Skip to main content

Key circRNAs from goat: discovery, integrated regulatory network and their putative roles in the differentiation of intramuscular adipocytes

Abstract

Background

The procession of preadipocytes differentiation into mature adipocytes involves multiple cellular and signal transduction pathways. Recently. a seirces of noncoding RNAs (ncRNAs), including circular RNAs (circRNAs) were proved to play important roles in regulating differentiation of adipocytes.

Result

In this study, we aimed to identificate the potential circRNAs in the early and late stages of goat intramuscular adipocytes differentiation. Using bioinformatics methods to predict their biological functions and map the circRNA-miRNA interaction network. Over 104 million clean reads in goat intramuscular preadipocytes and adipocytes were mapped, of which16 circRNAs were differentially expressed (DE-circRNAs). Furthermore, we used real-time fluorescent quantitative PCR (qRT-PCR) technology to randomly detect the expression levels of 8 circRNAs among the DE-circRNAs, and our result verifies the accuracy of the RNA-seq data. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the DE-circRNAs, two circRNAs, circ_0005870 and circ_0000946, were found in Focal adhesion and PI3K-Akt signaling pathway. Then we draw the circRNA-miRNA interaction network and obtained the miRNAs that possibly interact with circ_0005870 and circ_0000946. Using TargetScan, miRTarBase and miR-TCDS online databases, we further obtained the mRNAs that may interact with the miRNAs, and generated the final circRNA-miRNA-mRNA interaction network. Combined with the following GO (Gene Ontology) and KEGG enrichment analysis, we obtained 5 key mRNAs related to adipocyte differentiation in our interaction network, which are FOXO3(forkhead box O3), PPP2CA (protein phosphatase 2 catalytic subunit alpha), EEIF4E (eukaryotic translation initiation factor 4), CDK6 (cyclin dependent kinase 6) and ACVR1 (activin A receptor type 1).

Conclusions

By using Illumina HiSeq and online databases, we generated the final circRNA-miRNA-mRNA interaction network that have valuable functions in adipocyte differentiation. Our work serves as a valuable genomic resource for in-depth exploration of the molecular mechanism of ncRNAs interaction network regulating adipocyte differentiation.

Peer Review reports

Introduction

Intramuscular fat (i.m.) content is a determinant of edible flavor of the meat of farm animals. Unlike subcutaneous and visceral fat content, in the livestock industry, higher levels of intramuscular fat content have been shown to help improve flavor and palatability of meat [1, 2]. Adipogenesis consists of two phases, namely commitment and terminal differentiation. Preadiposcyte (brown or white or brite) presenting throughout adult life, it can differentiate and proliferate from distinct progenitor cells [3]. Examine the signaling cascades and regulation behind adipogenesis and adiposcyte differentiation, may provide insight into the plasticity of adipose tissue and the development of new techniques in animal husbandry. Numerous studies have demonstrated that adipocyte formation and deposition is a complex and precisely orchestrated process that mediated by a series of adipocyte regulatory factor networks. In it, the roles of ncRNAs in regulatory adipocyte differentiation, proliferation and fat deposition cannot be ignored [4,5,6,7]. However, the endogenous regulatory pathways and functions for most ncRNAs are still unclear. Thus, summarizing and mining new interactive networks is of substantial significance to explore the regulatory mechanisms of intramuscular adipocyte differentiation and deposition.

Benefiting from the development of high-throughput sequencing and computer analysis techniques, the classifications of ncRNAs are becoming more specific. Nowadays, it has been realized to identified differentially enriched ncRNAs in tissues of different growth conditions or different developmental stages, this promotes more accurate and efficient in-depth exploration the functions of ncRNAs [8, 9]. For adipocyte differentiation, lots of ncRNAs have been shown their functional roles in many signaling pathways. Such as, mTOR [10], PI3K/Akt [11], MAPK [12], TGFβ [13] and Wnt [14] signaling pathways, and so on. CircRNAs as a type of ncRNA, characterized by a covalently closed-loop structure in which a downstream 5′ splice site (ss) is joined with an upstream 3′ ss, and often exhibit cell-type-specific and tissue-specific patterns [15, 16]. Natural circRNAs received high attention cause their efficient sponge roles [17]. For example, CircFUT10 promotes adipocyte proliferation and inhibits adipocyte differentiation in cattle via sponging let-7 [4]. Recently, identification lnc/circRNA-miRNA-mRNA competitive endogenous RNA network, find their interacts became a development direction in-depth research ncRNAs. For instance, study found that LncRNA CCDC26 interacts with CELF2 protein can enhance mice myeloid leukemia cell proliferation and invasion via the circRNA_ANKIB1/miR-195-5p/PRR11 Axis [18]. From the various studies on ncRNAs we can know that ncRNAs are involved in a broad regulatory network and play critical roles under different conditions [15, 19, 20]. While, there are very few related studies on the regulatory network of ncRNAs in the field of intramuscular adipocyte differentiation.

Here, we choose Jianzhou Goat, a goat breed in Chinese southwest, that well-received for its high meat percentage and high intramuscular fat content [16, 17]. Illumina HiSeq method was used to compare the transcriptomes data of goat intramuscular adipocytes differentiation. We purposed to find the differentially and highly expressed circRNAs between the early and late stages of intramuscular adipocytes differentiation. Explored their expression profiles and further predicted their possible interaction relationships. This work serves as a valuable genomic resource for in-depth exploration of the molecular mechanism of ncRNAs interaction network regulating adipocyte differentiation.

