Analysis of Age-Related Circular RNA Expression Profiles in Mesenchymal Stem Cells of Rat Bone Marrow

Abstract

As multicellular organisms age, they undergo a reduction in tissue and organ function. Researchers have put forward a theory that stem cell aging is the main factor responsible for decreased tissue and organ function. The adult stem cells guarantee the maintenance and repair of adult tissues and organs. Among adult stem cells, mesenchymal stem cells (MSCs) are emerging as hopeful candidates for cell-based therapy of numerous diseases. In recent years, high-throughput sequencing technologies have evolved to identify circular RNAs (circRNAs) associated with an increasing number of diseases, such as cancer and age-related diseases. It has been reported that circRNAs can compete with microRNAs (miRNAs) to affect the stability or translation of target RNAs and further regulate gene expression at the transcriptional level. However, the role of circRNAs expressed in MSCs in aging mechanisms has not yet been deciphered. The aim of this study was to explore and analyze the expression profiles of age-related circRNAs in MSCs. In this study, bone marrow MSCs were extracted from aged and young rats and analyzed using high-throughput sequencing and bioinformatics. The reliability of high-throughput RNA sequencing was verified by quantitative real-time polymerase chain reaction. The most important circRNA functions and pathways were further selected by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. Age-related circRNAs were found in the circrNA-miRNA-mRNA interaction network. The results of high-throughput sequencing showed that 4,229 circRNAs were involved in age-related senescence of MSCs. Compared with the young group, there were 29 differentially expressed circRNAs in the aged group, of which four were upregulated and 25 were downregulated. GO analysis covered three domains: biological process (BP), cellular component (CC), and molecular function (MF). The terms assigned to the BP domain were cellular metabolic processes and cellular macromolecule metabolic processes. The identified CC terms were intracellular and intracellular part, and the identified MF terms were binding and protein binding. The top five KEGG pathways were mitophagy-animal-Rattus norvegicus, prostate cancer-Rattus norvegicus, pathways in cancer-Rattus norvegicus, lysosome-Rattus norvegicus, and autophagy-animal-Rattus norvegicus. Altogether, circRNAs may play a major role in age-related MSC senescence. This study provides new mechanistic insights into MSC senescence, possibly leading to novel therapeutic strategies for age-related diseases.

Keywords: MSCs; bioinformatics; circular RNA; high-throughput sequencing; senescence.

FIGURE 1

Detection of senescence-associated changes in MSCs. (A) Morphological alterations were observed under a phase-contrast microscope. The cell areas of MSCs in the old group were significantly increased (B), and the cell aspect ratios were clearly reduced (C). (D–E) SA-β-gal staining. As indicated by the red arrows, senescent cells were stained blue. The ratio of SA-β-gal-positive cells was much higher in MSCs obtained from old rats than those obtained from young rats. (F) RT-qPCR analyses of mRNA expression of the senescence-related factor pl6^INK4A and p21^WAF1/CIP1. MSCs obtained from old rats expressed elevated levels of pl6^INK4A and p21^WAF1/CIP1. Data indicate the mean ± SD, n = 3. **P < 0.01, ***P < 0.001 vs. Young (Y).

FIGURE 2

Results of high-throughput sequencing. (A) Expression correlation test of inter-sample. Young and old MSCs derived from 1–2- to 15–18-month-old rats were used, and three samples were contained in each group. The relativity between gene expression levels in the three samples indicated biological repetition. (B) CircRNA length distribution. It was predicted that approximately 95% of circRNAs had limited length of less than 2,000 nt. (C) Heatmap showing differential expression profiles of circRNAs between the two study groups and the homogeneity within each group. (D) Volcano map of differentially expressed circRNAs. The green and red dots in the figure represent the differentially expressed circRNAs, which are statistically significant. The upregulated circRNAs are represented by red dots, and the green dots denote the downregulated circRNAs.

FIGURE 3

RT-qPCR results. (A) RT-qPCR detected the expression of the selected circRNAs. (B) The three expression patterns of the four circRNAs are consistent with the trends of high-throughput sequencing analysis results.

FIGURE 4

Network of CircRNA–miRNA–mRNA interaction. Cytoscape was used to construct the network of two downregulated and one upregulated circRNA. Putative interactions between miRNAs and circRNAs were predicted using mirTarbase 7.0. We used mirdbV6 to predict the miRNAs’ target genes. Target scores >90 were selected. In this figure, rectangles represent mRNAs, ovals represent circRNAs, and triangles represent miRNAs.

FIGURE 5

Top 10 GO terms from BP, CC, and MF. The top 10 GO terms in each group were ranked by P-value. The top five items are cellular metabolic process, cellular macromolecule metabolic process, metabolic process, organic substance metabolic process, and primary metabolic process (from BP). Intracellular, intracellular part, membrane-bounded organelle, intracellular membrane-bounded organelle, and binding (from CC). Binding, protein binding, heterocyclic compound binding, organic cyclic compound binding, and transcription regulator activity (from MF).

FIGURE 6

KEGG analysis. KEGG pathway analysis was used to determine the involvement of mRNAs in different biological pathways. The size of each circle indicates the number of circRNAs. The color of the circle indicates the P-value. The larger the circle and the lower the P-value, indicating a richer and more meaningful path.

FIGURE 7

PPI network. STRING was used to predict protein interactions among the mRNA. The circRNAs with interaction scores >0.7 (high confidence) were chosen to construct PPI networks. Proteins with >5 relationships with other proteins are represented by ovals and may be the hub proteins in this network.