Comprehensive Profiling of Circular RNAs in Goat Dermal Papilla Cells and Prediction of Their Modulatory Roles in Hair Growth

Abstract

Circular RNAs (circRNAs) are capable of finely modulating gene expression at transcriptional and post-transcriptional levels; however, their characters in dermal papilla cells (DPCs)—the signaling center of hair follicle—are still obscure. Herein, we established a comprehensive atlas of circRNAs in DPCs and their skin counterparts—dermal fibroblasts (DFs)—from cashmere goats. In terms of the results, a sum of 3706 circRNAs were bioinformatically identified. Subsequent analysis suggested that the detected transcripts exhibited several prominent genomic features, including exons as their main sources. Compared with DFs, 76 circRNAs significantly displayed higher abundances in goat DPCs, with 45 transcripts markedly exhibiting adverse trends (p < 0.05). Furthermore, potential roles and underlying molecular mechanisms of circRNAs in goat DPCs were speculated through constructing their possible regulatory networks with mRNAs and microRNAs (miRNAs). We found that the circRNAs may serve as miRNA sponges to alleviate three hair growth-related functional genes (HOXC8, RSPO1, and CCBE1) of DPCs from miRNAs-imposed post-transcriptional modulation, further facilitating two critical processes (HOXC8 and RSPO1: hair follicle stem cell activation; CCBE1: follicular angiogenesis) closely involved in hair growth. In addition, we also speculated that two intron-derived circRNAs (chi_circ_0005569 and chi_circ_0005570) possibly affect the expression of their host gene CCBE1 at a transcriptional level in the nucleus. The above results demonstrated that circRNAs are abundantly expressed in goat DPCs, and certain circRNAs are potential participators in hair growth via the effects on the levels of related functional genes. Our study offers a preliminary clue for researchers hoping to untangle the roles of non-coding RNAs in hair growth.

1. Introduction

Dermal papilla cells (DPCs) are a group of specialized fibroblasts located at the base of the hair follicle (HF), the mini-organ responsible for the continuous production of mammalian hair in the skin [1]. Previous studies have validated that the DPCs are the signaling center of the HF and determine the growth of hair via modulating several key biological processes in hair growth [2,3]. Meanwhile, a few reports have demonstrated that such a unique capacity of DPCs is decided by the intrinsic expressions of signature genes in the cells. For example, the initial step of hair growth—activation of hair follicle stem cells (HFSCs)—is under the genetic control of Hoxc8 and RSPO1 [4,5], whose overexpression results in precious HF development and hair overgrowth. In addition, follicular angiogenesis, a critical event closely related to active hair growth, is stimulated by several potent angiogenic factors (e.g., VEGF) highly expressed in DPCs [6,7,8]. Although these studies highlighted the functional importance of the related genes in hair biology, how their expressions are precisely regulated remains as yet unknown.

In recent years, a class of enclosed non-coding RNAs called circular RNAs (circRNAs) gradually emerged as important participators in a wide array of biological processes, including organogenesis [9], pathogenesis [10], and carcinogenesis [11]. At the same time, extensive studies have found that circRNAs exert a regulatory character on the expressions of the protein-coding genes at transcriptional and post-transcriptional levels, through a series of unique molecular mechanisms [12,13]. Functioning as competing endogenous RNAs (ceRNAs) to bind the microRNAs (miRNAs) and modulate the activity of the cognate miRNAs and their target mRNAs is one of the widely accepted approaches. Therefore, the utilization of the ceRNAs theory to deduce the functionality of circRNAs, and the subsequent experimental verification of the proposed hypothesis are the common methods in the livestock field of study. In fiber-producing animals, such as goats and sheep, several studies have shown that the differentially expressed circRNAs in skin tissues are closely related to HF formation [14,15], and key fiber traits (e.g., fineness and quality) [16,17]. Acting as ceRNAs to finely regulate the levels of mRNAs through the circRNAs–miRNA–mRNAs axis seems to be the molecular basis of circRNAs in the above processes. Apart from validating the relationships, some of the researchers have also explored the roles of circRNAs hair biology, using in vitro cellular models. In goats, the promotive effect of circRNA-1926 on directing the committed differentiation of HFSCs towards the follicular cells via titrating miR-148a/b-3p to alleviate their inhibitory roles on the target gene, CDK19, was observed [18]. In addition to acting as ceRNAs, some of the circRNAs have been demonstrated as adjusting the gene expression via modulating the gene transcription in the nucleus [19,20], or competing with mRNAs for transcript alternative splicing in the cytosol [21]. Although mounting evidence suggests that the circRNAs should be unneglected players in modulating the gene expression and functionality of DPCs, their information currently remains scare.

