Silencing an insulin-induced lncRNA, LncASIR, impairs the transcriptional response to insulin signalling in adipocytes

Degirmenci, Ufuk; Li, Jia; Lim, Yen Ching; Siang, Diana Teh Chee; Lin, Shibo; Liang, Hui; Sun, Li

doi:10.1038/s41598-019-42162-5

Cited by 18 publications

(17 citation statements)

References 50 publications

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“…In mammals, lncRNAs were further identified as key regulators of energy metabolism and lipogenesis (Yang et al, 2016). In adipocytes, these genomic elements also play an integral part in the insulin-signaling pathway (Degirmenci et al, 2019). A central regulatory role of lncRNAs was furthermore observed in skeletal muscle in myogenesis and muscle cell differentiation: SYISL has been shown to regulate myoblast proliferation and fusion and acts in an inhibitory way in myogenic differentiation (Jin et al, 2018), Irm enhances myogenic differentiation during myogenesis through the binding to MEF2D (Sui et al, 2019), and lnc-mg overexpression has directly been linked to muscle hypertrophy in mice, whereas a knock-out led to dystrophy (Zhu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle

et al. 2019

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Background: Genomic regions associated with divergent livestock feed efficiency have been found predominantly outside protein coding sequences. Long non-coding RNAs (lncRNA) can modulate chromatin accessibility, gene expression and act as important metabolic regulators in mammals. By integrating phenotypic, transcriptomic, and metabolomic data with quantitative trait locus data in prioritizing co-expression network analyses, we aimed to identify and functionally characterize lncRNAs with a potential key regulatory role in metabolic efficiency in cattle.Materials and Methods: Crossbred animals (n = 48) of a Charolais x Holstein F2-population were allocated to groups of high or low metabolic efficiency based on residual feed intake in bulls, energy corrected milk in cows and intramuscular fat content in both genders. Tissue samples from jejunum, liver, skeletal muscle and rumen were subjected to global transcriptomic analysis via stranded total RNA sequencing (RNAseq) and blood plasma samples were used for profiling of 640 metabolites. To identify lncRNAs within the indicated tissues, a project-specific transcriptome annotation was established. Subsequently, novel transcripts were categorized for potential lncRNA status, yielding a total of 7,646 predicted lncRNA transcripts belonging to 3,287 loci. A regulatory impact factor approach highlighted 92, 55, 35, and 73 lncRNAs in jejunum, liver, muscle, and rumen, respectively. Their ensuing high regulatory impact factor scores indicated a potential regulatory key function in a gene set comprising loci displaying differential expression, tissue specificity and loci overlapping with quantitative trait locus regions for residual feed intake or milk production. These were subjected to a partial correlation and information theory analysis with the prioritized gene set.Results and Conclusions: Independent, significant and group-specific correlations (|r| > 0.8) were used to build a network for the high and the low metabolic efficiency group resulting in 1,522 and 1,732 nodes, respectively. Eight lncRNAs displayed a particularly high connectivity (>100 nodes). Metabolites and genes from the partial correlation and information theory networks, which each correlated significantly with the respective lncRNA, were included in an enrichment analysis indicating distinct affected pathways for the eight lncRNAs. LncRNAs associated with metabolic efficiency were classified to be functionally involved in hepatic amino acid metabolism and protein synthesis and in calcium signaling and neuronal nitric oxide synthase signaling in skeletal muscle cells.

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Section: Introductionmentioning

confidence: 99%

Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle

et al. 2019

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“…Long noncoding RNAs (lncRNAs) are a category of promising noncoding RNAs that have been found to interplay with multiple factors in various diseases, which are characterized by a length more than 200 nt and almost no protein‐coding function . However, although lncRNAs are not involved in protein coding, these molecules are able to mediate gene levels at different aspects, including transcription, post‐transcription processing, and so on .…”

Section: Introductionmentioning

confidence: 99%

Potential role of circulating long noncoding RNA MALAT1 in predicting disease risk, severity, and patients' survival in sepsis

