Identification and validation of key miRNAs and miRNA–mRNA regulatory network associated with uterine involution in postpartum Kazakh sheep
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ABSTRACT:
SUBMITTER: Yang H
PROVIDER: S-EPMC8131964 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MicroRNAs (miRNAs) are endogenous, noncoding RNAs that regulate various biological processes including adipogenesis and fat metabolism. Here, we adopted a deep sequencing approach to determine the identity and abundance of miRNAs involved in fat deposition in adipose tissues from fat-tailed (Kazakhstan sheep, KS) and thin-tailed (Tibetan sheep, TS) sheep breeds. By comparing HiSeq data of these two breeds, 539 miRNAs were shared in both breeds, whereas 179 and 97 miRNAs were uniquely expressed in KS and TS, respectively. We also identified 35 miRNAs that are considered to be putative novel miRNAs. The integration of miRNA-mRNA analysis revealed that miRNA-associated targets were mainly involved in the gene ontology (GO) biological processes concerning cellular process and metabolic process, and miRNAs play critical roles in fat deposition through their ability to regulate fundamental pathways. These pathways included the MAPK signaling pathway, FoxO and Wnt signaling pathway, and focal adhesion. Taken together, our results define miRNA expression signatures that may contribute to fat deposition and lipid metabolism in sheep.
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Project description:Uterine fibroids (UF) are the most common benign gynecologic tumors and lead to heavy menstrual bleeding, severe anemia, abdominal pain, and infertility, which seriously harm a women's health. Unfortunately, the regulatory mechanisms of UF have not been elucidated. Recent studies have demonstrated that miRNAs play a vital role in the development of uterine fibroids. As a high-throughput technology, microarray is utilized to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between UF and myometrium. We identified 373 candidate DEGs and the top 100 DEMs. Function enrichment analysis showed that candidate DEGs were mainly enriched in biological adhesion, locomotion and cell migration, and collagen-containing extracellular matrix. Subsequently, protein-protein interaction (PPI) networks are constructed to analyze the functional interaction between DEGs and screen hub DEGs. Subsequently, the expression levels of hub DEGs were validated by real-time PCR of clinical UF samples. The DGIdb database was used to select candidate drugs for hub DEGs. Molecular docking was applied to test the affinity between proteins and drugs. Furthermore, target genes for 100 candidate DEMs were predicted by miRwalk3.0. After overlapping with 373 candidate DEGs, 28 differentially expressed target genes (DEGTs) were obtained. A miRNA-mRNA network was constructed to investigate the interactions between miRNA and mRNA. Additionally, two miRNAs (hsa-miR-381-3p and hsa-miR-181b-5p) were identified as hub DEMs and validated through RT-PCR. In order to better elucidate the pathogenesis of UF and the synergistic effect between miRNA and transcription factor (TF), we constructed a miRNA-TF-mRNA regulatory network. Meanwhile, in vitro results suggested that dysregulated hub DEMs were associated with the proliferation, migration, and apoptosis of UF cells. Our findings provided a novel horizon to reveal the internal mechanism and novel targets for the diagnosis and treatment of UF.
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Project description:Prostate cancer (PC) is one of the major male cancers. Differential diagnosis of PC is indispensable for the individual therapy, i.e., Gleason score (GS) that describes the grade of cancer can be used to choose the appropriate therapy. However, the current techniques for PC diagnosis and prognosis are not always effective. To identify potential markers that could be used for differential diagnosis of PC, we analyzed miRNA-mRNA interactions and we build specific networks for PC onset and progression. Key differentially expressed miRNAs for each GS were selected by calculating three parameters of network topology measures: the number of their single regulated mRNAs (NSR), the number of target genes (NTG) and NSR/NTG. miRNAs that obtained a high statistically significant value of these three parameters were chosen as potential biomarkers for computational validation and pathway analysis. 20 miRNAs were identified as key candidates for PC. 8 out of 20 miRNAs (miR-25-3p, miR-93-3p, miR-122-5p, miR-183-5p, miR-615-3p, miR-7-5p, miR-375, and miR-92a-3p) were differentially expressed in all GS and proposed as biomarkers for PC onset. In addition, "Extracellular-receptor interaction", "Focal adhesion", and "microRNAs in cancer" were significantly enriched by the differentially expressed target genes of the identified miRNAs. miR-10a-5p was found to be differentially expressed in GS 6, 7, and 8 in PC samples. 3 miRNAs were identified as PC GS-specific differentially expressed miRNAs: miR-155-5p was identified in PC samples with GS 6, and miR-142-3p and miR-296-3p in PC samples with GS 9. The efficacy of 20 miRNAs as potential biomarkers was revealed with a Random Forest classification using an independent dataset. The results demonstrated our 20 miRNAs achieved a better performance (AUC: 0.73) than miRNAs selected with Boruta algorithm (AUC: 0.55), a method for the automated feature extraction. Studying miRNA-mRNA associations, key miRNAs were identified with a computational approach for PC onset and progression. Further experimental validations are needed for future translational development.