In the present study, we established a genome-wide profile of circRNAs expressed in goat DPCs and screened the functional circRNAs via comparing the transcriptomes between goat DPCs and DFs. We also reported that circRNAs might affect the expression of the signature genes of DPCs at transcriptional and post-transcriptional levels, highlighting the possible characteristics of circRNAs in key events (i.e., HFSCs’ activation and angiogenesis) in hair biology. Our study shines new light on a deeper exploration of the roles of non-coding RNAs in hair biology.

2. Materials and Methods

2.1. Animals and Cell Culture

Three healthy female Shanbei white cashmere goats (~2 years old, ~35 kg weight) with independent genetic lineage background were selected from a private farm located in Yangling District, Shannxi, China (34°28′ N and 108°07′ E). The rearing and management of the animals were performed under the recommended guidelines provided by the regional standard (DB61/T 584-2013). The skin samples harvested from the lateral backsides on cashmere goats were used for the cell lines’ acquisition. The primary culture of the dermal papilla cells (DPCs) was obtained using a canonical microdissection-based method [22]. At the same time, an explants-based protocol was adopted to acquire goat dermal fibroblasts (DFs) from the skin samples [23]. All of the cells were maintained in a sterile incubator at 37 °C temperature, 100% humidity, and 5% CO₂/95% air atmosphere. A conventional DMEM/F12, with the addition of 10% FBS (v/v), 100 UI/mL penicillin, and 100 μg/mL streptomycin, was chosen as the culture medium. At the fourth passage, the cell samples from three lines of DPCs and DFs were collected and subjected to downstream analysis. All of the reagents used in the present study were purchased from Sigma-Aldrich (Shanghai, China). The entire experimental procedure was approved and supervised by the Animal Care Commission of Northwest A and F University under the forced guideline (2013-31101684).

2.2. Sequencing Library Construction and Reference Genome Mapping

The total RNA extraction was implemented according to the classic Trizol-based RNA extraction method. The RNA degradation and contamination were monitored on 1% agarose gels, and their purity was checked using the NanoPhotometer^® spectrophotometer (IMP LEN, CA, USA). Furthermore, the RNA concentration was measured using a Qubit ^® RNA Assay Kit in a Qubit ^®2.0 Fluorometer (Life Technologies, CA, USA), and the RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). After the extraction and quality examination of the total RNA from all of the samples, 5 μg RNA from each sample was used for sequencing the library construction. In brief, the ribosomal RNA (rRNA) was eliminated by the Epicentre Ribo-zero™ rRNA Removal Kit (Epicentre, WI, USA) and the sequencing libraries were generated using the rRNA-depleted RNA by NEBNext^® Ultra™ Directional RNA Library Prep Kit for Illumina^® (NEB, USA), following the manufacturer’s recommendations. In addition, the products were purified (AMPure XP system) and the library quality was assessed on the Bioanalyzer 2100 system. Finally, all of the libraries were sequenced on the Illumina HiSeq 4000 platform and 150 bp paired-end reads were generated.

Thereafter, a series of quality control procedures, including the removal of the invalid reads and evaluation of the Q20 index of clean reads, were carried out, according to pipeline established by Novogene, as reported in a previous study [24]. Further, the clean reads were mapped and aligned to the representative goat reference genome (Code: ARS1), using Bowtie 2 [25]. The mapped reads were selected to identify the existence and relative expression of the mRNAs as we already reported in published literature [26]; meanwhile, the reads unmapped to the goat reference genome were chosen for the subsequent circRNAs’ identification. In addition, the construction of the sequencing libraries for the miRNAs and the subsequent bioinformatic analysis were executed, as stated before [26].