Results

CircRNAs mapped in goat intramuscular preadipocytes and adipocytes

Using the illumina HiSeq high-throughput assay platform, we mapped 88–100 and 85–104 million clean reads in goat intramuscular preadipocytes (IMPA) and intramuscular adipocytes (IMA), respectively (Table 1), and the error rate along reads < 0.5% (Fig. 1 A). Comparison the circRNAs that mapped in clean reads with HISAT2 software. Among the known types of genes, we found that approximately 75.9% of the reads mapped to protein coding regions, 0.8% mapped to LncRNAs or miscellaneous RNA regions, and 22.4% of the reads were not mapped (Fig. 1 B). CircRNAs can be derived from exon or intron splicing, and we found exon-derived circRNAs were occupied the majority in this study (Fig. 1 C). In addition, the density of read circRNAs were counted in each chromosome, we showed the Top10 chromosomes in the genome (Fig. 1 D), and the total number of mappings on the chromosomes (Fig. 1 E). The longer the length of the entire chromosome, the more total mapped reads in the chromosome. We next normalized the circRNAs expression patterns of the samples using TPM density distribution map, and our results showed that the number of CircRNAs transcripts in IMPA were slightly less than IMA (Fig. 1 F).

Table 1 The RNA-seq mapping reads of goat reference genome
Fig. 1
figure 1

Identification of circRNAs in goat IMPA and IMA. A Sequencing error rate distribution, due to the consumption of chemical reagents, the error rate increases with the increase of the sequence length, which is one of the characteristics of illumina HiSeq, and the error rate per base is generally < 0.5%; B Clean reads distribution in known types of genes; C Source statistics of circRNAs in samples; D Density distribution of circRNAs on chromosomes, the outer edge is shown as the top 10 chromosomes, and the gray area in the middle is the distribution of 10,000 reads; E Distribution of the reads on each chromosome of the reference genome, sliding window is 1 kb and green is positive chain, red is negative chain; F Boxplots of the circRNAs expression levels in the sample, using TPM to normalize statistics

Differentially expressed circRNAs of goat IMPA and IMA

Based on log2 fold change and TPM density distribution of expression, the DE-circRNAs were identified using TMM normalization and DEGseq [18, 21]. For comprehensive observation, we union the differentially expressed CircRNA sets of each experimental sample, and cluster the CircRNAs with log10(TPM + 1) value from large to small according to the TPM value, the hierarchical clustering analysis showed that there were more high-expression CircRNAs in each samples (Fig. 2 A). Then, we compared the data of goat IMPA with IMPA, we found there were 8 downregulated and 8 upregulated circRNAs when selection filter condition was q-value< 0.01 (Fig. 2 B), and details of the circRNAs were shown in Table 2. To further analyze the potential functions of circRNAs, GO and KEGG were performed for the characterization of the identified circRNAs. For cellular components (Fig. S1), 2 GO terms were involved in collagen and extracellular matrix part. Regarding molecular function (Fig. S2), 2 GO terms were classified in protein binding and S-adenosylmethionine-dependent methyltransferase activity. For the biological process (Fig. S3), the dominant categories were multicellular organismal development, system development and regulation of angiogenesis and so on. Notably, the function annotation analysis showed that the DE-circRNAs were mainly enriched in multicellular organismal development biological process (BP) (Fig. 2 C). Furthermore, the KEGG pathway analysis of these DE-circRNAs showed that they were very likely to participate in Lysine degradation, ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway (Fig. 2 D). The GO and KEGG terms of host genes encoding DE-circRNAs (P < 0.05) were listed in Supplementary Material S1. Further analysis the enrichment results, we found that both circ_0005870 and circ_0000946 were existed in the Focal adhesion and PI3K-Akt signaling pathway, which are closely related to cell proliferation, differentiation and lipid metabolism.

Fig. 2
figure 2

DE-circRNAs of goat IMPA and IMPA. A Hierarchical clustering diagram of DE-circRNAs, clustering as log10 (TPM + 1) value, red indicates high expression circRNAs, blue indicates low expression circRNAs; B Volcano plot of DE-circRNAs in IMPA and IMA, the screening condition is qvalue< 0.01; C GO enrichment histogram of the DE-circRNAs source gene; D KEGG enrichment scatter plot of the DE-circRNAs source gene

Table 2 DE-circRNAs in goat IMPA and IMPA

Validation of DE-circRNAs using qRT-PCR combined with RNA-seq

We selected 8 DE-circRNAs randomly to verify their expression levels by qRT-PCR method, the circPirmer were used to design amplification primers (Supplementary Material S2), the result shown in (Fig. 3). Our results of qRT-PCR were consistent with those of RNA-seq, circ_0009231, circ_0000946 and circ_0003129 were down regulated ncRNAs, and the circ_0006499, circ_0006511, circ_0011446, circ_0008259 and circ_0005870 were up regulated ncRNAs. Meanwhile, the expression level of circ_0006499, circ_0006511, circ_0011446 in qRT-PCR results were significantly higher than RNA-seq, the expression level of circ_0009231 in qRT-PCR result was significantly lower than RNA-seq.

Fig. 3
figure 3

Validation of DE-circRNAs using qRT-PCR. The qRT-PCR data were analyzed using 2−ΔΔCt method, and ubiquitously expressed transcript (UXT) was used as endogenous control. *P < 0.05, **P < 0.01

Identification of circRNA-miRNA network

Remarkably, emerging studies have manifested that some circRNAs have miRNA binding sites, and function as miRNA sponges to regulate gene expression and play crucial roles in multiple cellular processes [22, 23]. To construct a circRNA-miRNA network, we used miRanda and TargetFinder softwares to predict miRNA binding sites for spliced circRNAs and the predicted results were listed in Supplementary Material S3. Based on the result we generated the circRNA-miRNA network diagram for the 8 DE-circRNAs, and the circ_0005870 and circ_0000946 that both existed in Focal adhesion and PI3K-Akt signaling pathways were marked with red. (Fig. 4).