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Project description:BackgroundMicroRNAs (miRNAs) plays an essential role in the pathogenesis of colon cancer. This study aims to identify and verify key miRNAs that may serve as potential biomarkers for early diagnosis and treatment of colon cancer.MethodsTwo miRNA microarray datasets (GSE53339 and GSE126093) were downloaded from the Gene Expression Omnibus (GEO) database, and the common differentially expressed miRNAs (DEMis) of both were identified by the "limma" package and the intersect function in R. Then, the miRwalk online tool was used to predict the target genes of the common DEMis and perform Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on those DEMis. The miRNA-gene network was constructed using Cytoscape software to identify key miRNAs and validated using quantitative real-time PCR (qRT-PCR) and The Cancer Genome Atlas (TCGA) dataset information for experimental and external database validation, respectively. In addition, we explored the correlation between key miRNAs and infiltrating immune cells and immunotherapy biomarkers.ResultsFourteen common DEMis were identified from the GEO database, and five targeted genes were predicted. A microRNA-gene network was subsequently constructed to identify three key miRNAs (miR-363-3p, miR-520e, and miR-330-5p). Both qRT-PCR and external database validation confirmed our findings. In addition, we found that miR-520e was significantly associated with infiltrating immune cells and established immune checkpoints.ConclusionOur study identified three key miRNAs that influence the development of colon cancer. In addition, further studies suggest that infiltrating immune cells may play an essential role in the pathogenesis of colon cancer. These findings assist in early diagnosis and precision-targeted therapies.
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Project description:Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Breeds with high IMF content are often accompanied by high subcutaneous fat (SCF), severely affecting the meat rate of pigs. Studying the mechanisms of miRNAs related to lipogenesis and lipid metabolism has important implications for pig breeding. We constructed two small RNA libraries from intramuscular and subcutaneous fat to evaluate the patterns of lipogenesis in Laiwu pig, a Chinese breed. A total of 286 differentially expressed miRNAs (DEmiRNAs), including 193 known miRNA and 93 novel miRNAs, were identified from two types of adipose. GO and KEGG enrichment analysis for DEmiRNAs showed that their target genes involved in many adipogenesis and lipid metabolism biological processes and signaling pathways, such as Wnt signaling pathway,MAPK signaling pathway, Hippo signaling pathway, PI3K-Akt signaling pathway, Melanogenesis, Signaling pathways regulating pluripotency of stem cells and so on. Then, we constructed a miRNA-mRNA interaction network to find out which miRNAs were the key miRNAs of regulation in Wnt signaling pathway. In this pathway, miR-331-3p, miR-339-5p, miR-874 and novel346_mature target PPARD, WNT10B, RSPO3, WNT2B. This study provides a theoretical basis for further understanding the post-transcriptional regulation mechanism of meat quality formation and predicting and treating diseases caused by ectopic fat.