2.3. CircRNAs Identification

The two mainstream programs find_circ and CIRI2 were picked to predict the existence of the circular RNAs (circRNAs) in both of the cell types, according to their respective bioinformatic algorithms [27,28]. To overcome the problem that a high false-positive probability of circRNAs’ detection exists, the transcripts detected by both pieces of software were deemed as reliable circular candidates.

2.4. Normalization of circRNAs Abundance and Differential Expression Analysis

The raw circRNAs counts were first normalized using standard TPM (transcripts per million clean reads) as in the following equation: normalized expression level = (read counts per 1,000,000 reads)/libsize (libsize is the sum of circRNAs counts). Principal component analysis (PCA) and cluster dendrogram construction were performed using FactoMineR 2.4 [29] and Cluster 2.1.2 [30] R packages, respectively. The differential expression analysis of the circRNAs between the two sample sets was performed, using DESeq R package (1.10.1), which provides statistical routines for determining the differential expression in digital gene expression data, using a model based on the negative binomial distribution [31]. The transcripts with a p value <0.05 determined by the DESeq tool were thought of as differentially expressed.

2.5. Gene Ontology and KEGG Enrichment Analysis

The gene ontology (GO) enrichment for the host genes of the differentially expressed circRNAs was carried out using the GOseq R package, in which the gene length bias was corrected [32]. The GO terms with a corrected p value < 0.05 were deemed as significantly enriched by the genes. The KEGG pathway enrichment analysis of the host genes was performed, using a web server called KOBAS 3.0 [33]. The results were visualized by ggplot2 package.

2.6. Prediction of miRNA Binding Sites on circRNAs and mRNAs

MiRanda-3.3a was used to predict the target sites for the miRNA binding on the linear 3′ untranslated regions and the overall segments of mRNAs and circRNAs [34], respectively.

2.7. CircRNAs-miRNAs-mRNAs Interplay Network Construction

The interplay network among the circRNAs, miRNAs, and mRNAs was constructed according to the miRNAs target sites prediction on mRNAs and circRNAs. The theory of competing endogenous RNAs (ceRNAs), in which the circRNAs compete with the mRNAs for miRNAs binding, to ensure the stability of the mRNAs, was used to infer the mutual regulatory relationships [35]. R package ggalluvial 0.12.3 was used to generate the Sankey graphics.

3. Results

3.1. Bioinformatic Identification and Genomic Feature Characterization of Circular RNAs

To deeply explore the potential roles of the circRNAs in hair growth, we utilized transcriptomic data generated in our experiment and performed a series of bioinformatic analysis to predict the functioning pathways of circRNAs specifically expressed by the goat DPCs. As shown in Figure 1, Step 1 and part of the work of Step 2 (i.e., the analysis of the mRNAs and miRNAs) were finished for a previous study, and the remaining work belongs to the present study.

Through combining the prediction results of find_circ and CIRI2, a total of 3706 circular RNAs (circRNAs) were bioinformatically identified (Figure 2a). Among these transcripts, 94.6% of them originated from the exons of coding genes; only a small proportion derived from the introns (3.78%) and intergenic regions (1.62%) (Figure 2b). Next, we found that all of the types of the circRNAs share a similar length distribution, in which the majority of them are less than 600 nt. Moreover, the average lengths of the exonic, intronic, and intergenic circRNAs are 280, 266, and 253 nt, respectively (Figure 2c). We also discovered that most of the circRNAs were made up of one–four genomic segments, and the percentage of the circRNAs containing two segments ranked first for exonic and intronic transcripts (Figure 2d). In addition, we demonstrated that the average length of the circRNAs visually increased along with more of the segments (Figure 2e). Meanwhile, the average length per segment was inversely related to the component counts (Figure 2f). Finally, we demonstrated that 62% of the coding genes only produced one circularized transcript, but the minority of the genes were more prolific than that. For instance, the genes generating two and three circRNAs accounted for 22% and 8% of the total genes, respectively (Figure 2g). The above findings suggested that the presently detected circRNAs from the DPCs and DFs possess outstanding genomic features, which could be utilized to judge the fidelity of the circRNAs. The detailed sequence information of all of the transcripts is provided in Table S1 (Supplementary Materials).