Fig. 4
figure 4

The interaction network of circRNA-miRNA. The red network nodes represent the circRNAs that existed in both Focal adhesion and PI3K-Akt signaling pathway, the green network nodes represent the DE-circRNAs in our research, orange network nodes represent the target miRNAs of the DE-circRNAs and larger nodes represent more interacting miRNAs

A circRNA-miRNA-mRNA network associated with goat intramuscular adipocyte differentiation

Two circRNAs, circ_0005870 and circ_0000946, were found existed in both focal adhesion [24, 25] and PI3K-Akt signaling pathways [11, 26] in this study, which given a great possibility of their functions in adipocyte differentiation. Therefore, we further performed prediction analysis to find the potential target genes of miRNAs that interacting with these two circRNAs. By overlapping the prediction results of three online databases, TargetScan, miRTarBase and miR-TCDS, we obtained the target mRNAs that may interact with the miRNAs (Fig. 5A, Supplementary Material S4), then, we generated the circRNA-miRNA-mRNA network diagram, based on the above results (Fig. 5B).

Fig. 5
figure 5

The circRNA-miRNA-mRNA interaction network. A Venn diagram showing the number of mRNAs in the databases that targeting the miRNAs; B The red triangle nodes represent the circRNAs that may having function roles in adipocyte differentiation, the orange square nodes represent the miRNAs that interacting with the circRNAs and the green network nodes represent the mRNAs we obtained from the databases

GO and KEGG enrichment analysis of the target mRNA

To further explore the possible pathways that the circRNA-miRNA-mRNA interaction ways regulates goat intramuscular adipocyte differentiation in this study, we further performed GO and KEGG enrichment analysis on the target mRNAs (Fig. 6). The GO and KEGG analysis results were listed in Supplementary Material S5. From the enrichment analysis results, we found 31(46.2%) mRNAs were enriched in regulation of macromolecule metabolic process in BP (Fig. 6A), next, 5 of them were found enriched in the PI3K-Akt signaling pathway (FOXO3, PPP2CA, EIF4E, CDK6), AMPK signaling pathway (FOXO3, PPP2CA) and TGF-beta signaling pathway (ACVR1, PPP2CA), respectively (Fig. 6B). This result suggests the potential key roles of FOXO3, PPP2CA, EIF4E, CDK6 and ACVR1 in our circRNA-miRNA-mRNA interaction network.

Fig. 6
figure 6

GO and KEGG enrichment analysis of the target mRNA. A GO enrichment histogram of the mRNAs; B KEGG enrichment scatter plot of the mRNAs, we showed top 25 here

Discussion

Fat exists in almost all organisms. It is not only an important energy storage organ, but also has a regulatory effect on the physical support of tissues and organs, endocrine activity, energy balance and development [27, 28]. In animal husbandry field, the content of abdominal fat, subcutaneous fat and intramuscular fat of the industrialization animals are deemed as important indicator for the quality and flavor of meat [29,30,31]. Fat production can be divided into two stages, that are, mesenchymal stem cells turn into preadipocytes and terminal differentiation of the preadipocytes [32]. The procession of preadipocytes differentiation into mature adipocytes involves multiple cellular and signal transduction pathways, because, once preadipocytes have started the adipogenesis program, transcriptional cascades would inducing expression of metabolic genes and adipokines associated with the adipocyte phenotypes, such as Peroxisome proliferator-activated receptor γ (PPARγ), CCAAT-enhancer-binding protein α (C/EBPα), Krüppel-like factors (KLFs) and fatty-acid-binding protein-4 (Fabp4) and so on [33]. In addition, some extracellular signalings that acting upstream of transcription factors have been shown to regulate adipogenesis. For example, canonical Wnt-family proteins initiate signaling through β-catenin-dependent and -independent pathways, which inhibit adipocyte differentiation via blocking the expression of PPARγ and C/EBPα [34,35,36]. Signal transduction pathways closely related to adipogenesis are also Notch signalling, DLK1/PREF1 signalling, MAPK pathway, PI3K/AKT signalling and so on [37,38,39,40]. With the development of high-throughput RNA-seq technology, molecular mechanisms that regulate the adipocyte differentiation have been furthering described. And it is no doubt that exploring preadipocytes as a tool for precise regulation of adipocyte differentiation will promoting the development of precision medicine and the industrialization reform of edible meat quality in animal husbandry.

The specific functions of ncRNAs in regulating gene expression at the epigenetic, transcriptional and post-transcriptional levels suggested their important roles in a variety of biological processes [37, 41, 42]. In this study, illumina HiSeq high-throughput RNA-seq analysis was performed to identified and screened DE-circRNAs, 8 downregulated and 8 upregulated circRNAs were revealed in goat intramuscular adipocytes samples. From the KEGG enrichment analysis of the DE-circRNAs, we found that circ_0005870 and circ_0000946 were both enriched in the Focal adhesion and PI3K-Akt signaling pathways. Previous studies have demonstrated that focal adhesion kinase can induce mesenchymal stem cells (MSCs) differentiation [38]. Furthermore, same as multiple types of adipogenic induction, a study found that heparin promotes adipogenic differentiation by disrupting focal adhesions in immortalized and normal mouse marrow stromal cells by upregulating adipogenic genes [39, 40]. The PI3K-Akt intracellular signaling cascade is involving in many cellular regulation processes, and some evidences have indicated that PI3K/Akt pathway is a positive regulator of terminal adipocyte differentiation in mouse preadipocytes, moreover, phosphoinositide 3-kinase is also required for human adipocyte differentiation [26, 43, 44]. Based on these results, we hypothesized that circ_0005870 and circ_0000946 play an important role in the early differentiation of goat intramuscular preadipocytes.

