Screening and analysis of LncRNA related to skeletal muscle development in muscle development process of Tan sheep in salt pond

Abstract

The invention provides screening and analysis of LncRNA related to skeletal muscle development in the muscle development process of Tan sheep in a salt pond, and particularly relates to application of Lnc.23721 and a target gene CDKN1A thereof in regulation and control of skeletal muscle development of Tan sheep, and the sequence of the CDKN1A gene is as shown in SEQ ID NO.1. The invention further provides application of the Lnc.23721 and the target gene CDKN1A in regulation and control of skeletal muscle development of Tan sheep in the salt pond Tan sheep. Specifically, the expression level of the CDKN1A is up-regulated, the differentiation of the skeletal muscle cells and myotube formation of the Tan sheep can be inhibited, the expression level of the CDKN1A is down-regulated, and the myogenic differentiation and slow muscle fiber formation of the skeletal muscle cells of the Tan sheep can be promoted, so that the meat production performance of the Tan sheep can be improved, and the formation of tender meat is facilitated. The invention also provides the LncRNA capable of regulating and controlling the development of the skeletal muscle of the Tan sheep, and the nucleotide sequence of the LncRNA Lnc.23721 is as shown in SEQ ID NO. 2. Specifically, by down-regulating the expression level of Lnc.23721, the differentiation of the Tan sheep skeletal muscle cells can be inhibited.

Description

Technical Field

[0001] The present invention belongs to the technical field of bioinformatics, and in particular relates to the screening and analysis of LncRNA related to the muscle development process of Yanchitan sheep. Background Art

[0002] Tan sheep are a dual-purpose breed for both skin and meat, and are considered one of the highest-quality meat sheep in China and abroad. Their meat is tender, bright red in color, with evenly distributed, milky white fat and a low fat content. Its flavorful texture and less muttony than other meat sheep make it a recognized premium mutton. Tan sheep meat is low in cholesterol, with dry mutton containing over 90% protein. It is rich in a variety of trace elements and amino acids essential to the human body, particularly selenium and linoleic acid. Tan sheep are a unique breed with delicious meat and hold a key position in China's animal husbandry. Furthermore, due to the influence of hormones (primarily testosterone) in male Tan sheep, sexually mature male Tan sheep contain higher levels of unsaturated fatty acids than female Tan sheep, which negatively impacts the quality of their meat and influences consumer choice. However, female Tan sheep meat has a softer flavor, more evenly distributed fat, and a tender texture, making it more popular with consumers than male Tan sheep.

[0003] Meat production in livestock is largely determined by skeletal muscle mass. Skeletal muscle fibers, which develop from myoblasts originating from the mesoderm, undergo proliferation and myogenic differentiation, and then mature through hypertrophy and myotype transformation. Myofibers are generally classified as slow-twitch (MyHC I) and fast-twitch (MyHC IIa, MyHC IIb, and MyHC IIx). Compared to fast-twitch fibers, slow-twitch fibers are characterized by a thinner diameter, brighter flesh, a predominantly aerobic metabolism, and a higher mitochondrial content. The slow-twitch phenotype is regulated by multiple factors, including AMPK, MEF2C, and PGC1α. In recent years, advances in molecular biology techniques and the construction of high-density gene maps have led to a deeper understanding of the processes and regulation of myofiber growth and development at the molecular level. Several genes have been identified that play key roles in myofiber growth and development. In addition to numerous protein-coding genes such as PAX7, MYOG, MYOD, and MYHC, a large number of non-coding RNAs also play a crucial role in muscle development.

[0004] Long non-coding RNA (lncRNA) is a novel class of regulatory RNA, typically defined as transcribed RNAs ranging in size from 200 to 100 kbp that are not translated into proteins. A wealth of evidence indicates that lncRNAs play a key regulatory role in a variety of biological processes and diseases, such as skeletal muscle development and muscle diseases. While research on the functions of lncRNAs has been intense in recent years, only a small number of lncRNAs have been clearly characterized, necessitating an urgent need to further characterize and explore lncRNAs. Currently available lncRNA functional analysis tools are mostly applicable to model organisms such as humans, mice, and Arabidopsis thaliana, making reliable predictions about lncRNAs in non-model organisms such as sheep difficult. Furthermore, as sheep are a vital livestock species for human production and life, scholars' understanding of the functional mechanisms of lncRNAs in sheep remains limited, and studies, such as those aimed at predicting and validating ceRNA regulatory networks, are often conducted based on whole-transcriptome data. However, the amount of whole transcriptome data to be analyzed is large and has certain analytical difficulties, and not all LncRNAs function through the ceRNA network. Therefore, new methods are still needed to explore the action mechanisms of LncRNAs other than ceRNAs in order to more comprehensively annotate the sheep genome. Summary of the Invention

[0005] The first object of the present invention is to provide an application of the CDKN1A gene in regulating the development of skeletal muscle of Tan sheep.

