lncRNA DLEU2 acts as a miR-181a sponge regulated SEPP1 (may as a biomarker for sarcopenia) to inhibit skeletal muscle differentiation and regeneration

Background Sarcopenia is a serious public health problem. The ceRNA network has been demonstrated vital in the development of skeletal muscle, but there is currently no effective method to assess the risk of sarcopenia. The purpose of this research is to create and authenticate a ceRNA pathway based on a predictive model of sarcopenia. Methods A clinical prediction model for sarcopenia was established using the RNAs (validated by clinical data) that are co-differentially expressed in the database, and a ceRNA network was constructed. The correlation analysis of each element in the ceRNA network was performed according to the clinical samples and the GTEX database, and the possible key ceRNA pathways were screened. C2C12 mouse myoblast Cells experiments were used to verify these ceRNA pathways. Findings Based on four molecular markers of SEPP1, SV2A, GOT1 and GFOD1, we developed a new model for predicting sarcopenia with well accuracy, and constructed a ceRNA network accordingly. Clinical sample showed that the expression levels of lncDLEU2, SEPP1, and miR-181a were closely related to the risk of sarcopenia. The C2C12 mouse myoblast cells were cultivated to verify that lncDLEU2 inhibits muscle proliferation and differentiation by acting as a miR-181a sponge regulated SEPP1. Interpretation Our research developed a highly accurate prediction tool for the risk of sarcopenia. These findings suggest that lncDLEU2-miR-181a-SEPP1 pathway inhibits muscle differentiation and regeneration. This pathway may uncover some new therapeutic targets for the treatment of sarcopenia caused by aging.


Introduction
Sarcopenia has be closely associated with physical disabilities as well as the risk of diabetes and fractures characterized by a generalized and gradual loss of the function and strength of skeletal muscles. [1][2][3][4][5][6][7] With the increase of age, the incidence of sarcopenia has gradually increased and has become an important factor affecting the physical health of the elderly. However, these previous researches mainly study the pathological role of the ceRNA network in sarcopenia, no quantifying method has been brought up to forecast the risk of sarcopenia; therefore, it is necessary based on several simple molecular markers to develop an reliable predictive tool and early interventions to reduce the risk of sarcopenia.
The purpose of this study was to create an effective tool for early predicting the risk of sarcopenia in patients based on several simple molecular markers. Based on this prediction model, we predicted lncRNA DLEU2 as a miR-181a sponge regulates SEPP1 protein and inhibited muscle differentiation and regeneration through biological information analysis and vitro experiments. This study would like to provide a new therapeutic target for treating sarcopenia caused by aging through detecting the lncDLEU2/miR-181a/SEPP1 pathway.

DE-mRNAs from GEO datasets
After normalization, 11 differentially expressed mRNAs were obtained from skeletal muscle samples (GSE8479, GSE1428 and GSE52699) ( Figure 1A). Multiple volcano plots of differential expression are presented in Figure 1B. For those data, SEPP1 were the co-upregulated mRNAs and GFOD1, GOT1, and SV2A were the co-downregulated mRNAs in the sarcopenia group. The expression levels of mRNAs acquired from GSE8479, GSE1428 and GSE52699 datasets are shown in Figure 1C. Our clinical data demonstrated that the predictive model had an AUC of 0.790 ( Figure   2F), suggesting that the nomogram could be used to forecast the chances of sarcopenia. The C index is 0.915 (95% CI, 0.84052-0.98948) in the training set, 0.923 (95% CI, 0.86616-0.97984)in the validation set and was validated to be 0.79(95% CI,0.66848-0.91152 ) by clinic cohort, suggesting strong discriminatory power and accurate predictive performance. The decision curve analysis of the nomogram is shown in Figure 2G. Those implied that the nomogram model can be a tool for clinical practice, including early intervention of risk factors for sarcopenia, thereby reducing the risk of sarcopenia. In total, matrix data for DE-mRNAs and predicted risk score for sarcopenia from the clinic cohort and the entire GEO database cohort were presented as heatmap and risk plot in Figure 2B and Figure 2D.
Combined with the nomogram prediction model results, these results indicate that SEPP1, GFOD1, GOT1, and SV2A may be key modulators of sarcopenia.

