 ligase Skp1/Cullin1/Rbx1/F-box protein complex, which increased in renal cell carcinoma and promotes cancer cell proliferation, migration, and invasion31. MAP2K4 is a negative regulator of the TGF-β1 signaling associated with atrial remodeling and arrhythmogenesis32. So we speculate whether MAP2K4 could regulate hair follicle growth and development by regulating the TGF-β1 pathway. PPP3CC decrease is responsible for activation of NF-κB and contributes to invasion and growth in glioma cells33. Subsequently, further researches will focus on the relationship between these genes and hair follicle growth and development.
Next, co-expression analysis of differentially expressed lncRNA-mRNA was performed to predict the correlation of lncRNAs and mRNAs by calculating the Pearson correlation coefficients. Analyzing the expression level of some lncRNA-mRNA, we found that XR_001436817.1 and PHLPP2, XR_001433856.1 and CUL1, and XR_001434203.1 and MAP2K4 have the same expression trend. These mRNAs were enriched in some pathways related to hair follicle growth and development, so we speculated that these co-expression lncRNAs might have some relationship with hair follicle development.
In conclusion, we screened significant differentially expressed lncRNAs and mRNAs in hair follicles of different waves of Hu sheep lambskin. Further research on these lncRNAs can provide a useful basis for the hair follicle development of lambskin, and our research may provide a certain basis to research the mechanism of the pattern formation in Hu sheep.
Animal research proposals were approved by the Chinese Ministry of Agriculture (License Number: 39) and the Jiangsu Provincial Government Animal Care and Use Committee (IACUC) (License Number: 45). All experimental procedures were carried out in strict accordance with the guidelines for the care and use of laboratory animals in Jiangsu Province and the recommendations of the Animal Protection and Use Committee of the Ministry of Agriculture of China. All efforts are made to minimize the suffering of animals.
The experiment Hu sheep were selected from the Suzhou Stud Farm in Jiangsu Province, China. A total of 9 two-day-old healthy lambs showing approximately similar weight included three pairs of full-sib individuals with small, medium, large waves. 1 cm of hair root was cut off and placed in the freezing tube with Drikold until about 1/3 volume was collected from the dorsal side of the Hu sheep.
RNA library construction
Total RNA was isolated for whole transcriptome sequencing,. The constructed RNA library was qualified by Agilent 2100 Bioanalyzer. A NanoDrop 2000 Ultra Microscope were utilized in determining the quality control of the extracted total RNAs. Ribosomal RNA was removed using a Ribo-Zero (TM) kit (Epicenter, Madison, WI, USA). Short fragments (approximately 200 bp in length) were obtained and used as templates for first-stand cDNA synthesis. Second-strand cDNA synthesis was performed using a buffer, dNTPs, RNase H, and DNA polymerase I. After the PCR amplification and purification using the Qubit® dsDNA HS Assay Kit, the cDNA library was constructed using an NEBNext® Ultra™ RNA Library Preparation Kit. The cDNA libraries were sequenced on the Illumina HiSeq 2500 platform in Shanghai OE Biomedical Technology Co (sequencing read length: 150 bp).
The raw data was filtered to eliminate low-quality reads. Clean reads mapped to the reference genome (Ovis aries v4.0) were selected for de novo assembly. Coding RNA and non-coding RNA candidates from the unknown transcripts were categorized using four coding potential analysis methods, namely, CPC15, CNCI16, Pfam17, and PLEK18. The minimum length and the number of exons were set as thresholds, thereby filtering putative encoded RNAs, and transcripts containing two exons and longer than 200 nt were selected as candidate lncRNAs. Different types of lncRNAs were classified by cuffcompare, including intergenic lncRNAs (character u), intronic lncRNAs (character i), anti-sense lncRNAs (character x), and sense-overlapping lncRNAs (character o). After that, we used a reference transcript as a library and software bowtie234 and eXpress35 to determine the abundance of each transcript in each sample by means of sequence similarity alignment. Then, we used FPKM method (Fragments Per kb Per Million Reads) to calculate mRNA and lncRNA expression. The number of counts of each lncRNA was normalized by DESeq. In addition, the difference multiple was calculated, and differential significance test was analyzed by NB (negative binomial distribution test) to screen differentially expressed lncRNAs.
GO and KEGG pathway analyses
Functional annotation was performed using GO enrichment analysis and KEGG pathway analysis. We performed functional annotation on differentially expressed transcripts to cellular component(CC), molecular function(MF), and biological processes(BP) by the gene ontology database (http://geneontology.org/). Enrichment analysis employed counting the number of transcripts in each GO term was counted, followed by Fisher’s exact test to assess statistical significance (p < 0.05). KEGG (http://www.genome.jp/kegg/) is the main public database used in pathway analysis, which was followed by Fisher’s exact test to assess statistical significance (p < 0.05).
Co-expression analysis of differentially expressed lncRNA-mRNA
Based on the expression values of mRNAs and lncRNAs, the correlation of lncRNAs were predicted by calculating the Pearson correlation coefficients and P values of the lncRNA-mRNA. The |correlation| ≥ 0.7 and P ≤ 0.05 were considered to be relevant36. We selected these lncRNA-mRNAs for follow-up studies.
Sequencing results verification
We randomly selected 7 differentially expressed lncRNAs and mRNAs with GAPDH as a reference genes. In order to verify the reliability of the sequencing results, the SYBR Green I method was used. Relevant information on primers used in RT-PCR is shown in Table 3. Each sample was tested 3 times using a Fast 7500 PCR instrument to establish standard curve. To calculate the relative expression of the target genes, we used the following method37: Relative expression = 2−ΔΔCt, ΔΔC t = (C, target gene—C, housekeeping gene) large waves − (Ct, target gene—C, housekeeping gene)small waves. SPSS16.0 and one-way ANOVA were used to compute for relative expression and analysis of significance.
Raw sequencing data used as part of my study should be deposited in SRA. SRA Accession: PRJNA531734.
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This work was performed by National Natural Science Foundation of China(31872333), The Projects of Domesticated Animals Platform of the Ministry of Science, Key Research and Development Plan (modern agriculture) in Jiangsu Province (BE2018354), Major new varieties of agricultural projects in Jiangsu Province (PZCZ201739), Jiangsu Agricultural Science and Technology Innovation Fund (CX (18)2003), The Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Major projects of Natural Science Research of colleges and universities in Jiangsu Province (17KJA230001), The Project of six peak of talents of Jiangsu Province of China and Postgraduate Innovation Project of Yangzhou University (XKYCX17_060, SJCX18_0804).