[0006] Furthermore, the nucleotide sequence of the CDKN1A gene is shown in SEQ ID NO.1.

[0007] Furthermore, increasing the expression level of CDKN1A can inhibit the differentiation of skeletal muscle cells of Tan sheep; reducing the expression level of CDKN1A can promote the differentiation of skeletal muscle cells and the formation of slow muscle fibers in Tan sheep, inhibit the expression of fast muscle fibers, improve the meat production performance of Tan sheep, and make the meat of Tan sheep more tender.

[0008] The second object of the present invention is to provide a LncRNA capable of regulating the development of skeletal muscle of Tan sheep, wherein the LncRNA is LncRNA Lnc.23721, and the nucleotide sequence of the LncRNA Lnc.23721 is shown in SEQ ID NO.2.

[0009] The third object of the present invention is to provide the application of the aforementioned LncRNA in regulating the development of skeletal muscle of Tan sheep.

[0010] Furthermore, knockdown of Lnc.23721 could significantly inhibit the myogenic differentiation of Tan sheep skeletal muscle cells.

[0011] The fourth object of the present invention is to provide siRNA for knocking down CDKN1A, wherein the siRNA is siR-CDKN1A, the sense strand sequence is shown in SEQ ID NO.5, and the antisense strand sequence is shown in SEQ ID NO.6.

[0012] The fifth object of the present invention is to provide siRNA for knocking down Lnc.23721, wherein the siRNA is siR-3, the sense strand sequence is shown in SEQ ID NO.3, and the antisense strand sequence is shown in SEQ ID NO.4.

[0013] The sixth object of the present invention is to provide a method for screening LncRNAs related to meat quality in Yanchi Beach Sheep, the method comprising the following steps:

[0014] S1. Sample sequencing or download clean data of transcriptome data of the desired species from public databases;

[0015] S2. Use cutadapt for quality control to filter low-quality data and adapters in the reads, and then use FastQC to verify the sequence quality;

[0016] S3. Use Hisat2 to align all sample reads to the reference genome of the target species. Count the reads aligned to the reference genome and their regional distribution in the Clean Data according to the GTF file.

[0017] S4. Mapped reads for each sample were assembled using the default parameters of String Tie software. The transcriptome was then reconstructed using gffcompare software. The expression levels of all transcripts were estimated using String Tie and ballgown. The expression abundance of each transcript was expressed by calculating FPKM (fragment per kilobase of transcript per million mapped reads).

[0018] S5. After filtering known mRNAs, lncRNAs, and transcripts smaller than 200 bp, we used CPC 0.9-r2 and CNCI 2.0 default parameters to predict novel transcripts with coding potential. We retained all transcripts with CPC scores below 0.5 and CNCI scores below 0, and determined the intersection of the two software results as predicted novel lncRNAs.

[0019] S6. DESeq2 and edgeR software were used to compare the differential expression of transcripts between the two groups of samples and between any two samples, respectively. The p-value calculation model based on the negative binomial distribution was used for P value calculation, and the BH

[30] P-value correction was performed to obtain the false discovery rate (FDR) value (also known as Q value or P.adj value). The fold change (FC) was calculated by dividing the mean expression level of the two groups. mRNAs / lncRNAs with Q < 0.05 and FC ≥ 2 or ≤ 0.5 (|log2fc| > = 1 & Q < 0.05) were considered differentially expressed mRNAs / lncRNAs. GO and KEGG enrichment analysis was performed on the sequenced mRNAs to clarify the functions of each protein-coding gene.

[0020] S7. Download the promoter sequence of the target species reference genome from the ENSEMBL genome browser and use the NCBI local version of BLAST to align the sequenced lncRNA sequence with the promoter sequence. Use the IDHistory Converter tool on the ENSEMBL website to convert the ID to a gene symbol and export the GO terms corresponding to each protein-coding gene. Select target genes and lncRNAs based on the GO terms corresponding to each protein-coding gene.

