Dataset Information

CircRNA Role and circRNA-Dependent Network (ceRNET) in Asthenozoospermia.

ABSTRACT: The role of circRNA in reproduction is under investigation. CircRNAs are expressed in human testis, spermatozoa (SPZ), and seminal plasma. Their involvement in embryo development has also been suggested. Asthenozoospermia, a common cause of male infertility, is characterized by reduced or absent sperm motility in fresh ejaculate. While abnormal mitochondrial function, altered sperm tail, and genomic causes have been deeply investigated, the epigenetic signature of asthenozoospermic derived SPZ still remains unexplored. CircRNAs may take part in the repertoire of differentially expressed molecules in infertile men. Considering this background, we carried out a circRNA microarray, identifying a total of 9,138 transcripts, 22% of them novel based and 83.5% with an exonic structure. Using KEGG analysis, we evaluated the circRNA contribution in pathways related to mitochondrial function and sperm motility. In order to discriminate circRNAs with a differential expression in SPZ with differential morphological parameters, we separated sperm cells by Percoll gradient and analyzed their differential circRNA payload. A bioinformatic approach was then utilized to build a circRNA/miRNA/mRNA network. With the aim to demonstrate a dynamic contribution of circRNAs to the sperm epigenetic signature, we verified their modulation as a consequence of an oral amino acid supplementation, efficacious in improving SPZ motility.

SUBMITTER: Manfrevola F

PROVIDER: S-EPMC7366322 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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CircRNA Role and circRNA-Dependent Network (ceRNET) in Asthenozoospermia.

Manfrevola Francesco F Chioccarelli Teresa T Cobellis Gilda G Fasano Silvia S Ferraro Bruno B Sellitto Carolina C Marella Giovanni G Pierantoni Riccardo R Chianese Rosanna R

Frontiers in endocrinology 20200710

The role of circRNA in reproduction is under investigation. CircRNAs are expressed in human testis, spermatozoa (SPZ), and seminal plasma. Their involvement in embryo development has also been suggested. Asthenozoospermia, a common cause of male infertility, is characterized by reduced or absent sperm motility in fresh ejaculate. While abnormal mitochondrial function, altered sperm tail, and genomic causes have been deeply investigated, the epigenetic signature of asthenozoospermic derived SPZ s ...[more]

PMID: 32754116

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Project description:To explore the potential role of circular RNAs (circRNAs) in children developing very early-onset schizophrenia (VEOS). Total RNA was extracted from the plasma samples of 10 VEOS patients and eight healthy controls. Expression profiles of circRNAs, micro RNAs (miRNAs), and messenger RNAs (mRNAs) were analyzed using RNA-seq. The interaction networks between miRNAs and targets were predicted using the miRanda tool. A differentially expressed circRNA-miRNA-mRNA (ceRNA) network was further constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target mRNAs in the ceRNA network were performed to predict the potential functions of their host genes. The patient group and the control group were also compared on the regulatory patterns of circRNAs on mRNAs. 1934 circRNAs were identified from the samples and reported for the first time in schizophrenia. The circRNA expression levels were lower in the VEOS group than in the healthy control group, and 1889 circRNAs were expressed only in the control group. Differential expression analysis (i.e., log2fold change > 1.5, p 0.05) identified 235 circRNAs (1 up-regulated, 234 down-regulated), 11 miRNAs (7 up-regulated, 4 down-regulated), and 2,308 mRNAs (1906 up-regulated, 402 down-regulated) respectively. In VEOS, a ceRNA network with 10 down-regulated circRNA targets, 6 up-regulated miRNAs, and 47 down-regulated mRNAs was constructed. The target genes were involved in the membrane, the signal transduction, and the cytoskeleton and transport pathways. Finally, different expression correlation patterns of circRNA and mRNA in the network were observed between the patient group and the control group. The current research is the first to reveal the differentially expressed circRNAs in the plasma of VEOS patients. A circRNA-miRNA-mRNA network was also conducted in this study. It may be implied that the circRNAs in this network are potential diagnostic biomarkers for VEOS and they play an important role in the onset and development of VEOS symptoms.

Project description:BackgroundThe existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases.ResultsIn this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix.ConclusionsAfter 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

Dataset Information

CircRNA Role and circRNA-Dependent Network (ceRNET) in Asthenozoospermia.

Publications

CircRNA Role and circRNA-Dependent Network (ceRNET) in Asthenozoospermia.

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