Identification of differentially expressed non-coding RNAs in embryonic stem cell neural differentiation
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PROVIDER: S-EPMC3479216 | biostudies-literature | 2012 Oct
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC3401476 | biostudies-literature
Project description:Macrophages display remarkable plasticity, with the ability to undergo dynamic transition between classically and alternatively activated phenotypes. Long non-coding RNAs (lncRNAs) are more than 200 nucleotides in length and play roles in various biological pathways. However, the role of lncRNAs in regulating macrophage polarization has yet to be explored. In this study, lncRNAs expression profiles were determined in human monocyte-derived macrophages (MDMs) incubated in conditions causing activation toward M(IFN-γ + LPS) or M(IL-4) phenotypes. Compared with primary MDMs, 9343 lncRNAs and 5903 mRNAs were deregulated in M(IFN-γ + LPS) group (fold change ≥ 2.0, P < 0.05), 4592 lncRNAs and 3122 mRNAs were deregulated in M(IL-4) group. RT-qPCR results were generally consistent with the microarray data. Furthermore, we found that TCONS_00019715 is expressed at a higher level in M(IFN-γ + LPS) macrophages than in M(IL-4) macrophages. TCONS_00019715 expression was decreased when M(IFN-γ + LPS) converted to M(IL-4) whereas increased when M(IL-4) converted to M(IFN-γ + LPS). Knockdown of TCONS_00019715 following the activation of THP-1 cellls using IFN-γ and LPS diminished the expression of M(IFN-γ + LPS) markers, and elevated the expression of M(IL-4) markers. These data show a significantly altered lncRNA and mRNA expression profile in macrophages exposure to different activating conditions. Dysregulation of some of these lncRNAs may play important roles in regulating macrophage polarization.
| S-EPMC4726337 | biostudies-literature
Project description:BackgroundEfforts to eradicate tuberculosis are largely threatened by drug-resistant tuberculosis, particularly, multidrug-resistant tuberculosis (MDR-TB). Screening and identification potential biomarkers for MDR-TB is crucial to diagnose early and reduce the incidence of MDR-TB.MethodsTo screen the differentially expressed long non-coding RNAs in MDR-TB, the lncRNA and mRNA expression profiles in serum derived from healthy controls (HCs), individuals with MDR-TB and drug-sensitive tuberculosis (DS-TB) were analyzed by microarray assay and 10 lncRNAs were randomly selected for further validation by reverse transcription-quantitative real-time PCR(RT-qPCR). The biological functions of differentially expressed mRNAs as well as relationships between genes and signaling pathways were investigated using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), respectively.ResultsA total of 353 differentially expressed lncRNAs (312 upregulated) and 202 mRNAs (99 upregulated) were found in the MDR-TB group compared to HCs. And compared with the DS-TB group, 442 differentially expressed lncRNAs (115 upregulated) and 190 mRNAs (87 upregulated) were found in the MDR-TB group. The expression levels of lncRNA n335659 were found to differ significantly between each group by RT-qPCR. Compared with DS-TB group, the GO analysis showed that the differential mRNAs were mainly enriched in the processes associated with the detection of the chemical stimulus, the regulation of mRNA metabolic process and neutrophil activation in the MDR-TB group; the KEGG analysis indicated that the differential mRNAs between DS-TB and MDR-TB were mainly enriched in proteasome and Notch signaling pathway, which might reveal a fraction of the mechanism of MDR-TB. The discovery of the serum lncRNA n335659 might serve as a potential biomarker for MDR-TB and Notch signaling pathway provided a new clue for the investigation of the pathological mechanism of MDR-TB.
| S-EPMC8772445 | biostudies-literature
Project description:This study aimed to identify differentially expressed non-coding RNAs (ncRNAs) associated with preterm birth (PTB) and determine biological pathways being influenced in the context of PTB. We processed cell-free RNA sequencing data and identified seventeen differentially expressed (DE) ncRNAs that could be involved in the onset of PTB. Per the validation via customized RT-qPCR, the recorded variations in expressions of eleven ncRNAs were concordant with the in-silico analyses. The results of this study provide insights into the role of DE ncRNAs and their impact on pregnancy-related biological pathways that could lead to PTB. Further studies are required to elucidate the precise mechanisms by which these DE ncRNAs contribute to adverse pregnancy outcomes (APOs) and their potential as diagnostic biomarkers.