Expression and correlation analysis of DE-mRNAs, miR-181a and DLEU2
DE-miRNAs (SEPP1, GFOD1, GOT1, and SV2A),miR-181a and DLEU2 expression levels were examined in the muscle of 25 patients with sarcopenia and 25 patients without sarcopenia by quantitative real-time PCR ( Figure 4A). The average SEPP1 and DLEU2 expression level was meaningfully higher in the group of sarcopenia than in the group of control, while the expression of GFOD1, GOT1, SV2A and miR-181a was significantly lower in sarcopenia than the other ones.
The result of ceRNA network ( Figure 3D,E) identified that lncRNA DLEU2 acts as a sponge of miR-181a to up-regulation the expression of SEPP1 as well as in the correlation analysis. Firstly, correlation analysis of clinic cohort showed that miR-181a had a significant negative correlation with SEPP1 and DLEU2, combined with ( Figure 4D). Besides, SEPP1 also had a significant positive correlation with DLEU2 both in GTEX database and clinic cohort( Figure 4E,F). Furthermore, to understand the expression of SEPP1 and DLEU2 more intuitively, we constructed human tissue-enriched protein expression maps. SEPP1 and DLEU2 were relatively low enriched in muscle ( Figure 4B,C). Thus, miR-181a could be a protective factor, whereas DLEU2 and SEPP1 could be detrimental to skeletal development.

DLEU2 inhibited myogenic proliferation and differentiation of C2C12 myoblasts
To demonstrate the functions of DLEU2, our laboratory group constructed a lentiviral vector encoding with DLEU2 or DLEU2 shRNA, then prepared a lentivirus system for C2C12 cells infection. The results showed that in C2C12 cells transduced by lentivirus DLEU2 (Figure 5A), DLEU2 expression was highly expressed; while shDLEU2-1(shRNA-1) had the highest knockout efficiency in those C2C12 systems ( Figure 5C). Besides, the levels of DLEU2 and differentiation markers of myofibrils (MyoD and MyoG) were negative correlation as determined by quantitative RT-PCR analysis (P < 0.05, Figure 5A,C). CCK-8 and EDU assays demonstrated that treatment with DLEU2 reduced cell proliferation and the level of EDU-positive C2C12 cells (Figure. 5B,D and E). Overexpression of DLEU2 in C2C12 cells can significantly reduce the protein and mRNA levels of muscle-derived markers (MyoG and MyoD) as well as SEPP1 and inhibit the proliferation of C2C12 cells, while shDLEU2 (shRNA-1&2) promote these levels in C2C12 cells. (P < 0.05, Figure 5 B,C,F,G and H)

Validation of miR-181a targets DLEU2 in C2C12 cells
To more understand the biological mechanism of DLEU2 regulating muscle differentiation, we predict that microRNA181a (miR-181a) is one of the target miRNAs of DLEU2 and a binding site between miR-181a and DLEU2 was also predicted by using RNA hybrid 2.12 (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/) ( Figure 6A). Further study revealed that the biotinylated miR-181a specifically pulled down the expression level of DLEU2 in C2C12 cells( Figure. 6B). And the double luciferase reporting experiment showed that DLEU2 transfection could reduce the luciferase activity of miR-181a-WT, but did not affect the luciferase activity of miR-181a-Mut. At the same time, over expression of DLEU2 containing the mutant binding site(DLEU2-Mut) did not decrease the luciferase activity of microRNA181a-Mut and microRNA181a-WT ( Figure 6C). Co-transfection with microRNA181a-WT upregulation the protein and mRNA levels of muscle-derived markers but decreased the levels of SEPP1 in DLEU2 transfected C2C12 cells ( Figure 6D,E). In addition, EDU and CCK-8 assays was also performed to exam the proliferation of C2C12 cells after overexpression of DLEU2 and co-transfection with the microRNA181a mimic or microRNA181a inhibitor. Cells transfected with DLEU2 and treated with the miR-181a inhibitor showed significantly decreased proliferation compared with the others, whereas proliferation was increased in cells treated with the miR-181a mimic ( Figure 6F,G). Therefore, SEPP1 protein expression could be increased by DLEU2 infection in C2C12 cells, and miR-181a mimic decreased SEPP1 protein expression as well as promoting the muscle differentiation of C2C12 cells.