[0021] S8. Use the ENSEMBL genome browser to download the 2000 bp upstream sequence of the target gene's transcription start site as the gene's promoter sequence. Use blastn in the NCBI online BLAST tool to re-align the target LncRNA and target gene promoter sequences and visualize the results.

[0022] S9. Use MEGA11 to align sequences and determine the similarity of target gene promoter sequences, target lncRNA, and target gene mature mRNA sequences. Simultaneously add the mature mRNA sequence of a gene from the same family as the target gene and a gene with lower similarity to assist in the determination. Draw an unrooted dendrogram or phylogenetic tree to visually display the similarity between sequences.

[0023] S10. Use RNAfold to predict the secondary structure of the target LncRNA to determine whether its structure is stable and whether there is a miRNA precursor;

[0024] S11. Comprehensively analyze the analysis results of three softwares, BLAST, MEGA11, and RNAfold, to infer their mechanism of action and clarify the regulatory mode of the target LncRNA.

[0025] Furthermore, FPKM in S4 = Total_Exon_Fragments / [Mapped_Reads (million) × Exon_Length (KB)].

[0026] Based on the above method, the present invention predicted a LncRNA related to meat quality during the muscle development of Tan sheep, namely LncRNA Lnc.23721, the details of which are as follows:

[0027] RNA-seq was performed on longissimus dorsi muscle samples from unrelated female Tan sheep raised under the same conditions at 0, 60, 180, and 365 days old, with three replicates per group. A total of 17,617 lncRNAs were identified, including 983 known lncRNAs and the remainder newly predicted. Further analysis of the sequenced mRNA revealed significant differences in genes involved in muscle development, glucose metabolism, and lipid metabolism during Tan sheep development. This suggests that unique lncRNAs regulate meat quality during Tan sheep development.

[0028] All lncRNA sequences obtained by sequencing were aligned to the promoter sequences of the sheep reference genome downloaded from the ENSEMBL genome browser. The promoters of 256 protein-coding genes (including CDKN1A, HACD1, MYOG, and MYF6) were found to potentially bind to lncRNAs. Genes related to muscle development and myofiber type were screened based on the GO terms associated with each protein-coding gene. These genes were then compared with lncRNA sequences identified in local BLAST results and statistically analyzed. Target lncRNA and mRNA expression levels were queried. Protein-coding genes and lncRNAs with significant differences between groups, with expression levels greater than 10 in all four groups, and expression levels greater than 1 in all four groups, were identified as target genes and lncRNAs related to Tan sheep muscle development, namely CDKN1A and Lnc.23721. The sequence of Lnc.23721 is shown in SEQ ID NO. 2.

[0029] Compared with the prior art, the LncRNA screening and analysis method of the present invention has the following advantages:

[0030] This method proposes a new approach for predicting lncRNA function: after obtaining all lncRNA sequences, they are aligned with promoter sequences in a reference genome. Protein-coding genes in the alignment results are annotated, and genes relevant to the research objective are screened based on the GO terms involved in each protein-coding gene. After compiling the alignment results between their promoters and lncRNAs, the desired target genes and lncRNAs are selected based on expression levels or other criteria. The NCBI online version of the BLAST tool is then used to intuitively display the alignment between the target gene promoter sequence and the target lncRNA, along with information such as the score and expected value. MEGA11 and RNAfold are used to analyze the similarity and structural stability between the target lncRNA and the target gene promoter. The results of these three analysis tools are combined to determine the possible regulatory mechanism between the target lncRNA and the target gene.

[0031] In summary, this method analyzed the action mode of lncRNA and its potential target genes from multiple aspects. The results of the three analysis software confirmed each other, with high reliability and easy operation. The amount of data processed was smaller than that of the whole transcriptome, which made up for the deficiency of online tools or databases in predicting lncRNA target genes or target miRNAs in non-model organisms. It also supplemented people's prediction methods for the action mechanism of lncRNA other than ceRNA. In addition, the present invention proposed for the first time the lncRNA Lnc.23721 and its target gene CDKN1A that regulate skeletal muscle development during muscle development of female Tan sheep. This lncRNA may regulate the expression level of CDKN1A by competing with the promoter sequence of CDKN1A, thereby regulating the meat quality of Tan sheep skeletal muscle. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a visual display of the NCBI online BLAST tool for the CDKN1A promoter sequence and the matching region of Lnc.23721. Figure 2 This is an unrooted tree diagram of MEGA11 based on the Lnc.23721 sequence, CDKN1A promoter sequence, CDKN1A mature mRNA sequence, IGFBP5 mature mRNA sequence and CDK2 mature mRNA sequence.