| S-EPMC11730358 | biostudies-literature
Project description:Purpose: Melanoma is the most aggressive and life-threatening cutaneous cancer. To explore new treatment strategies, it is essential to identify the mechanisms underlying melanoma tumorigenesis and metastasis. Methods: In the current study, we demonstrated altered expression of long non-coding RNA (lncRNA) and messenger RNA (mRNA) in melanoma using data from the Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) analyses were conducted. We also constructed a functional lncRNA-mRNA regulatory network and Kaplan-Meier analysis. Results: We identified 246 differentially expressed (DE) lncRNAs and 856 DEmRNAs. A total of 184 DElncRNAs and 428 DEmRNAs were upregulated in metastatic melanoma, while all others were downregulated. Additionally, we investigated the co-expression pattern of 363 genes, among which 26 upregulated lncRNAs, 9 down- regulated lncRNAs, 49 upregulated mRNAs and 151 downregulated mRNAs were identified as being co-expressed with others. Survival analysis suggested high levels of 14 lncRNAs and 10 mRNAs may significantly increase or decrease overall survival. These differentially expressed genes are also potentially prognostic in melanoma. Conclusion: Our findings observe potential roles for lncRNAs and mRNAs during melanoma progression and provide candidate biomarkers for further studies.
| S-EPMC6459964 | biostudies-literature
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.
| S-EPMC8359208 | biostudies-literature
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.
| S-EPMC10478209 | biostudies-literature
Project description:The vast majority of human transcriptome is represented by various types of small RNAs with little or no protein-coding capability referred to as non-coding RNAs (ncRNAs). Functional ncRNAs include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), which are expressed at very low, but stable and reproducible levels in a variety of cell types. ncRNAs regulate gene expression due to miRNA capability of complementary base pairing with mRNAs, whereas lncRNAs and circRNAs can sponge miRNAs off their target mRNAs to act as competitive endogenous RNAs (ceRNAs). Each miRNA can target multiple mRNAs and a single mRNA can interact with several miRNAs, thereby creating miRNA-mRNA, lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks. Over the past few years, a variety of differentially expressed miRNAs, lncRNAs, and circRNAs (DEMs, DELs, and DECs, respectively) have been linked to cancer pathogenesis. They can exert both oncogenic and tumor suppressor roles. In this review, we discuss the recent advancements in uncovering the roles of DEMs, DELs, and DECs and their networks in aberrant cell signaling, cell cycle, transcription, angiogenesis, and apoptosis, as well as tumor microenvironment remodeling and metabolic reprogramming during hepatocarcinogenesis. We highlight the potential and challenges in the use of differentially expressed ncRNAs as biomarkers for liver cancer diagnosis and prognosis.
| S-EPMC10474437 | biostudies-literature
Project description:BackgroundMolecular regulation related to the health benefits of different exercise modes remains unclear. Long non-coding RNAs (lncRNAs) have emerged as an RNA class with regulatory functions in health and diseases. Here, we analyzed the expression of lncRNAs after different exercise training programs and their possible modes of action related to physical exercise adaptations.MethodsPublic high-throughput RNA-seq data (skeletal muscle biopsies) were downloaded, and bioinformatics analysis was performed. We primarily analyzed data reports of 12 weeks of resistance training (RT), high-intensity interval training (HIIT), and combined (CT) exercise training. In addition, we analyzed data from 8 weeks of endurance training (ET). Differential expression analysis of lncRNAs was performed, and an adjusted P-value < 0.1 and log2 (fold change) ≥0.5 or ≤-0.5 were set as the cutoff values to identify differentially expressed lncRNAs (DELs).ResultsWe identified 204 DELs after 12 weeks of HIIT, 43 DELs after RT, and 15 DELs after CT. Moreover, 52 lncRNAs were differentially expressed after 8 weeks of ET. The lncRNA expression pattern after physical exercise was very specific, with distinct expression profiles for the different training programs, where few lncRNAs were common among the exercise types. LncRNAs may regulate molecular responses to exercise, such as collagen fibril organization, extracellular matrix organization, myoblast and plasma membrane fusion, skeletal muscle contraction, synaptic transmission, PI3K and TORC regulation, autophagy, and angiogenesis.ConclusionFor the first time, we show that lncRNAs are differentially expressed in skeletal muscle after different physical exercise programs, and these lncRNAs may act in various biological processes related to physical activity adaptations.
| S-EPMC7533564 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) are subclass of noncoding RNAs that have been recently shown to play critical roles in cancer biology. However, little is known about their mechanistic role in breast cancer pathogenesis, especially in triple-negative breast carcinomas (TNBC) that have particular poor outcomes. This study was specifically designed to identify the signatures relevant lncRNAs in breast cancer and characterize lncRNAs that modulate the phenotype. Here we provide detailed methods and analysis of microarray data, which is deposited in the Gene Expression Omnibus (GEO) with the accession number GSE64790. The basic analysis as contained in the manuscript published in Oncotarget with the PMID 26078338. These data can be used to further elucidate the mechanisms of breast cancer.
| S-EPMC4664755 | biostudies-literature