Characterization of the SEPP1 subtypes regarding different functional pathways
GO function analysis of GSEA showed remarkably effected SEPP1-related signaling functions, such as regulation of skeletal muscle tissue development, skeletal muscle tissue development, skeletal muscle organ development, skeletal muscle fiber development, skeletal muscle cell differentiation, and skeletal muscle tissue regeneration ( Figure 7A). And SEPP1-related KEGG pathways were the Endocrine resistance pathway, Glycolysis / Gluconeogenesis pathway, Inositol phosphate metabolism pathway, Oxidative phosphorylation pathway, Purine metabolism pathways and RNA degradation pathways ( Figure 7B). These results suggest that SEPP1 play a vital role in the regeneration and development of muscle as well as associated with the pathways of endocrine resistance and cellular metabolism.

DLEU2 promoted the expression of SEPP1 protein
MiRWalk and miRcode databases predict that SEPP1 are regulated by miR-181a ( Figure 8A). Furthermore, C2C12 cells co-transfected with SEPP1-WT and miR-181a showed less luciferase activity than those co-transfected with SEPP1-WT and microRNA-negative control from our further luciferase reporter assays (NC; p < 0.05) ( Figure 8D). In addition, forcing expression of miR-181a in C2C12 cells can meaningfully down-regulate Wnt5a protein expression, while miR-181a inhibitor can up-regulate Wnt5a expression ( Figure 8B,C,E). And cells treated with the miR-181a mimic showed significantly promoted proliferation compared with the others ( Figure   8F). These results indicated that miR-181a was direct regulators of SEPP1 expression in muscle. In conclusion, lncRNA DLEU2 acts as a miR-181a sponge to regulate the expression of DLEU2, thereby promoting muscle proliferation and differentiation.

Discussion
In this research, we found that lncRNA DLEU2 acts as a miR-181a sponge and inhibits skeletal muscle regeneration and differentiation. The lncDLEU2-miR-181a-SEPP1 pathway inhibits muscle differentiation and regeneration could be used as novel therapeutic targets for the treatment of sarcopenia caused by aging. In addition, this study is the first to forecast the chances of sarcopenia based on four molecular markers (SEPP1, SV2A, GOT1 and GFOD1).
Besides, the prediction model performed well based on the detection of clinical In addition, it has also been reported that SEPP1 is positively correlated with TNF-α levels, and high expression of TNF-α is characteristic of primary muscle disease and high glucose microenvironment. [46,47] In summary, when DLEU2 is knocked out or over-expressed in C2C12 cells, the expression level of miR-181a is up-regulated or down-regulated, resulting in a decrease or increase in the level of SEPP1 protein, and ultimately up or down regulation of muscle proliferation and differentiation, respectively. These data suggest that DLEU2 may interact with miR-181a to up-regulate the level of SEPP1 protein after transcription.
With the increase of age, the muscle mass of the human body gradually decreases.
The incidence of sarcopenia is 13% in the elderly aged 60-70, and it reaches 50% at the age of 80[48-50]. Accurate risk assessment will allow doctors to better assess the risk of patients' illness and facilitate communication between doctors and patients, while also preventing costly medical treatment from occurring. Therefore, we first In summary, this study found that lncRNA lncDLEU2 acts as a miR-181a sponge to regulate the SEPP1 protein, thereby inhibiting muscle proliferation and differentiation, which may be a new therapeutic target for reversing aging skeletal muscle atrophy.
And based on the four molecular markers (SEPP1, SV2A, GOT1 and GFOD1), a new prediction model with well accuracy can be developed to help clinicians predict the risk of sarcopenia.

Study participants
We identified patients residing in Shanghai, China, who underwent patellar surgery at

Methods of assessment
The diagnostic criteria established by the 2014 Asian Working Group for Sarcopenia (AWGS) and EWGSOP2 defines sarcopenia. [51,52] We used a fixed distance of 6 m, as recommended by AWGS, to measure the subject's daily walking speed. [53,54] For patients with a walking speed of ≤0.8 m/s, we used the bioelectrical impedance analysis (BIA) to assess muscle mass by using a bioimpedance meter (TANITA RD-953, Japan). The results of BIA are very similar to those of double-energy X-ray absorptiometry and magnetic resonance imaging; BIA also offers the advantages of safety, technical simplicity, low cost, and high patient compliance. [55,56] All the results of BIA have been standardized by using cross-validated Sergi.