[0033] Figure 3 This is the prediction result of RNAfold for the secondary structure of Lnc.23721.

[0034] Figure 4 This is the expression change trend of CDKN1A and Lnc.23721 after transfection of four siRNAs and the transfection efficiency of the four siRNAs.

[0035] Figure 5To investigate the effect of knocking down Lnc.23721 on the differentiation status of Tan sheep skeletal muscle cells.

[0036] Figure 6 To investigate the effect of knocking down Lnc.23721 on myogenic differentiation and the expression levels of four muscle fiber types.

[0037] Figure 7 This is the effect of siR-CDKN1A transfection on the expression level of CDKN1A.

[0038] Figure 8 To investigate the effect of knocking down CDKN1A on cell differentiation status.

[0039] Figure 9 To investigate the effect of knocking down CDKN1A on the expression of myogenic differentiation marker genes.

[0040] Figure 10 To investigate the effect of knocking down CDKN1A on the expression of myogenic differentiation marker proteins.

[0041] Figure 11 To investigate the effect of knocking down CDKN1A on the expression of genes encoding fast and slow muscles.

[0042] Figure 12 To investigate the effect of knocking down CDKN1A on the expression of fast and slow muscle fiber proteins.

[0043] Figure 13 To investigate the effect of knocking down CDKN1A on the expression of key genes regulating slow muscle. DETAILED DESCRIPTION

[0044] The present invention is further explained below with reference to the following examples, but the examples do not limit the present invention in any form.

[0045] The experimental methods in the following examples are conventional methods unless otherwise specified, and the experimental materials used in the following examples are commercially available unless otherwise specified.

[0046] Example 1 Application of the method for predicting and screening the function of LncRNA related to skeletal muscle development in female Tan sheep during development

[0047] (1) The longissimus dorsi muscle samples of unrelated female Tan sheep were collected at 0, 60, 180, and 365 days of age under the same rearing environment. Three replicates were set up in each group. Total RNA was extracted using the Trizol method and a cDNA library was constructed. The cDNA library was cloned and analyzed using the Illumina Novaseq TM 2×150 bp paired-end sequencing (PE150) was performed on a 6000 sequencer according to the protocol recommended by the supplier.

[0048] (2) Use cutadapt for quality control to filter low-quality data and adapters in the reads, and then use FastQC to verify the sequence quality to obtain valid data (Clean Data).

[0049] (3) Use Hisat2 to align all sample reads to the reference genome, and count the reads aligned to the reference genome and their regional distribution in CleanData based on the GTF file.

[0050] (4) Mapped reads for each sample were assembled using the default parameters of String Tie software. The transcriptome was then reconstructed using gffcompare software. String Tie and ballgown were used to estimate the expression levels of all transcripts, and the expression abundance of each transcript was calculated using FPKM (fragment per kilobase of transcript per million mapped reads). FPKM = Total_Exon_Fragments / [Mapped_Reads (million) × Exon_Length (KB)].

[0051] (5) After filtering out transcripts that overlapped with known mRNAs, lncRNAs, and transcripts less than 200 bp, CPC 0.9-r2 and CNCI 2.0 default parameters were used to predict novel transcripts with coding potential. All transcripts with CPC scores below 0.5 and CNCI scores below 0 were retained, and the intersection of the two software results was determined to be the predicted novel lncRNA.

[0052] (6) DESeq2 and edgeR software were used to compare the differential expression of transcripts between the two groups of samples and between any two samples. The p-value calculation model based on the negative binomial distribution was used to calculate the p-value, and the BH

[30] was used to correct the p-value to obtain the FDR value (false discovery rate, also known as Q value, P.adj value); the fold change (FC) was calculated by dividing the average expression level of the two groups. mRNA / LncRNA with Q < 0.05 and FC ≥ 2 or ≤ 0.5 (|log2fc| > = 1 & Q < 0.05) was considered as differentially expressed mRNA / LncRNA. GO and KEGG enrichment analysis was performed on the mRNAs detected in RNA-seq to clarify the functions of each gene.