Among ncRNAs, circRNAs are pronlly considered to be more stable due to the lack of 5′ to 3′ polarity and polyadenylation tail. Recent study found that, circ_0030042 regulates abnormal autophagy and protects atherosclerotic plaque stability by targeting eukaryotic initiation factor 4A-III, in high-fat-diet fed ApoE−/− mice [45]. In adipose tissues from cachectic patients, circPTK2 competitively binding to miR-182-5p then promoted lipolysis and lipogenesis were inhibitied [46]. Based on circRNA-seq analysis, circBDP1 was proved to regulate bovine fat development by sponging miR-181b/miR-204 targeting Sirt1/TRARG [47]. In this study, we using miRanda and TargetFinder softwares predicting the miRNA binding sites for these DE-circRNA and a circRNA-miRNA interaction network for the DE-circRNAs was constructed by using Cytoscape software. From our interaction network, we found that circ_0000946 may have an interaction relationship with miR-412-3p, miR-197-3p, miR-26b-5p, miR-26a-5p, miR-3432-5p, miR-16b-3p, miR-34c -3p and miR-34b-3p. Among them, miR-197-3p [48], miR-26b-5p [49] and miR-26a-5p [50] have showed their correlation with adipocyte differentiation. For circ_0005870, we found it can have an interaction with miR-1249, miR-532-3p, miR-487a-5p, miR-140-3p, miR-216b, miR-423-5p, miR-502a, miR-500-3p, miR-504, miR-128-5p, miR-671-5p and miR-2331. Among them, miR-532-3p [51], miR-140-3p [50], miR-423-5p [52], miR-500-3p [53] and miR-671-5p [54] have showed their correlation with adipocyte differentiation.. In summary, our research indicated that the significantly DE circRNAs in goat preadipocytes might interact with some potential miRNAs.

In order to make our interaction network more hierarchically and characteristic, so, we selected circ_0005870 and circ_0000946, related to adipocyte differentiation, and their interaction miRNAs for further analysis. We first overlapped the prediction results from three online databases TargetScan, miRTarBase and miR-TCDS, obtained the potential target genes of the miRNAs, and generated the circRNA-miRNA-mRNA interaction network. Based on the predicted results, we next performed GO and KEGG enrichment analysis on these target mRNAs. The GO enrichment result showed that the mRNAs we found were all participated in macromolecule metabolic process. Three pathways, PI3K-Akt signaling pathway, AMPK signaling pathway and TGF-beta signaling pathway, got our attentions in KEGG enrichment result, cause they all having essential roles during adipocyte differentiation [26, 55, 56]. Then we obtained 5 key mRNAs in the circRNA-miRNA-mRNA network, FOXO3 [57], PPP2CA [58], EEIF4E [59], CDK6 [60] and ACVR1 [61], and they all showed valuable functions in fat-related research. The above results showed that the circRNA-miRNA-mRNA interaction network in this study can provide a reliable research idea for further exploring the mechanism of goat adipocyte differentiation. However, these findings are preliminary, and future research is needed to confirm them.

Conclusions

Our finding revealed the expression profile and potential roles of circRNAs in goat intramuscular adipocytes. For adipocyte differentiation, we found circ_0005870 and circ_0000946 may play function roles in Focal adhesion and PI3K-Akt signaling pathway. By using online databases, we generated the final circRNA-miRNA-mRNA interaction network, and further showed 5 mRNAs, FOXO3, PPP2CA, EEIF4E, CDK6 and ACVR1, who existed in key signaling pathways in adipocyte differentiation. Taken together, our study explored the potential molecular mechanism of ncRNAs interaction network regulating adipocyte differentiation.

Materials and methods

The experiment flow in this study

Five samples of goat IMPA, induced 0d, and IMA, induced 3d, respectively, were performed Illumina HiSeq to identify the differentially expressed circRNAs, and the experimental strategy is shown in Supplementary material S6.

Animals

The samples were collected from the longissimus dorsi muscle intramuscular fat of three healthy Jianzhou goat (male, 7 days old) that purchased from Jianyang Da Geda Animal Husbandry Co., Ltd. (Sichuan, China). Isolation procedures of goat intramuscular preadipocytes have been published previously [62, 63].

Cell culture

The goat intramuscular preadipocytes were cultured in DMEM/F12 (Hyclone, USA) medium that containing 10% (v/v) fetal bovine serum (FBS, Hyclone, USA) and put in a humidified incubator with 5% CO2 at 37 °C for proliferation. To induce preadipocyte differentiation we replaced medium with adipogenic inducer of DMEM/F12 containing 10% FBS and 50 μmol•L-1 oleic acid when the preadipocytes proliferated to 90% of the cell culture dish [64]. For RNA extraction and sequencing, IMPA and IMA were collected on day 0 and day 3 after the proliferation medium was changed to induction medium, respectively.

RNA-Seq library construction and sequencing analysis

Total RNA of IMPA and IMA was isolated using TRIzol reagent (TaKaRa, Otsu, Japan), and Nanodrop and agarose gel electrophoresis methods were used to detecte RNA purity and integrity. Afterwards, RNA concentrations were quantified using NanoDrop spectrophotometer (NanoDrop, Wilmington, USA) and Agilent 2100 Bioanalyzer (Agilent, Santa Clara, USA). Before constructing the RNA-seq libraries, samples were treated with epicentre Ribo-Zero™ Kit (Epicentre, Madison, USA) to remove Ribosomal RNA. Library preparation and Illumina HiSeq analysis process were described in previous study [65]. The raw reads were analysised using High Performance Computing (HPC) and the quality assessment data was listed in Supplementary material S7.

NcRNAS identification and the expression levels analysis

The raw reads obtained by sequencing were filtered with removed the low-quality reads to obtain clean reads. To identify reliable circRNAs, we used two software packages “find_circ” and “CIRI2” to identify and detected the circRNAs, separately [66, 67]. Combining the circRNAs identified by find_circ and CIRI2, the candidate circRNAs in “find_circ” group with read count ≥2were selected, and then intersection of the two identified results based on the location of the circRNAs in the chromosome. The expression statistics of known and novel circRNAs in each sample was performed, and the raw counts were normalized using TPM [68]. Normalized expression level = (readCount*1,000,000) / libsize (libsize is the sum of the circRNA read count). For small RNA libraries, we used novoaligen software to align clean reads with the miRBase database to identify known miRNAs, and used mirDeep software to predict Novel miRNAs, and then used novoaligen and samtools software for quantitative analysis, using RPM (reads per million) for standardization [69,70,71,72].