Geng

Liu

2019

Clinical Laboratory Analysis

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Background This study aimed to investigate the plasma long noncoding RNA metastasis‐associated lung adenocarcinoma transcript 1 (lncRNA MALAT1) expression with risk, severity, inflammation level, and prognosis in sepsis. Methods One hundred and ninety sepsis patients and 190 health controls (HCs) were consecutively recruited. Blood samples within 24 hours after admission of sepsis patients and those on enrollment of HCs were collected, and then, plasma was separated for lncRNA MALAT1 and miR‐125b expressions detections by RT‐qPCR. In sepsis patients, clinical data and 28‐day mortality were recorded, and plasma inflammatory cytokines expressions were detected by ELISA. Results Plasma lncRNA MALAT1 expression was elevated in sepsis patients than HCs (P < 0.001), and ROC curve disclosed that it had a good value in predicting sepsis risk with an area under curve (AUC) of 0.823 (95% CI: 0.783‐0.864). Additionally, lncRNA MALAT1 expression was positively correlated with Scr (P = 0.005), WBC (P = 0.017), CRP (P < 0.001), PCT (P < 0.001), TNF‐α (P < 0.001), IL‐8 (P < 0.001), IL‐17 (P = 0.001), APACHE II score (P < 0.001), and SOFA score (P < 0.001). LncRNA MALA1 expression was elevated in deaths compared with survivors (P < 0.001) and could predict the risk of 28‐day mortality with an AUC of 0.755 (95% CI: 0.682‐0.828). Accumulating survival was worse in patients with lncRNA MALAT1 high expression compared with patients who had lncRNA MALAT1 low expression (P < 0.001). Moreover, lncRNA MALAT1 expression was negatively correlated with miR‐125b level in both sepsis patients (P < 0.001) and HCs (P < 0.001). Conclusion LncRNA MALAT1 could be developed as a potential biomarker for facilitating diagnosis and management in sepsis patients.

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“…LncRNAs regulate gene expression in multiple ways including transcription, posttranscription, and epigenetic mechanism 27‐29 . Most lncRNAs were found in the nucleus.…”

Section: Discussionmentioning

confidence: 99%

Identification of mitochondrial function‐associated lncRNAs in septic mice myocardium

Shi

Zheng

et al. 2020

J of Cellular Biochemistry

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The present study aimed to analyze long noncoding RNA (lncRNA) and messenger RNA (mRNA) expression profiles in septic mice heart and to identify potential lncRNAs and mRNAs that be responsible for cardiac mitochondrial dysfunction during sepsis. Mice were treated with 10 mg/kg of lipopolysaccharides to induce sepsis. LncRNAs and mRNAs expression were evaluated by using lncRNA and mRNA microarray or real‐time polymerase chain reaction technique. LncRNA‐mRNA coexpression network assay, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. The results showed that 1275 lncRNAs were differentially expressed in septic myocardium compared with those in the control group. A total of 2769 mRNAs were dysregulated in septic mice heart, most of which are mainly related to the process of inflammation, mitochondrial metabolism, oxidative stress, and apoptosis. Coexpression network analysis showed that 14 lncRNAs were highly correlated with 11 mitochondria‐related differentially expressed mRNA. Among all lncRNAs and their cis‐acting mRNAs, 41 lncRNAs‐mRNA pairs (such as NONMMUG004378 and Apaf1 gene) were enriched in GO terms and KEGG pathways. In summary, we gained some specific lncRNAs and their potential target mRNAs that might be involved in mitochondrial dysfunction in septic myocardium. These findings provide a panoramic view of lncRNA and might allow developing new treatment strategies for sepsis.

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Silencing an insulin-induced lncRNA, LncASIR, impairs the transcriptional response to insulin signalling in adipocytes

Cited by 18 publications

References 50 publications

Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle

Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle

Potential role of circulating long noncoding RNA MALAT1 in predicting disease risk, severity, and patients' survival in sepsis

Identification of mitochondrial function‐associated lncRNAs in septic mice myocardium

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