3.2. Global Expression Pattern and Differential Expression Analysis of circRNAs

As shown by the boxplots in Figure 3a, the TPM values of all of the transcripts in the six samples displayed a similar distribution mode, suggesting that the circRNAs’ transcripts of all of the cells shared a nearly identical expression pattern at a global level. Next, we found a significant segregation of the samples at the first dimension on PCA figure (Figure 3b). This result is in high accordance with the sample clustering analysis, in which the cellular samples clustered into two independent clades (Figure 3c). Altogether, these findings hinted that the expression of some of the transcripts were highly cell-type-dependent. Subsequently, we identified a sum of 121 differentially expressed circRNAs between two of the sample sets (p < 0.05), including 76 upregulated and 45 downregulated in the DPCs when the DFs were set as controls (Figure 3d,e). We listed the top 20 of the differentially expressed circRNAs ranked with a p value in

Table 1. Top 20 differentially expressed circRNAs between goat DPCs and DFs ranked by statistical significance.

CircRNAs ID	DPCs TPM	DFs TPM	log2 (FC)	p Value	Host Genes
chi_circ_0001237	27.66	2.60	3.19	5.83 × 10−7	EPDR1
chi_circ_0001124	10.42	0	5.06	0.00018033	RBM33
chi_circ_0005517	77.94	33.43	1.20	0.00019107	MTCL1
chi_circ_0005255	9.48	0	4.93	0.00029672	ADAMTS9
chi_circ_0003390	36.68	12.84	1.46	0.00044071	FNDC3A
chi_circ_0002616	8.49	0	4.78	0.00052082	SMOC2
chi_circ_0005862	0	7.36	−4.65	0.00091454	HTRA1
chi_circ_0002154	5.49	19.05	−1.76	0.0013659	PLIN2
chi_circ_0004069	0.67	8.68	−3.27	0.0022086	ENAH
chi_circ_0003353	0	5.74	−4.33	0.0025604	CDK8
chi_circ_0005056	56.58	106.67	−0.91	0.0025953	CEMIP
chi_circ_0004639	0.00	5.69	−4.32	0.0025977	APPBP2
chi_circ_0005733	5.61	0	4.15	0.0041094	FGFR2
chi_circ_0003643	35.55	16.39	1.12	0.0041723	TASP1
chi_circ_0001957	9.21	0.94	2.81	0.0041946	DPYSL3
chi_circ_0003371	0	5.05	−4.11	0.0048414	DCLK1
chi_circ_0004078	5.29	0	4.05	0.0053418	KIF26B
chi_circ_0002817	3.91	13.87	−1.75	0.006652	PTGDR
chi_circ_0003391	21.04	8.06	1.35	0.0067238	FNDC3A
chi_circ_0000379	4.27	14.53	−1.70	0.0068278	ZMAT3

Note: DPCs, dermal papilla cells; DFs, dermal fibroblasts; circRNAs, circular RNAs; TPM, transcripts per million clean reads; FC, fold change.

, and found that a group of transcripts (e.g., chi_circ_0001124 and chi_circ_0005862) were exclusively expressed in one cell type. Furthermore, we also found that the three ciRNAs (e.g., chi_circ_0005569) and the three intergenic region-generated circRNAs (e.g., chi_circ_0000835) displayed distinct abundances between the DPCs and DFs (

Table 2. Differentially expressed ciRNAs and intergenic region-generated circRNAs between goat DPCs and DFs.

