Identification of competing endogenous RNA (ceRNA) and micro-RNA profiles and regulatory networks in 4-nonylphenol-induced impairment of sertoli cells
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ABSTRACT:
ORGANISM(S): Rattus norvegicus
PROVIDER: GSE167912 | GEO | 2021/06/09
REPOSITORIES: GEO
Similar Datasets
Project description:The xenoestrogens nonylphenols (NPs), which are materials used in the plastic polymer industry, are considered endocrine disruptors in a wide range of organisms. Studies have shown that human health problems, such as infertility and reproductive toxicology, are linked with NPs. However, the mechanism by which NPs interfere with male reproduction is not fully elucidated. Here, we found that 4-NP can result in male reproductive impairment and reduce androgen receptor (AR) protein levels in rat sertoli cells in vitro and in vivo. Moreover, we performed RNA sequencing to assess the differential expression of ceRNAs in rat primary sertoli cells treated with 4-NP. Bioinformatics methods, such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database and ceRNA functional network analyses, were used to investigate the sequencing data and gain further understanding of the biological processes. Our analysis revealed a core set of mRNAs (Ar, Atf6 and Cbp), and circRNAs (circ673, circ1377, circ1789, and circPTEN) that were selected and validated by RT-qPCR. In addition, the head-to-tail splicing of circ673, circ1377, circ1789, and circPTEN was identified by Sanger sequencing. These findings provide the first insight into the ceRNA expression profiles of rat sertoli cells and reveal that ceRNAs participate in 4-NP-induced impairment of sertoli cell function, thereby indicating potential therapies for both reproductive toxicology and male infertility.
| S-EPMC8167654 | biostudies-literature
Project description:Purpose: To uncover the mechanism of 4-nonylphenol-induced impairment of sertoli cells, we used RNA sequencing technology to compare the small RNA expression differences between the control groups and the 4-nonylphenol treated groups. Sertoli cells small RNA profiles of the control groups and the 4-nonylphenol treated groups were generated by deep sequencing, in triplicate. The library quality was determined using a Bioanalyzer 2100 (Agilent). The Illumina BGISEQ platform was used for small RNA sequencing. The quality of RNA-seq reads was examined using FastQC.
2021-06-09 | GSE167909 | GEO
Project description:Purpose: To uncover the mechanism of 4-nonylphenol-induced impairment of sertoli cells, we used RNA sequencing technology to compare the lncRNA expression differences between the control groups and the 4-nonylphenol treated groups. Sertoli cells lncRNA, circRNA, mRNA profiles of the control groups and the 4-nonylphenol treated groups were generated by deep sequencing, in duplicate. The library quality was determined using a Bioanalyzer 2100 (Agilent). The Illumina hiseq X ten platform was used for lnc RNA sequencing. The quality of RNA-seq reads was examined using FastQC.
2021-06-09 | GSE167905 | GEO
Project description:Identification of competing endogenous RNA (ceRNA) and micro-RNA profiles and regulatory networks in 4-nonylphenol-induced impairment of sertoli cells
| PRJNA705486 | ENA
Project description:Identification of competing endogenous RNA (ceRNA) and micro-RNA profiles and regulatory networks in 4-nonylphenol-induced impairment of sertoli cells I
| PRJNA705483 | ENA
Project description:Identification of competing endogenous RNA (ceRNA) and micro-RNA profiles and regulatory networks in 4-nonylphenol-induced impairment of sertoli cells II
| PRJNA705488 | ENA
Project description:MicroRNAs (miRNAs) are small RNA molecules, about 22 nucleotide long, which post-transcriptionally regulate their target messenger RNAs (mRNAs). They accomplish key roles in gene regulatory networks, ranging from signaling pathways to tissue morphogenesis, and their aberrant behavior is often associated with the development of various diseases. Recently it has been experimentally shown that the way miRNAs interact with their targets can be described in terms of a titration mechanism. From a theoretical point of view titration mechanisms are characterized by threshold effect at near-equimolarity of the different chemical species, hypersensitivity of the system around the threshold, and cross-talk among targets. The latter characteristic has been lately identified as competing endogenous RNA (ceRNA) effect to mark those indirect interactions among targets of a common pool of miRNAs they are in competition for. Here we propose a stochastic model to analyze the equilibrium and out-of-equilibrium properties of a network of [Formula: see text] miRNAs interacting with [Formula: see text] mRNA targets. In particular we are able to describe in detail the peculiar equilibrium and non-equilibrium phenomena that the system displays in proximity to the threshold: (i) maximal cross-talk and correlation between targets, (ii) robustness of ceRNA effect with respect to the model's parameters and in particular to the catalyticity of the miRNA-mRNA interaction, and (iii) anomalous response-time to external perturbations.
| S-EPMC3694070 | biostudies-literature
Project description:To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.
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Project description:Pancreatic cancer (PC) is a highly malignant disease characterized by insidious onset, rapid progress, and poor therapeutic effects. The molecular mechanisms associated with PC initiation and progression are largely insufficient, hampering the exploitation of novel diagnostic biomarkers and development of efficient therapeutic strategies. Emerging evidence recently reveals that noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs) and microRNAs (miRNAs), extensively participate in PC pathogenesis. Specifically, lncRNAs can function as competing endogenous RNAs (ceRNAs), competitively sequestering miRNAs, therefore modulating the expression levels of their downstream target genes. Such complex lncRNA/miRNA/mRNA networks, namely, ceRNA networks, play crucial roles in the biological processes of PC by regulating cell growth and survival, epithelial-mesenchymal transition and metastasis, cancer stem cell maintenance, metabolism, autophagy, chemoresistance, and angiogenesis. In this review, the emerging knowledge on the lncRNA-associated ceRNA networks involved in PC initiation and progression will be summarized, and the potentials of the competitive crosstalk as diagnostic, prognostic, and therapeutic targets will be comprehensively discussed.
| S-EPMC8573238 | biostudies-literature
Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA?sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE?miRNAs), long non?coding RNAs (DE?lncRNAs) and genes (DEGs). Subsequently, lncRNA?miRNA, miRNA?gene (that is, miRNA?mRNA) and gene?gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA?mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE?lncRNAs, 917 DEGs and 30 DE?miRNAs were screened. The miRNA?mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand?receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine?cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA?206 and kallikrein?related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.
| S-EPMC6059698 | biostudies-literature