RNA extraction and qRT-PCR

Total RNA was extracted using TRIzol (TaKaRa, Otsu, Japan), and stored at − 80 °C. The mRNA was reverse transcripted using a Revert Aid First Strand cDNA Synthesis Kit (TaKaRa, Otsu, Japan) according to manufacturer instructions. The reaction volume for qRT-PCR was 20 μL that consisted of 1 μL cDNA, 1 μL reverse and forward primers, 7 μL double-distilled water, and 10 μL TB Green™ Premix Ex Taq™ II (TaKaRa, Otsu, Japan). The relative expression levels were determined by the 2−ΔΔCt method.

Gene ontology and pathway analysis

We used GO analysis (http://www.geneontology.org/) to characterize circRNA-hosting and miRNA-hosting genes. GO terms provide information about the biological processes, cellular components and metabolic pathways that genes involved in. Meanwhile, the KEGG (http://www.kegg.jp/) pathway analysis was also performed to obtain insight into the molecular interactions of the differential expression circRNAs and miRNAs [73,74,75]. Targetscan (https://www.targetscan.org/vert_80/), miRTarBase (https://maayanlab.cloud/Harmonizome/dataset/MiRTarBase+microRNA+Targets) and miRT-CDS (http://www.microrna.gr/microT-CDS) were used to predict the miRNAs target genes.

Statistical analysis of data

The RNA-seq data were all performed Benjamini & Hochberg False Discovery Rate (FDR) correction. qRT-PCR data were expressed with log2 fold change value and visualized using GraphPad Prism 8 software. The circRNA-miRNA and circRNA-miRNA-mRNA interaction network were visualized using Cytoscape software. Visualization of volcano plots and bubble plots using ggplot2 and ImageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html).

Abbreviations

IMPA:

intramuscular preadipocytes

IMA:

intramuscular adipocytes

ncRNAs:

noncoding RNAs

miRNAs:

microRNAs

circRNAs:

circular RNAs

DE-circRNAs:

differentially expressed circRNAs

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GO:

Gene Ontology

qRT-PCR:

real-time fluorescent quantitative PCR.

References

  1. Li X, Fu X, Yang G, Du M. Review: enhancing intramuscular fat development via targeting fibro-adipogenic progenitor cells in meat animals. Animal. 2020;14:312–21.

    Article  CAS  Google Scholar 

  2. Alfaia CM, Lopes PA, Madeira MS, Pestana JM, Coelho D, Toldrá F, et al. Current feeding strategies to improve pork intramuscular fat content and its nutritional quality. Adv Food Nutr Res. 2019;89:53–94.

    Article  CAS  Google Scholar 

  3. Sarantopoulos CN, Banyard DA, Ziegler ME, Sun B, Shaterian A, Widgerow AD. Elucidating the Preadipocyte and its role in adipocyte formation: a comprehensive review. Stem Cell Rev Rep. 2018;14:27–42.

    Article  CAS  Google Scholar 

  4. Jiang R, Li H, Yang J, Shen X, Song C, Yang Z, et al. circRNA profiling reveals an abundant circFUT10 that promotes adipocyte proliferation and inhibits adipocyte differentiation via sponging let-7. Mol Ther Nucleic Acids. 2020;20:491–501.

    Article  CAS  Google Scholar 

  5. Jin S-S, Lin C-J, Lin X-F, Zheng J-Z, Guan H-Q. Silencing lncRNA NEAT1 reduces nonalcoholic fatty liver fat deposition by regulating the miR-139-5p/c-Jun/SREBP-1c pathway. Ann Hepatol. 2021;27:100584.

    Article  Google Scholar 

  6. Pan Y, Hui X, Hoo RLC, Ye D, Chan CYC, Feng T, et al. Adipocyte-secreted exosomal microRNA-34a inhibits M2 macrophage polarization to promote obesity-induced adipose inflammation. J Clin Invest. 2019;129:834–49.

    Article  Google Scholar 

  7. Shi C, Huang F, Gu X, Zhang M, Wen J, Wang X, et al. Adipogenic miRNA and meta-signature miRNAs involved in human adipocyte differentiation and obesity. Oncotarget. 2016;7:40830–45.

    Article  Google Scholar 

  8. Chan JJ, Tay Y. Noncoding RNA:RNA regulatory networks in Cancer. Int J Mol Sci. 2018;19:E1310.

    Article  Google Scholar 

  9. Zhang P, Wu W, Chen Q, Chen M. Non-coding RNAs and their integrated networks. J Integr Bioinform. 2019;16(3):20190027.

    Article  Google Scholar 

  10. Gao Y, Cao Y, Cui X, Wang X, Zhou Y, Huang F, et al. miR-199a-3p regulates brown adipocyte differentiation through mTOR signaling pathway. Mol Cell Endocrinol. 2018;476:155–64.

    Article  CAS  Google Scholar 

  11. Wang L, Zhang S, Cheng G, Mei C, Li S, Zhang W, et al. MiR-145 reduces the activity of PI3K/Akt and MAPK signaling pathways and inhibits adipogenesis in bovine preadipocytes. Genomics. 2020;112:2688–94.

    Article  CAS  Google Scholar 

  12. Xiao F, Tang C-Y, Tang H-N, Wu H-X, Hu N, Li L, et al. Long non-coding RNA 332443 inhibits Preadipocyte differentiation by targeting Runx1 and p38-MAPK and ERK1/2-MAPK signaling pathways. Front Cell Dev Biol. 2021;9:663959.

    Article  Google Scholar 

  13. Qiu J, Huang G, Na N, Chen L. MicroRNA-214-5p/TGF-β/Smad2 signaling alters adipogenic differentiation of bone marrow stem cells in postmenopausal osteoporosis. Mol Med Rep. 2018;17:6301–10.

    CAS  Google Scholar 

  14. Liu R, Li N, Lin Y, Wang M, Peng Y, Lewi K, et al. Glucagon like Peptide-1 promotes adipocyte differentiation via the Wnt4 mediated sequestering of Beta-catenin. PLoS One. 2016;11:e0160212.