CircRNAs ID	DPCs_TPM	DFs_TPM	log2 (FC)	p Value	Host Gene
CiRNAs
chi_circ_0005569	4.95	0	3.95	0.0070649	CCBE1
chi_circ_0003704	2.93	0	3.04	0.049869	ZFAND1
chi_circ_0005570	48.90	24.95	0.95	0.012148	CCBE1
Intergenic region-derived circRNAs
chi_circ_0000835	8.21	0	3.30	0.034524	NA
chi_circ_0004524	129.36	11.39	2.95	0.0098427	NA
chi_circ_0006241	19.51	34.13	−0.80	0.032297	NA

Note: CiRNAs, cNote: CiRNAs, circular intronic RNAs; NA, not available.

). Collectively, the above results indicated that the DPCs and DFs possessed a featured transcriptional profile and the signature transcripts might underpin their functional heterogeneity. Overall, the expression levels and the differential expression analysis result are provided in Table S2 (Supplementary Materials).

3.3. Relationship of circRNAs Expression with Their Host Genes

To determine the relationship between the expression levels of the circRNAs with their host genes, we correlated their relative abundances between the cell types and the performed statistical analysis. As shown by the heatmap in Figure 4a, the trend of the circRNAs expression pattern is not strictly consistent with that of the mRNAs. Notably, the fold changes of the two host genes, ZMYM6 and RPS6KC1, both display inverse modes with the relative abundances of the circRNAs derived from them. In addition, the statistical results using Pearson’s correlation analysis indicated that the relative levels of the circRNAs and mRNAs are correlated at a medium level (Figure 4b: R = 0.41; p = 3.9 × 10⁻⁵), suggesting that no more than a weak association exists. Furthermore, we summarized the expression status of the circRNAs and their host genes between DPCs and DFs (Table S2, Supplementary Materials), and found that more than half of the differentially expressed circRNAs derive from genes with equal abundances in DPCs and DFs. At the same time, less than 50% percent of the differentially expressed transcripts showed similar expression patterns with that of their host genes. In addition, the abundances of a small percentage of the circRNAs showed reverse trends compared to their host genes. These results demonstrated that the physiological and cellular functions of circRNAs may not strictly depend on the expression status of their host genes.

3.4. Functional Enrichment Analysis of the Host Genes

A total of 104 host genes were used as input for the functional enrichment analysis. As shown in Figure 4c, several signaling pathways, including Lysine degradation, MAPK signaling pathway, small-cell lung cancer, and four other signaling pathways were significantly enriched (p < 0.05). The GO analysis results showed that 76 of the items were significantly enriched, most of which belonged to the molecular function (MF) and biological process (BP) categories (Figure 4d). The most significantly enriched GO items in MF comprised ion binding (GO: 0043167), kinase activity (GO: 0016301), and others. At the same time, several cell cycle-related items, including the G2/M transition of mitotic cell cycle (GO: 0000086) and the regulation of the G2/M transition of the mitotic cell cycle (GO: 0010389) were significantly highlighted in BP. Detailed information is provided in Table S3 (Supplementary Materials).

3.5. Screening of circRNAs Acting as ceRNAs and Their Regulatory Relationships with Functional Genes in Goat DPCs

To infer the possible characteristics of the circRNAs in cells, we screened the circRNAs acting as ceRNAs and constructed the regulatory relationships of the circRNAs with miRNAs and mRNAs. As a result, a total of 48,676 circRNAs–miRNA–mRNAs interactive lines were bioinformatically identified (Table S4, Supplementary Materials). In our previous study, we defined the core signatures of the goat DPCs through a comparative transcriptomic analysis of the goat DPCs and DPCs [26]. Among the signature genes, HOXC8 and RSPO1 were shown to govern the activation of the hair follicle stem cells, the most critical path of the DPCs in controlling hair growth [4,5]. Thus, we filtered the circRNAs functioning as ceRNAs to modulate the transcripts’ abundances of HOXC8 and RSPO1, and exhibited the corresponding relationship between the three types of transcripts. As suggested by the heatmap in Figure 5a, the circRNAs’ candidates and the genes showed reverse expression patterns compared to the trends of the miRNAs between the samples, which fitted the theoretical basis of the ceRNAs [35]. Furthermore, we demonstrated that the circRNAs might indirectly adjust the transcript abundances of the mRNAs via sponging miRNAs (Figure 5b). For example, the adverse effect of the miRNA-145 on the HOXC8 mRNA abundance or translation could be specifically ameliorated by a set of circRNAs, including chi-circ_0001956, and others (Figure 4b). Moreover, the inhibitive actions of novel_624 and other six miRNAs on RSPO1 mRNAs may be eased by cognate circRNAs, such as chi_circ_0004422 via chi_circ_0004422-novel_624-RSPO1 interacting line. These results implied that the circRNAs possibly participate in hair-follicle-stem cells vitalization via finely adjusting the expressions of the pivotal genes in DPCs.