Data retrieval
The dataset supporting the conclusions of this article is available in the GEO database

Logistic regression of sarcopenia data
The series matrixs from GEO datasets (GSE8479, GSE1428 and GSE52699 ) were downloaded. Data from skeletal muscles in the older (N = 47) and younger (N = 46) groups were analyzed using R software (version 3.5.3). The samples were randomly divided into training and validation (7: 3) groups. These analyses were put into practice by using the "caret" package to identify and evaluate models.

Analysis of data from the GTEX databases
To clarify the correlation between DElncRNAs and DEmRNAs in ceRNA. The R software (https://www.r-project.org/) with several publicly available packages was used for statistical analysis of data from the GTEX databases. A human tissueenriched protein expression map and a boxplot of genes were generated using the "gganatogram" and "ggpubr" models, respectively. For the genotypic correlation analysis, the Fisher's exact test or χ² test (two-sided) was used.

Quantitative real-time PCR (qPCR)
Total mRNA and lncRNA was isolated from cell cultures using the Mini-BEST

Western blot analysis
To evaluate protein expression, cells were harvested in RIPA buffer containing a protease inhibitor cocktail, and total protein was quantified using a bicinchoninic acid

Cell transfection
According to a previously reported modification protocol, C2C12 cells were cultured to 60% confluence. [73] The culture medium was then removed and 1.5×10 8 IU virus particles were added with 8 g/mL hexadimethyl bromide ( Sigma-Aldrich, St. Louis,MO, USA). After that, DMEM + virus particles with 10% were changed to DMEM fiber channel standard and the cells were cultured for 1-7 days.

Construction of lentiviral vectors and lentivirus production
To construct the lncDLEU2 overexpression lentiviral vector, we subcloned DLEU2 and the full-length lncDLEU2 into the lentiviral GV112 vector according to the manufacturer's instructions. [74] This vector was provided by Shanghai Genechem (Shanghai, China). For the lncDLEU2-KD lentiviral vector, the shRNA subcloned of the lncDLEU2 or negative control scramble sequence was used in the GV112 carrier.

Transfection of miRNAs
Transfection of miRNAs was performed as previously described.
[76] In short, miR-181a is enhanced and inhibited using chemically synthesized miRNAs mimics and inhibitors (Gene Pharma (Shanghai, China)). According to the manufacturer's protocol (Ribobio, Guangzhou, China), 24 hours after seeding, cells were transfected using a riboFECT™ CP transfection kit for 24 hours. The transfection efficiency was measured through real-time quantitative PCR after transfection 48 hours.

Pulling down the Biotin-labeled lncDLEU2
Biotin-labeled lncDLEU2 was synthesized by Sangon Biotech (Shanghai, China According to the manufacturer's protocol, the dual luciferase reporter assay system (Promega, Madison, WI, USA) was used to detect renilla luciferase activities after 48h.

EDU assays
The C2C12 cells treated under different treatment conditions were seeded in 24-well plates at a rate of 1 × 10 5 cells / well and incubated for 24h. As previously reported basing on the manufacturer's instructions, the 5-erhynyl-20-deoxyuridine (EDU) incorporation assay was performed with an EDU assay kit(#COO75S, Beyotime Biotechnology). The proportion of EDU-positive cells was counted according to the images taking under a laser scanning confocal microscope (Olympus)[78-80].

Gene set enrichment analysis(GSEA)
GSEA is a "molecular signature database" used to investigate potential mechanisms by using the project of JAVA (http://software.broadinstitute.org/gsea/index.jsp). [81] The number of random samples was set to 1000, and the significance threshold was set to p <0.05.

Statistical analysis
Statistical analysis was using GraphPad Prism (version 7.0) software. Results are expressed as the mean ± standard deviation of three or six independent experiments.
Statistical significance used one-way analysis of variance or two-tailed t test.
Correlation analysis was performed using pear in correlation test. The difference was statistically significant, *P <0.05.
Acknowledgements: Thanks for the anonymous reviewers for their valuable comments and suggestions that helped improve the quality of our manuscript.      E. C2C12 myoblasts were treated with DLEU2 or shRNA-1/2. Cells were stained with Edu. The relative ratio of Edu+ C2C12 cells was quantified. The data represents the mean ± SD (n = 3). Different from control or NC, ** p < 0.01,***p<0.005.   ***p<0.005,****p<0.0005.

Figure 7
Gene set enrichment analysis revealed the biological pathways and processes related to SEPP1. The enrichment results show that there is a significant correlation between the high and low SEPP1 groups.
A. GO enrichment analysis; B. KEGG enrichment analysis