[0053] (7) The promoter sequence of the sheep reference genome was downloaded from the ENSEMBL genome browser. The NCBI local version of the BLAST tool was used to align the sequenced lncRNA sequence with the promoter sequence. The ID History Converter tool on the ENSEMBL website was used to convert the ID and export the GO entry corresponding to each protein-coding gene. Target genes and target lncRNAs related to the meat quality of Tan sheep were screened based on the GO entry corresponding to each protein-coding gene. Lnc.23721 was found to be likely to bind to the promoter sequence of CDKN1A.

[0054] (8) The 2000 bp sequence upstream of the CDKN1A transcription start site was downloaded from the ENSEMBL genome browser as the promoter sequence of the gene. The blastn tool in the NCBI online version of the BLAST tool was used to re-align the Lnc.23721 and CDKN1A promoter sequences and visualize them. The alignment score was 54.7 points, and the expected value was 3×10 -10 , Per.Ident is 100%, and the reliability of the alignment result is high. LNC.17167 matched the 1175th to 1410th nt of the IGFBP5 promoter sequence, and the matching region accounts for 11% of the total length of the IGFBP5 promoter sequence. Figure 1 shown.

[0055] (9) MEGA11 was used to determine the similarity of CDKN1A promoter sequence, Lnc.23721 and CDKN1A mature mRNA sequence, and the mature mRNA sequences of IGFBP5 and CDK2 were added to assist in the judgment, and an unrooted tree diagram was drawn (e.g. Figure 2 ) to intuitively display the similarity between sequences.

[0056] The results showed that among the five aligned RNA sequences, Lnc.23721 had a high degree of similarity with the CDKN1A promoter sequence. It was speculated that Lnc.23721 may regulate the expression of CDKN1A by competing with the CDKN1A promoter.

[0057] (10) RNAfold was used to predict the secondary structure of Lnc.23721. The MFE planar structure diagram is shown in the figure below. Figure 3 .

[0058] The results showed that the MFE structure frequency was 0.00%, the cluster diversity was 1134.10, and the structural stability was poor. It is speculated that Lnc.23721 may regulate CDKN1A expression by competing with the CDKN1A promoter. This is consistent with the BLAST alignment results of LNC.123721 and the CDKN1A promoter sequence and the sequence similarity results obtained by MEGA11, suggesting that Lnc.23721 may regulate CDKN1A transcription by binding to the promoter sequence.

[0059] (11) Comprehensively analyze the analysis results of three softwares, BLAST, MEGA11 and RNAfold, infer their mechanism of action, and clarify the regulatory mode of the target LncRNA.

[0060] Example 2 Experimental verification of the effects of Lnc.23721 and its target gene CDKN1A on the differentiation of Tan sheep skeletal muscle cells

[0061] (1) Through the steps of Example 1, the present invention has obtained the relevant genes and LncRNAs that regulate the development of skeletal muscle of Tan sheep, that is, LncRNA Lnc.23721 may regulate the expression level of CDKN1A by competing with the promoter sequence of the protein-coding gene CDKN1A, thereby affecting the development of skeletal muscle of Tan sheep.

[0062] (2) Four siRNAs were designed based on the sequence of Lnc.23721. They were transfected into the logarithmic growth phase of the proliferation of Tan sheep skeletal muscle satellite cells. Cell RNA samples were collected 48 hours after transfection. The expression levels of Lnc.23721 and CDKN1A were detected by RT-qPCR to evaluate the interference efficiency of the four siRNAs. The results are as follows: Figure 4 Although siR-1 had the strongest downregulation effect on Lnc.23721, it also led to downregulation of CDKN1A expression. The expression levels of Lnc.23721 and CDKN1A in cells transfected with siR-1 were positively correlated, which was not in line with expectations. Therefore, siR-3, which had the highest transfection efficiency among the remaining three siRNAs, was selected for subsequent experiments. Its sequences are shown in SEQ ID NO.3 and SEQ ID NO.4.

[0063] siR-3 positive chain: GCCCCAGAUAGCACUGAAATT (SEQ ID NO. 3);

[0064] siR-3 antisense strand: UUUCAGUGCUAUCUGGGGCTT (SEQ ID NO. 4).

