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LncRNA expression predicts mRNA abundance.

ABSTRACT: Aim: Long noncoding RNAs (lncRNAs) have been reported to influence multiple gene regulatory processes. Technological advances in RNA-seq platforms allow detection of low-abundance RNA species such as lncRNAs. This study examined the relationship between expression of lncRNAs and their putative partner mRNAs. Methods: We analyzed total RNA-seq data from mouse macrophages under various inflammatory and intervention conditions. Results: The macrophage expression of lncRNAs is strongly regulated by an inflammatory stimulus. Moreover, the expression of a majority of lncRNAs was correlated or anti-correlated with the partner mRNA(s), across the different treatment conditions. This relationship was maintained even in cells from a distinct genotype. Conclusion: These results suggest a previously unappreciated tight coupling of lncRNA and mRNA expression during macrophage responses to various microenvironmental perturbations.

SUBMITTER: Pacholewska A

PROVIDER: S-EPMC6739901 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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lncRNA expression predicts mRNA abundance.

Pacholewska Alicja A Sung Myong-Hee MH

Epigenomics 20190724 10

Aim: Long noncoding RNAs (lncRNAs) have been reported to influence multiple gene regulatory processes. Technological advances in RNA-seq platforms allow detection of low-abundance RNA species such as lncRNAs. This study examined the relationship between expression of lncRNAs and their putative partner mRNAs. Methods: We analyzed total RNA-seq data from mouse macrophages under various inflammatory and intervention conditions. Results: The macrophage expression of lncRNAs is s ...[more]

PMID: 31339345

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Project description:BackgroundEsophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported.MethodsFirstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups.ResultsThe WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC.ConclusionIn this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.

Project description:Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.

Project description:The serotonin transporter (SERT, SLC6A4) is a Na+-dependent transporter that regulates the availability of serotonin (5-HT, 5-hydroxytryptamine), a key neurotransmitter and hormone in the brain and the intestine. The human SERT gene consists of two alternate promoters that drive expression of an identical SERT protein. However, there are different mRNA transcript variants derived from these two promoters that differ in their 5' untranslated region (5'UTR), which is the region of the mRNA upstream from the protein-coding region. Two of these transcripts contain exon-1a and are abundant in neuronal tissue, whereas the third transcript contains exon-1c and is abundant in the intestine. The 3'UTR is nearly identical among the transcripts. Current studies tested the hypothesis that the UTRs of SERT influence its expression in intestinal epithelial cells (IECs) by controlling mRNA or protein levels. The SERT UTRs were cloned into luciferase reporter plasmids and luciferase mRNA and activity were measured following transient transfection of the UTR constructs into the model IEC Caco-2. Luciferase activity and mRNA abundance were higher than the empty vector for two of the three 5'UTR variants. Calculation of translation index (luciferase activity divided by the relative luciferase mRNA level) revealed that the exon-1a containing 5'UTRs had enhanced translation when compared to the exon-1c containing 5'UTR which exhibited a low translation efficiency. Compared to the empty vector, the SERT 3'UTR markedly decreased luciferase activity. In silico analysis of the SERT 3'UTR revealed many conserved potential miRNA binding sites that may be responsible for this decrease. In conclusion, we have shown that the UTRs of SERT regulate mRNA abundance and protein expression. Delineating the molecular basis by which the UTRs of SERT influence its expression will lead to an increased understanding of post-transcriptional regulation of SERT in GI disorders associated with altered 5-HT availability.

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LncRNA expression predicts mRNA abundance.

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lncRNA expression predicts mRNA abundance.

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