    Article  Google Scholar 

  15. Matboli M, Gadallah SH, Rashed WM, Hasanin AH, Essawy N, Ghanem HM, et al. mRNA-miRNA-lncRNA regulatory network in nonalcoholic fatty liver disease. Int J Mol Sci. 2021;22:6770.

    Article  CAS  Google Scholar 

  16. Guo J, Zhao W, Zhan S, Li L, Zhong T, Wang L, et al. Identification and expression profiling of miRNAome in goat longissimus dorsi muscle from prenatal stages to a neonatal stage. PLoS One. 2016;11:e0165764.

    Article  Google Scholar 

  17. Du Y, Wang Y, Xu Q, Zhu J, Lin Y. TMT-based quantitative proteomics analysis reveals the key proteins related with the differentiation process of goat intramuscular adipocytes. BMC Genomics. 2021;22:417.

    Article  CAS  Google Scholar 

  18. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  19. Kleaveland B, Shi CY, Stefano J, Bartel DP. A network of noncoding regulatory RNAs acts in the mammalian brain. Cell. 2018;174:350–362.e17.

    Article  CAS  Google Scholar 

  20. Anastasiadou E, Jacob LS, Slack FJ. Non-coding RNA networks in cancer. Nat Rev Cancer. 2018;18:5–18.

    Article  CAS  Google Scholar 

  21. Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.

    Article  Google Scholar 

  22. Mohanapriya R, Akshaya RL, Selvamurugan N. A regulatory role of circRNA-miRNA-mRNA network in osteoblast differentiation. Biochimie. 2022;193:137–47.

    Article  CAS  Google Scholar 

  23. Singh D, Kesharwani P, Alhakamy NA, Siddique HR. Accentuating CircRNA-miRNA-transcription factors Axis: a conundrum in Cancer research. Front Pharmacol. 2021;12:784801.

    Article  CAS  Google Scholar 

  24. Li J-J, Xie D. Cleavage of focal adhesion kinase (FAK) is essential in adipocyte differentiation. Biochem Biophys Res Commun. 2007;357:648–54.

    Article  CAS  Google Scholar 

  25. Xiong Y, Wang Y, Xu Q, Li A, Yue Y, Ma Y, et al. LKB1 regulates goat intramuscular Adipogenesis through focal adhesion pathway. Front Physiol. 2021;12:755598.

    Article  Google Scholar 

  26. Wang T, Zhong D, Qin Z, He S, Gong Y, Li W, et al. miR-100-3p inhibits the adipogenic differentiation of hMSCs by targeting PIK3R1 via the PI3K/AKT signaling pathway. Aging (Albany NY). 2020;12:25090–100.

    Article  CAS  Google Scholar 

  27. Scheja L, Heeren J. The endocrine function of adipose tissues in health and cardiometabolic disease. Nat Rev Endocrinol. 2019;15:507–24.

    Article  CAS  Google Scholar 

  28. Audano M, Pedretti S, Caruso D, Crestani M, De Fabiani E, Mitro N. Regulatory mechanisms of the early phase of white adipocyte differentiation: an overview. Cell Mol Life Sci. 2022;79:139.

    Article  CAS  Google Scholar 

  29. Carmona JM, Lopez-Bote CJ, Daza A, Rey AI. Fat accumulation, fatty acids and melting point changes in broiler chick abdominal fat as affected by time of dietary fat feeding and slaughter age. Br Poult Sci. 2019;60:219–28.

    Article  CAS  Google Scholar 

  30. Kouba M, Sellier P. A review of the factors influencing the development of intermuscular adipose tissue in the growing pig. Meat Sci. 2011;88:213–20.

    Article  CAS  Google Scholar 

  31. Van Der Merwe DA, Brand TS, Hoffman LC. Precision finishing of south African lambs in feedlots: a review. Trop Anim Health Prod. 2020;52:2769–86.

    Article  Google Scholar 

  32. Cristancho AG, Lazar MA. Forming functional fat: a growing understanding of adipocyte differentiation. Nat Rev Mol Cell Biol. 2011;12:722–34.

    Article  CAS  Google Scholar 

  33. Rosen ED, MacDougald OA. Adipocyte differentiation from the inside out. Nat Rev Mol Cell Biol. 2006;7:885–96.

    Article  CAS  Google Scholar 

  34. Logan CY, Nusse R. The Wnt signaling pathway in development and disease. Annu Rev Cell Dev Biol. 2004;20:781–810.

    Article  CAS  Google Scholar 

  35. Moldes M, Zuo Y, Morrison RF, Silva D, Park B-H, Liu J, et al. Peroxisome-proliferator-activated receptor gamma suppresses Wnt/beta-catenin signalling during adipogenesis. Biochem J. 2003;376(Pt 3):607–13.

    Article  CAS  Google Scholar 

  36. Liu M, Qin J, Cong J, Yang Y. Chlorogenic acids inhibit Adipogenesis: implications of Wnt/β-catenin signaling pathway. Int J Endocrinol. 2021;2021:2215274.

    Article  Google Scholar 

  37. Sun L, Xie H, Mori MA, Alexander R, Yuan B, Hattangadi SM, et al. Mir193b-365 is essential for brown fat differentiation. Nat Cell Biol. 2011;13:958–65.

    Article  CAS  Google Scholar 

  38. Teo BKK, Wong ST, Lim CK, Kung TYS, Yap CH, Ramagopal Y, et al. Nanotopography modulates mechanotransduction of stem cells and induces differentiation through focal adhesion kinase. ACS Nano. 2013;7:4785–98.

    Article  CAS  Google Scholar 

  39. Mathieu PS, Loboa EG. Cytoskeletal and focal adhesion influences on mesenchymal stem cell shape, mechanical properties, and differentiation down osteogenic, adipogenic, and chondrogenic pathways. Tissue Eng Part B Rev. 2012;18:436–44.

    Article  CAS  Google Scholar 

  40. Luo W, Shitaye H, Friedman M, Bennett CN, Miller J, MacDougald OA, et al. Disruption of cell-matrix interactions by heparin enhances mesenchymal progenitor adipocyte differentiation. Exp Cell Res. 2008;314:3382–91.