In addition, we observed the elevated abundances of the two intron-derived circRNAs chi_circ_0005569 and chi_circ_0005570 in goat DPCs compared to DFs (Figure 5c). Previous studies reported that their host genes CCBE1 were involved in tissue angiogenesis [36], a critical physiological process related to hair follicle development. At the same time, several findings pointed out that intronic circRNAs mostly reside in the nucleus and regulate the transcriptions of their host genes in cis [19,20]. Based on the facts of the above studies, we reasonably reckoned that the two ciRNAs possibly execute similar roles on their parental gene. Moreover, we also found that two intergenic segment-formed circRNAs chi_circ_0000835 and chi_circ_0004524 possess binding sites for the miRNAs, targeting the matured CCBE1 transcripts (Figure 5d), suggesting their strong potential in enhancing CCBE1 transcript stability or protein output. The above results suggested that the circRNAs might take part in modulating the follicular angiogenesis process via acting as ceRNAs or transcriptional regulators.

4. Discussion

Serving as the signaling center of HF, DPCs are essential for a wide range of developmental events during the entire phase of hair growth [1,2]. Previous studies have identified a group of DPCs’ signature genes involved in key events (e.g., HFSCs’ activation) in HF growth [4,5]; however, how the expressions of the genes are regulated at transcriptional or translational level still remains elusive. In recent years, circRNAs gradually emerged as the key participators in various physiological and pathological processes via the regulating gene transcription, decoying miRNAs and other functioning mechanisms [12]. Meanwhile, the potential functions of the circRNAs in key aspects of hair biology (i.e., HF development and fiber traits) have been proposed, even though most of these studies were carried out at the tissue level [14,15,16,17]. Our present study points out that the circRNAs might participate in the pivotal events of hair growth via affecting the abundances of related functional genes in DPCs. We established a comprehensive genome-wide profile of circular transcripts in goat DPCs and DFs, and constructed the modulatory relationships between the circRNAs and coding genes.

We demonstrated that the presently identified circRNAs possess several prominent features regarding their sources, length, and other sequence characteristics. These genomic properties are highly consistent with the circular transcripts found in the tissues of goats [37], sheep [38], humans [39], and yaks [40]. The phenomenon not only validates the fidelity of our circRNAs identification, but also fits the evolutionary conservation of the biogenesis and functioning mechanisms of the circRNAs. In addition, we also found that DPCs and DFs possess a distinct transcriptional profile and the expressions of the circRNAs are highly cell-context dependent. Numerous studies have confirmed that the specific expressions of the transcripts is the hallmark and indicator of their special functionality in cells or tissues [12,13]. For example, circTshz2-1, a uniquely expressed transcript in differentiated adipocytes, exerts a promotive effect on mouse adipogenesis via upregulating the genes critical for lipid accumulation [10]. Similarly, the significant upregulation of circRNA-0100 expression positively drives the committed differentiation of HFSCs towards their progenies in cashmere goats [41]. Thus, it is reasonably to speculate that these differentially expressed circRNAs should perform functions of importance related to the characters of these cells in tissue development.