[0065] (3) After transfection of siR-Lnc.23721 in the logarithmic growth phase of Tan sheep skeletal muscle satellite cells for 48 hours, the medium was changed and the cells were cultured to 90% confluence before induction of differentiation. Samples were collected and tested at D7 of differentiation. The cell differentiation status was shown in Figure 5 The expression of myogenic differentiation marker genes MyHC, MyoD and MyoG was detected by RT-qPCR. Figure 6 (A) Western Blot assay was used to detect the expression of myogenic differentiation marker proteins MyHC, MyoG, and Myf5. Figure 6 (B) & (C). The experimental results showed that knocking down Lnc.23721 could significantly inhibit myogenic differentiation. The expression levels of MYH7, MYH2, MYH4 and MYH1, genes encoding four types of muscle fibers, were detected by RT-qPCR. Figure 6 (D) The results showed that knockdown of Lnc.23721 significantly inhibited the formation of four myofiber types.

[0066] (4) Specifically, knockdown of Lnc.23721 can significantly upregulate the expression of CDKN1A and significantly inhibit the expression of marker genes for myogenic differentiation and myotube formation.

[0067] (5) siRNA-CDKN1A was designed based on the sequence of CDKN1A. The sequence is shown in SEQ ID NO.5 and SEQ ID NO.6. The cells were transfected into the logarithmic growth phase of the proliferation stage of Tan sheep skeletal muscle satellite cells. RNA samples of the cells were collected 48 hours after transfection. The expression of CDKN1A was detected by RT-qPCR. The results are shown in Figure 5. Figure 7 , transfection of siRNA-CDKN1A can successfully knock down the expression of CDKN1A.

[0068] siR-CDKN1A positive strand: AAACGCAGGCUGAUCUGCUTT (SEQ ID NO. 5);

[0069] siR-CDKN1A antisense strand: AGCAGAUCAGCCUGCUGCGUUUTT (SEQ ID NO. 6).

[0070] (6) After transfection of siR-CDKN1A in the logarithmic growth phase of Tan sheep skeletal muscle satellite cells for 48 hours, the medium was changed and the cells were cultured to 90% confluence before induction of differentiation. Samples were collected and tested at D7 of differentiation. The cell differentiation status was shown in Figure 8 The expression levels of myogenic differentiation marker genes MyHC, MyoD and MyoG in Tan sheep were detected by RT-qPCR test. Figure 9Western Blot assay was used to detect myogenic differentiation marker proteins MyHC, Myf5 and MyoG. Figure 10 The results showed that knocking down CDKN1A expression can significantly promote myogenic differentiation. The expression levels of MYH7, the gene encoding slow muscle fiber MyHCⅠ, and MYH2, MYH4 and MYH1, the genes encoding fast muscle fiber MyHCⅡa, MyHCⅡb and MyHCⅡx, were detected by RT-qPCR. Figure 11 ; The expression levels of MYH7 and MYH4 proteins were detected by Western Blot assay. Figure 12 The expression levels of AMPK, MEF2C, and PGC1α, key genes that promote slow muscle fiber formation, were detected by RT-qPCR. Figure 13 The experimental results showed that knocking down CDKN1A significantly promoted the expression of slow muscle fibers, significantly inhibited the expression of fast muscle fibers, and significantly upregulated the expression of key genes for slow muscle formation.

[0071] (7) Specifically, knockdown of CDKN1A can significantly promote the expression of marker genes for myogenic differentiation and myotube formation, significantly promote the formation of slow muscle fibers, and significantly inhibit the formation of fast muscle fibers, which helps to improve the meat production performance of Tan sheep and form tenderer meat.

[0072] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Application of CDKN1A gene in regulating skeletal muscle development in Tan sheep.

2. The use according to claim 1, characterized in that The nucleotide sequence of the CDKN1A gene is shown in SEQ ID NO.

1.

3. The use according to claim 1, characterized in that Increasing the expression level of CDKN1A can inhibit the differentiation of skeletal muscle cells of Tan sheep; reducing the expression level of CDKN1A can promote the differentiation of skeletal muscle cells and the formation of slow muscle fibers in Tan sheep, inhibit the expression of fast muscle fibers, improve the meat production performance of Tan sheep, and make the meat of Tan sheep more tender.

