Dataset Information

Identification of differentially expressed non-coding RNAs in embryonic stem cell neural differentiation.

ABSTRACT: Protein-coding genes, guiding differentiation of ES cells into neural cells, have extensively been studied in the past. However, for the class of ncRNAs only the involvement of some specific microRNAs (miRNAs) has been described. Thus, to characterize the entire small non-coding RNA (ncRNA) transcriptome, involved in the differentiation of mouse ES cells into neural cells, we have generated three specialized ribonucleo-protein particle (RNP)-derived cDNA libraries, i.e. from pluripotent ES cells, neural progenitors and differentiated neural cells, respectively. By high-throughput sequencing and transcriptional profiling we identified several novel miRNAs to be involved in ES cell differentiation, as well as seven small nucleolar RNAs. In addition, expression of 7SL, 7SK and vault-2 RNAs was significantly up-regulated during ES cell differentiation. About half of ncRNA sequences from the three cDNA libraries mapped to intergenic or intragenic regions, designated as interRNAs and intraRNAs, respectively. Thereby, novel ncRNA candidates exhibited a predominant size of 18-30 nt, thus resembling miRNA species, but, with few exceptions, lacking canonical miRNA features. Additionally, these novel intraRNAs and interRNAs were not only found to be differentially expressed in stem-cell derivatives, but also in primary cultures of hippocampal neurons and astrocytes, strengthening their potential function in neural ES cell differentiation.

SUBMITTER: Skreka K

PROVIDER: S-EPMC3401476 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Identification of differentially expressed non-coding RNAs in embryonic stem cell neural differentiation.

Skreka Konstantinia K Schafferer Simon S Nat Irina-Roxanna IR Zywicki Marek M Salti Ahmad A Apostolova Galina G Griehl Matthias M Rederstorff Mathieu M Dechant Georg G Hüttenhofer Alexander A

Nucleic acids research 20120406 13

Protein-coding genes, guiding differentiation of ES cells into neural cells, have extensively been studied in the past. However, for the class of ncRNAs only the involvement of some specific microRNAs (miRNAs) has been described. Thus, to characterize the entire small non-coding RNA (ncRNA) transcriptome, involved in the differentiation of mouse ES cells into neural cells, we have generated three specialized ribonucleo-protein particle (RNP)-derived cDNA libraries, i.e. from pluripotent ES cells ...[more]

PMID: 22492625

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Project description:BackgroundPeriodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.Material and methodsTranscriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman's correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.ResultsA total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.ConclusionsThe 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.

Project description:Background and objectiveLong non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to compete for micro-RNAs (miRNAs) in regulation of downstream genes, various biological functions and diseases. Yet, the expression and regulation of lncRNAs in periodontitis are not fully understood. The objective of the study was to identify potential genes (lncRNA, messenger RNA [mRNA] and miRNA) involved in periodontitis, construct lncRNA-miRNA-mRNA ceRNA networks, explore gene functions and validate gene expressions.Material and methodsThe data sets for the lncRNA, mRNA and miRNA expression profiles in gingival samples from periodontally healthy subjects and chronic periodontitis patients were obtained from the Gene Expression Omnibus. The differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) and miRNAs (DEmiRNAs) were identified, and ceRNA networks were then constructed. The expression of DElncRNAs and DEmRNAs was examined by quantitative real-time polymerase chain reaction (qPCR). Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed for exploring the potential functions and biological pathways.ResultsThe GSE80715 and GSE54710 data sets were retrieved. Subsequently, 26 DElncRNAs, 436 DEmRNAs and 12 DEmiRNAs were identified (|fold change| ≥2, adjusted p < 0.05). Further bioinformatics analysis contributed to establishment of the ceRNA networks, which consisted of 10 DElncRNAs, 11 DEmiRNAs and 83 DEmRNAs. Notably, the qPCR results showed a marked decrease in the expression of lncRNA H19 and two mRNAs (NOS1 and MAPT) which further supported the identified ceRNA network. The GO results revealed that the up-regulated mRNAs were significantly enriched in inflammatory processes, whilst the down-regulated mRNAs were enriched in cellular potentials.ConclusionNon-coding RNAs are critically involved in the regulatory mechanisms in the pathogenesis of periodontitis. Further study is warranted to investigate the specific underlying genetic traits and networks.

Dataset Information

Identification of differentially expressed non-coding RNAs in embryonic stem cell neural differentiation.

Publications

Identification of differentially expressed non-coding RNAs in embryonic stem cell neural differentiation.

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