Dataset Information

Profiling of lncRNAs and mRNAs in liver, adipose and muscle tissue in mice under representative metabolic conditions

ABSTRACT: To systemically identify long non-coding RNAs (lncRNAs) regulating energy metabolism, we performed transcriptome analyses to simultaneously profile mRNAs and lncRNAs in key metabolic organs in mice under pathophysiologically representative metabolic conditions. Of 4759 regulated lncRNAs, function-orientated filters yield 359 tissue-specifically regulated and metabolically sensitive lncRNAs which are predicted by lncRNA-mRNA correlation analyses to function in diverse aspects of energy metabolism. An liver metabolically sensitive lncRNA was experimentally confirmed in mice to suppress lipogenesis by forming a negative feedback loop in the SREBP1c pathway. Taken together, this study supports that a class of lncRNAs function as important metabolic regulators, and establishes a framework for systemically investigating the role of lncRNAs in physiological homeostasis.

ORGANISM(S): Mus musculus

PROVIDER: GSE85439 | GEO | 2016/09/22

SECONDARY ACCESSION(S): PRJNA338471

REPOSITORIES: GEO

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Project description:Purpose: Accurate diagnosis of complete inactivation of tuberculosis lesions is still a challenge with respect to sputum-negative tuberculosis. RNA-sequencing was conducted to uncover potential lncRNA indicators of metabolic activity in tuberculosis lesions. Methods: Lung tissues with high metabolic activity (PET-high) and low metabolic activity (PET-low) demonstrated by fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) were collected from five sputum-negative tuberculosis patients for RNA-sequencing. Differentially expressed mRNAs and lncRNAs were identified, and their correlations were evaluated to constructed lncRNA-mRNA co-expression network. Afterwards, cis- or trans-targets of differentially expressed lncRNAs were predicted, followed by establishing lncRNA-target-pathway network, in which lncRNAs and mRNAs with high degrees were measured by quantitative real-time PCR using samples collected from 11 patients. Prediction efficiencies of lncRNA indicators were assessed by receiver operating characteristic curve analysis, Bioinformatics analysis was performed for potential lncRNAs. Results: In total, 386 mRNAs and 44 lncRNAs were identified to be differentially expressed. Differentially expressed mRNAs in lncRNA-mRNA co-expression network were significantly associated with the fibrillar collagen, platelet-derived growth factor binding, and leukocyte migration involved in inflammatory response. One cis-target gene and 440 trans-target genes were predicted for differentially expressed lncRNAs. Seven genes (C1QB, CD68, CCL5, CCL19, MMP7, HLA-DMB, and CYBB) and two lncRNAs (ENST00000429730.1 and MSTRG.93125.4) were validated to be significantly up-regulated. The area under the curve of ENST00000429730.1 and MSTRG.93125.4 was 0.750 and 0.813, respectively. Conclusion: Two lncRNAs ENST00000429730.1 and MSTRG.93125.4 might be considered as potential indicators of metabolic activity in tuberculosis lesions for sputum-negative tuberculosis.

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Profiling of lncRNAs and mRNAs in liver, adipose and muscle tissue in mice under representative metabolic conditions

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