4. LncRNA capable of regulating skeletal muscle development in Tan sheep, characterized by: The LncRNA is LncRNA Lnc.23721, and the nucleotide sequence of the LncRNA Lnc.23721 is shown in SEQ ID NO.

2.

5. Use of the LncRNA according to claim 4 in regulating skeletal muscle development in Tan sheep.

6. The use according to claim 5, characterized in that Knockdown of Lnc.23721 can significantly inhibit the myogenic differentiation of Tan sheep skeletal muscle cells.

7. siRNA for knocking down CDKN1A, characterized in that The siRNA is siR-CDKN1A, the sense strand sequence is shown in SEQ ID NO.5, and the antisense strand sequence is shown in SEQ ID NO.

6.

8. siRNA for knocking down Lnc.23721, characterized in that The siRNA is siR-3, the sense strand sequence is shown in SEQ ID NO.3, and the antisense strand sequence is shown in SEQ ID NO.

4.

9. A method for screening LncRNAs related to meat quality in Yanchi Beach Sheep, characterized in that: The method comprises the following steps: S1. Sample sequencing or download clean data of transcriptome data of the desired species from public databases; S2. Use cutadapt for quality control to filter low-quality data and adapters in the reads, and then use FastQC to verify the sequence quality; S3. Use Hisat2 to align all sample reads to the reference genome of the target species. Count the reads aligned to the reference genome and their regional distribution in CleanData based on the GTF file. S4. Mapped reads for each sample were assembled using the default parameters of String Tie software. The transcriptome was then reconstructed using gffcompare software. The expression levels of all transcripts were estimated using String Tie and ballgown. The expression abundance of each transcript was expressed by calculating FPKM (fragment per kilobase of transcript per million mapped reads). S5. After filtering known mRNAs, lncRNAs, and transcripts smaller than 200 bp, we used CPC 0.9-r2 and CNCI 2.0 default parameters to predict novel transcripts with coding potential. We retained all transcripts with CPC scores below 0.5 and CNCI scores below 0, and determined the intersection of the two software results as predicted novel lncRNAs. S6. Use DESeq2 and edgeR software to compare differential expression of transcripts between two groups of samples and between any two samples, respectively. P-values ​​were calculated using a negative binomial distribution-based p-value calculation model and corrected using BH to obtain the false discovery rate (FDR) (also known as Q-value or P.adj value). The fold change (FC) was calculated by dividing the mean expression level of the two groups. mRNAs / lncRNAs with Q < 0.05 and FC ≥ 2 or ≤ 0.5 (|log2fc| > = 1 & Q < 0.05) were considered differentially expressed mRNAs / lncRNAs. Gene oncogene (GO) and KEGG enrichment analyses were performed on sequenced mRNAs to clarify the functions of individual protein-coding genes. S7. Download the promoter sequence of the target species reference genome from the ENSEMBL genome browser and use the NCBI local version of BLAST to align the sequenced lncRNA sequence with the promoter sequence. Use the ID History Converter tool on the ENSEMBL website to convert the ID to a gene symbol and export the GO terms corresponding to each protein-coding gene. Select target genes and lncRNAs based on the GO terms corresponding to each protein-coding gene. S8. Use the ENSEMBL genome browser to download the 2000 bp upstream sequence of the target gene's transcription start site as the gene's promoter sequence. Use blastn in the NCBI online BLAST tool to re-align the target LncRNA and target gene promoter sequences and visualize the results. S9. Use MEGA11 to align sequences and determine the similarity of target gene promoter sequences, target lncRNA, and target gene mature mRNA sequences. Simultaneously add the mature mRNA sequence of a gene from the same family as the target gene and a gene with lower similarity to assist in the determination. Draw an unrooted dendrogram or phylogenetic tree to visually display the similarity between sequences. S10. Use RNAfold to predict the secondary structure of the target LncRNA to determine whether its structure is stable and whether there is a miRNA precursor; S11. Comprehensively analyze the analysis results of three softwares, BLAST, MEGA11, and RNAfold, to infer their mechanism of action and clarify the regulatory mode of the target LncRNA.

10. The screening method according to claim 5, characterized in that FPKM in S4=Total_Exon_Fragments / [Mapped_Reads(million)×Exon_Length(KB)].

Images