    Article  CAS  Google Scholar 

  41. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet. 2014;15:7–21.

    Article  CAS  Google Scholar 

  42. Wang L, Liang W, Wang S, Wang Z, Bai H, Jiang Y, et al. Circular RNA expression profiling reveals that circ-PLXNA1 functions in duck adipocyte differentiation. PLoS One. 2020;15:e0236069.

    Article  CAS  Google Scholar 

  43. Yu W, Chen Z, Zhang J, Zhang L, Ke H, Huang L, et al. Critical role of phosphoinositide 3-kinase cascade in adipogenesis of human mesenchymal stem cells. Mol Cell Biochem. 2008;310:11–8.

    Article  CAS  Google Scholar 

  44. Aubin D, Gagnon A, Sorisky A. Phosphoinositide 3-kinase is required for human adipocyte differentiation in culture. Int J Obes. 2005;29:1006–9.

    Article  CAS  Google Scholar 

  45. Yu F, Zhang Y, Wang Z, Gong W, Zhang C. Hsa_circ_0030042 regulates abnormal autophagy and protects atherosclerotic plaque stability by targeting eIF4A3. Theranostics. 2021;11:5404–17.

    Article  CAS  Google Scholar 

  46. Ding Z, Sun D, Han J, Shen L, Yang F, Sah S, et al. Novel noncoding RNA CircPTK2 regulates lipolysis and adipogenesis in cachexia. Mol Metab. 2021;53:101310.

    Article  CAS  Google Scholar 

  47. Zhang S, Jiang E, Kang Z, Bi Y, Liu H, Xu H, et al. CircRNA profiling reveals an abundant circBDP1 that regulates bovine fat development by sponging miR-181b/miR-204 targeting Sirt1/TRARG1. J Agric Food Chem. 2022;70:14312–28.

    Article  CAS  Google Scholar 

  48. Xia Y, Zhang Y, Wang H. Upregulated lncRNA HCG18 in patients with non-alcoholic fatty liver disease and its regulatory effect on insulin resistance. Diabetes Metab Syndr Obes. 2021;14:4747–56.

    Article  CAS  Google Scholar 

  49. Ma J, Lin Y, Zhu J, Huang K, Wang Y. MiR-26b-5p regulates the preadipocyte differentiation by targeting FGF21 in goats. In Vitro Cell Dev Biol Anim. 2021;57:257–63.

    Article  CAS  Google Scholar 

  50. Hou Y, Fu L, Li J, Li J, Zhao Y, Luan Y, et al. Transcriptome analysis of potential miRNA involved in Adipogenic differentiation of C2C12 myoblasts. Lipids. 2018;53:375–86.

    Article  CAS  Google Scholar 

  51. Cai R, Zhang Q, Wang Y, Yong W, Zhao R, Pang W. Lnc-ORA interacts with microRNA-532-3p and IGF2BP2 to inhibit skeletal muscle myogenesis. J Biol Chem. 2021;296:100376.

    Article  CAS  Google Scholar 

  52. Ge J, Zhu J, Xia B, Cao H, Peng Y, Li X, et al. miR-423-5p inhibits myoblast proliferation and differentiation by targeting Sufu. J Cell Biochem. 2018;119:7610–20.

    Article  CAS  Google Scholar 

  53. Dogan S, Cicekdal MB, Ozorhan U, Karabiyik G, Kazan BT, Ekici ID, et al. Roles of adiponectin and leptin signaling-related microRNAs in the preventive effects of calorie restriction in mammary tumor development. Appl Physiol Nutr Metab. 2021;46:866–76.

    Article  CAS  Google Scholar 

  54. Lien G-S, Liu J-F, Chien M-H, Hsu W-T, Chang T-H, Ku C-C, et al. The ability to suppress macrophage-mediated inflammation in orbital fat stem cells is controlled by miR-671-5p. Stem Cell Res Ther. 2014;5:97.

    Article  Google Scholar 

  55. Zhang Z, Meng Y, Gao F, Xiao Y, Zheng Y, Wang H-Q, et al. TGF-β1-mediated FDNCR1 regulates porcine Preadipocyte differentiation via the TGF-β signaling pathway. Animals (Basel). 2020;10:E1399.

    Article  Google Scholar 

  56. Bu S, Yuan CY, Xue Q, Chen Y, Cao F. Bilobalide suppresses Adipogenesis in 3T3-L1 adipocytes via the AMPK signaling pathway. Molecules. 2019;24:E3503.

    Article  Google Scholar 

  57. Homan EP, Brandão BB, Softic S, El Ouaamari A, O’Neill BT, Kulkarni RN, et al. Differential roles of FOXO transcription factors on insulin action in brown and white adipose tissue. J Clin Invest. 2021;131:e143328.

    Article  CAS  Google Scholar 

  58. Kinney BP, Qiao L, Levaugh JM, Shao J. B56alpha/protein phosphatase 2A inhibits adipose lipolysis in high-fat diet-induced obese mice. Endocrinology. 2010;151:3624–32.

    Article  CAS  Google Scholar 

  59. Zhao Y, Guo X, Yan S, Shi B, Sheng R. Acetate regulates milk fat synthesis through the mammalian target of rapamycin/eukaryotic initiation factor 4E signaling pathway in bovine mammary epithelial cells. J Dairy Sci. 2021;104:337–45.

    Article  CAS  Google Scholar 

  60. Hou X, Zhang Y, Li W, Hu AJ, Luo C, Zhou W, et al. CDK6 inhibits white to beige fat transition by suppressing RUNX1. Nat Commun. 2018;9:1023.

    Article  Google Scholar 

  61. Zaragosi L-E, Wdziekonski B, Villageois P, Keophiphath M, Maumus M, Tchkonia T, et al. Activin a plays a critical role in proliferation and differentiation of human adipose progenitors. Diabetes. 2010;59:2513–21.