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Project description:Uterine involution, ovarian activity, and incidence of postpartum uterine disease (PUD) were assessed in forty-eight dairy cows from calving until the 10th postpartum week. Postpartum follow-up included evaluation of uterine involution and ovarian structures by B-mode, Doppler color, and Doppler spectral ultrasound of the right uterine artery in cows with no calving or postpartum uterine problems (healthy cows). Data from cows that developed PUD (PUD cows) were compared with healthy cows matched by herd and days in milk (DIM). Data were analyzed by descriptive statistics, simple regression, one-way ANOVA, or repeated ANOVA measures, and in data analysis of healthy cows, uterine horn diameter assessed by B-mode ultrasound ranged from 22.9 ± 2.4 to 19.4 ± 1.4 mm and 19.9 ± 2.2 to 20.5 ± 2.3 mm from the fourth to the seventh postpartum week in the left and right uterine horns, respectively (P > 0.05). During the study, 15 and 7 cows had corpus luteum in the left and right ovaries, respectively. The mean time for the first postpartum CL was 30.1 ± 3.2 DIM (min 8, max 67 DIM). In data analysis of PUD cows, uterine blood flow assessed by color Doppler ranged from 7.4 ± 4.0 to 43.75 ± 10.3% in cows that developed PUD compared to 16.7 ± 11.0% in healthy cows (P > 0.05). No statistically significant changes were found in resistance index, pulsatility index, time-averaged maximum velocity, time-averaged mean velocity, or diastole/systole ratio (D/S) in cows that developed PUD compared to healthy cows (P > 0.05). Finally, no correlation was found between Doppler spectral parameters and uterine involution (P > 0.05). Our data suggest that cows receiving transition diets and exhibiting normal calving undergo a rapid macroscopic uterine involution and ovarian follicular dynamics resumption. Complete ultrasound evaluation provides valuable data for assessing uterine involution in postpartum dairy cows.
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Project description:To examine the possible miRNA molecular regulatory mechanisms during maternal uterine involution after delivery, we selected ovary and uterus tissues that are structurally connected as experimental materials. We employed Illumina HiSeq sequencing to screen and analyze the quantity and characteristics of miRNA in postpartum ewes in the methylergometrine-treated group and physiological saline control group. Results showed that 16 miRNAs were identified in the ovary libraries, including 4 known miRNAs and 12 novel miRNAs. In the uterus libraries, 54 miRNAs were identified, which included 5 known miRNAs and 49 novel miRNAs. At the same time, target gene prediction, GO annotation, and KEGG signaling pathway enrichment analysis were employed. We found that maternal uterine involution after delivery may involve two miRNA-target gene pairs, i.e., miRNA-200a-ZEB1 and YAP1. The YAP1/Hippo signaling pathway is used to construct an ovary-uterine axial regulatory mechanism to regulate the restoration of postpartum maternal uterine morphology and function. In view of this, the identification of miRNAs with significant differences in this study fills a gap in research on miRNAs associated with regulation of postpartum uterine recovery in ewes and provided an important reference for comprehensive understanding and in-depth research on the regulatory molecular network mechanism for postpartum uterine involution in small ruminants.
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Project description:BackgroundOsteoarthritis (OA) is a chronic degenerative joint disease and the most frequent type of arthritis. This study aimed to identify the key miRNAs and genes associated with OA progression.MethodsThe GSE105027 (microRNA [miRNA/miR] expression profile; 12 OA samples and 12 normal samples) and GSE48556 (messenger RNA [mRNA] expression profile; 106 OA samples and 33 normal samples) datasets were selected from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and miRNAs (DEMs) were analyzed using the limma and ROCR packages in R, respectively. The target genes that negatively correlated with the DEMs were predicted, followed by functional enrichment analysis and construction of the miRNA-gene and miRNA-transcription factor (TF)-gene regulatory networks. Additionally, key miRNAs and genes were screened, and their expression levels were verified by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsA total of 1696 DEGs (739 upregulated and 957 downregulated) and 108 DEMs (56 upregulated and 52 downregulated) were identified in the OA samples. Furthermore, 56 target genes that negatively correlated with the DEMs were predicted and found to be enriched in three functional terms (e.g., positive regulation of intracellular protein transport) and three pathways (e.g., human cytomegalovirus infection). In addition, three key miRNAs (miR-98-5p, miR-7-5p, and miR-182-5p) and six key genes (murine double minute 2, MDM2; glycogen synthase kinase 3-beta, GSK3B; transmembrane P24-trafficking protein 10, TMED10; DDB1 and CUL4-associated factor 12, DCAF12; caspase 3, CASP3; and ring finger protein 44, RNF44) were screened, among which the miR-7-5p → TMED10/DCAF12, miR-98-5p → CASP3/RNF44, and miR-182-5p → GSK3B pairs were observed in the regulatory network. Moreover, the expression levels of TMED10, miR-7-5p, CASP3, miR-98-5p, GSK3B, and miR-182-5p showed a negative correlation with qRT-PCR verification.ConclusionMiR-98-5p, miR-7-5p, miR-182-5p, MDM2, GSK3B, TMED10, DCAF12, CASP3, and RNF44 may play critical roles in OA progression.
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