Next, we demonstrated that the expressions of the circRNAs are not tightly coupled to the levels of linear transcripts of their host genes. This discovery is in high accordance with the results found in the tissues of other animals [19,42]. Previous studies validated that both the circRNAs and mRNAs are alternative splicing products of primary mRNAs [21], confirming that a mutually competitive relationship exists during their biogenesis. This also partially explains why the expressions of a subset of circRNAs are independent of their linear isoforms in the present and other studies. Researchers have often performed GO and KEGG enrichment analysis of the host genes to reckon the functionalities of the circRNAs [37,40,43]. In human adipocytes, the targeted knockdown of linear or circular transcripts of the gene-Arhgap5 obviously caused adverse trends in the abundance of the genes that determine adipogenesis [10]. A similar case occurs with circSMARCA5, which decreases the expression level of its parental gene via exclusively binding to the gene locus and pausing transcription in breast cancer tissue [44]. The above cases implied that the linear and circular isoforms of a gene can exert opposite effects on the same biological processes. However, our results imply that the enriched terms (e.g., MAPK signaling pathway and cell cycle-related terms) frequently appear in transcriptomic studies involving hair growth and reflect the differences in cellular identity of the cells [45,46]. We highly recommend that additional precautions should be taken when the bioinformatic deduction of the potential roles of circRNAs using functional enrichment of their host genes occurs.

Finally, we observed that the circRNAs might serve as ceRNAs, and constructed the interaction network of circRNAs–miRNAs–mRNAs, in which we highlighted the modulatory roles of circRNAs on genes (e.g., HOXC8, RSPO1, and CCBE1) concerning two key events in hair growth. Functioning as sponges that bind miRNAs and thus prevent them from binding and suppressing their target mRNAs is one of the most important and extensively explored approaches through which the circRNAs exert their functionalities. A large quantity of related circRNAs have been identified in the skin tissues of goats and sheep, and some of the circRNAs have been experimentally authenticated [15,16,17]. The circRNAs–miRNAs–mRNAs axis was gradually recognized as a pivotal avenue through which the circRNAs exert their important roles in several aspects of hair biology.

For example, the circRNA-1926–miR-148a/b-3p–CDK19 axis was exhibited to participate in the goat HFSCs’ activation [18]. In the present study, we proposed that the circRNAs might participate in the regulation of the signature genes of goat DPCs in the same manner. HOXC8 and RSPO1 have been verified as the key drivers in the DPCs-stimulated activation of HFSCs and the subsequent hair regrowth via vitalization of the Wnt signaling pathway [4,5]. Therefore, it is possible that an interactive axis, such as chi-circ_0001956–miRNA-145–HOXC8 could perform regulatory roles of crucial importance in HFSCs’ activation via the post-transcriptional modification of genes involved.

In addition, we also discovered that the abundances of two intron-derived circRNAs chi_circ_0005569 and chi_circ_0005570 are consistent with that of their host gene CCBE1. A few studies have demonstrated that circRNAs are capable of regulating gene transcription via interacting with RNA Pol II, or transcription factors [19,20]. Notably, an intronic circRNA named ci-ankrd52 could drive its host gene expression via locating at the genomic sites of transcription and interacting with the transcriptional machinery [20]. Thus, it is possible that the expression of CCBE1 is under the transcriptional control of intronic circRNAs, derived from itself. CCBE1 encodes an extracellular matrix protein that implicates angiogenesis [36], which is a physiological process closely associated with active hair growth [7]. A decreased mRNA level of CCBE1 was associated with the impaired hair growth-stimulatory capacity of DPCs under the treatment of dihydrotestosterone; the hormone causes undesired androgenic alopecia [47]. Therefore, the two intronic circRNAs perhaps exert regulatory characteristics in follicular angiogenesis via an adjustment of the abundances of CCBE1 in goat DPCs. Moreover, we also found that some circRNAs might serve as ceRNAs to modulate the expression of CCBE1, further confirming the complexity of gene expression regulation at transcriptional and translational levels.

5. Conclusions

In present study, we established a comprehensive global profile of circRNAs in goat DPCs and DFs. Through comparative analysis, we identified a group of circRNAs specifically expressed in each cell type. Further, we predicted the potential roles of circRNAs in DPCs via constructing their regulatory relationships with the genes involved in key events in hair growth. The validation of such relationships will provide new insight into how the functionality of DPCs is maintained by circRNAs through regulating the gene expression at transcriptional and translational levels.