    Article  CAS  Google Scholar 

  62. Xu Q, Lin S, Wang Y, Zhu J, Lin Y. Fibroblast growth factor 10 (FGF10) promotes the adipogenesis of intramuscular preadipocytes in goat. Mol Biol Rep. 2018;45:1881–8.

    Article  CAS  Google Scholar 

  63. Xu Q, Lin S, Li Q, Lin Y, Xiong Y, Zhu J, et al. Fibroblast growth factor 21 regulates lipid accumulation and adipogenesis in goat intramuscular adipocyte. Anim Biotechnol. 2021;32:318–26.

  64. Shang Z, Guo L, Wang N, Shi H, Wang Y, Li H. Oleate promotes differentiation of chicken primary preadipocytes in vitro. Biosci Rep. 2014;34(1):e00093.

    Article  Google Scholar 

  65. Li X, Zhang H, Wang Y, Li Y, He C, Zhu J, et al. RNA-seq analysis reveals the positive role of KLF5 in the differentiation of subcutaneous adipocyte in goats. Gene. 2022;808:145969.

    Article  CAS  Google Scholar 

  66. Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495:333–8.

    Article  CAS  Google Scholar 

  67. Gao Y, Zhang J, Zhao F. Circular RNA identification based on multiple seed matching. Brief Bioinform. 2018;19:803–10.

    Article  CAS  Google Scholar 

  68. Zhou L, Chen J, Li Z, Li X, Hu X, Huang Y, et al. Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27.3 associate with clear cell renal cell carcinoma. PLoS One. 2010;5:e15224.

    Article  CAS  Google Scholar 

  69. Thankaswamy-Kosalai S, Sen P, Nookaew I. Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics. Genomics. 2017;109:186–91.

    Article  CAS  Google Scholar 

  70. An J, Lai J, Lehman ML, Nelson CC. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res. 2013;41:727–37.

    Article  CAS  Google Scholar 

  71. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  Google Scholar 

  72. Bastide A, Yewdell JW, David A. The RiboPuromycylation method (RPM): an immunofluorescence technique to map translation sites at the sub-cellular level. Bio Protoc. 2018;8:e2669.

    Article  Google Scholar 

  73. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    Article  CAS  Google Scholar 

  74. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28:1947–51.

    Article  CAS  Google Scholar 

  75. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587–92.

Download references

Acknowledgements

We would like to thank the Beijing Novogene Technology Company for the assistance in original data analysis.

Availability of data and material

The datasets generated during the current study are available in the [NCBI] repository, (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE215297), GEO accession: GSE215297. Reviewer access token: odivygokvfsblsb.

Funding

This study was supported by grants from the National Natural Science Foundation of China (32072723), and the Sichuan Science and Technology Program (2022JDTD0030), and by the Fundamental Research Funds for the Central Universities, Southwest Minzu University (2021057).

Author information

Authors and Affiliations

Authors

Contributions

L.Y. directed the project; D.Y., L.X. and Q.X. carried out the experiment; D.Y., Z.H., W.Y., Z.J. and L.Y. analyzed the data and revised the article; D.Y. wrote the manuscript with input from all members. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Lin Yaqiu.

Ethics declarations

Ethics approval for consent to participate

The 7-day-old male Jianzhou goats were purchased from Jianyang Da Geda Animal Husbandry Co., Ltd. (Sichuan, China) and we obtained the consent from the owner for the use of animals. And the experimental animals were injected barbiturate injection into the intraperitoneal cavity at a dose of 100 mg/kg, then bled to death. The carcasses are temporarily stored in freezer and then handed over to a professional solid waste disposal company for unified disposal. The experiment in this study were complied with the requirements of the directory of the Ethical Treatment of Experimental Animals of China. The animal study was reviewed and approved by Animal Experimental Ethical Inspection Committee of Southwest Minzu University (Chengdu, Sichuan, China), and all the experiment were performed under the requirement No. 2020086. All animal experimental protocol has been carried out in accordance with relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines for the reporting of animal experiments.

Consent for publication

Not applicable.

Competing interests

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Fig. S1.

GO enrichment histogram of the DE-circRNAs source genes for cellular components. Fig. S2. GO enrichment histogram of the DE-circRNAs source genes for molecular function. Fig. S3. GO enrichment histogram of the DE-circRNAs source genes for biological process.

Additional file 2: Supplementary Material S1.

The GO terms of host genes encoding DE-circRNAs.

Additional file 3: Supplementary Material S2.

The expression of circRNAs in each sample was normalized by TPM.

Additional file 4: Supplementary Material S3.

The predicted results of miRNA-circRNAs binding pair.

Additional file 5: Supplementary Material S4.

The miR-140-3p target genes prediction results in TargetScan. Common elements in TargetScan miRTarBase miR-TCDS. The miR-671-5p target genes prediction results in TargetScan. The miR-532-3p target genes prediction results in TargetScan. The miR-500-3p target genes prediction results in TargetScan. The miR-423-5p target genes prediction results in TargetScan. The miR-26b-5p target genes prediction results in TargetScan. Common elements in TargetScan miRTarBase miR-TCDS.

Additional file 6: Supplementary Material S5.

Top 20 of the KEGG Pathway enrichment analysis.

Additional file 7: Supplementary material S6.

Schematic diagram of the experimental flow. The first horizontal arrow indicates the process of preadipocytes differentiation into adipocytes. The vertical arrows indicate the experimental flow. Diagram indicate the sample model establishment, experiment principles, data acquisition and data analysis.

Additional file 8: Supplementary Material S7.

The data quality assessment table.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, D., Xin, L., Qing, X. et al. Key circRNAs from goat: discovery, integrated regulatory network and their putative roles in the differentiation of intramuscular adipocytes. BMC Genomics 24, 51 (2023). https://doi.org/10.1186/s12864-023-09141-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-023-09141-7

Keywords

  • NcRNAs
  • Adipocyte differentiation
  • Interaction network
  • Goat