Comprehensive RNA-Seq Data Analysis Identifies Key mRNAs and lncRNAs in Atrial Fibrillation.
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ABSTRACT:
SUBMITTER: Wu DM
PROVIDER: S-EPMC6783610 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Comprehensive RNA-Seq Data Analysis Identifies Key mRNAs and lncRNAs in Atrial Fibrillation.
Wu Dong-Mei DM Zhou Zheng-Kun ZK Fan Shao-Hua SH Zheng Zi-Hui ZH Wen Xin X Han Xin-Rui XR Wang Shan S Wang Yong-Jian YJ Zhang Zi-Feng ZF Shan Qun Q Li Meng-Qiu MQ Hu Bin B Lu Jun J Chen Gui-Quan GQ Hong Xiao-Wu XW Zheng Yuan-Lin YL
Frontiers in genetics 20191002
Long non-coding RNAs (lncRNAs) are an emerging class of RNA species that may play a critical regulatory role in gene expression. However, the association between lncRNAs and atrial fibrillation (AF) is still not fully understood. In this study, we used RNA sequencing data to identify and quantify the both protein coding genes (PCGs) and lncRNAs. The high enrichment of these up-regulated genes in biological functions concerning response to virus and inflammatory response suggested that chronic vi ...[more]
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Project description:BackgroundAtrial fibrillation (AF) is one of the most common arrhythmia contributing to serious conditions such as stroke and heart failure. Recent studies demonstrated that long noncoding RNAs (lncRNAs) were related to cardiovascular disease. However, the molecular mechanisms of AF are not fully clear. This study intended to discover lncRNAs that are differentially expressed in AF compared with controls and evaluate the potential functions of these lncRNAs.MethodsNinety-seven patients (49 patients with AF and 48 patients without AF) were included in this study. Among these patients, leucocyte suspensions of 3 AF patients and 3 controls were sent for RNA-seq analysis to select differentially expressed lncRNA and mRNA. Different lncRNA expressions were validated in another samples (46 AF patients and 45 controls). Gene ontology (GO) enrichment analysis was conducted to annotate the function of selected mRNAs. Alternative splicing (AS) analysis was performed and a lncRNA-mRNA network was also constructed. The receiver operating characteristics (ROC) curve was used to evaluate diagnostic values. Logistic regression analysis was utilized to assess the risk or protective factor of AF.ResultsA total of 223 mRNAs and 105 lncRNAs were detected in AF patients compared with controls. Total 4 lncRNAs (LINC01781, AC009509.2, AL662844.3, AL662844.4) associated with AF were picked out for validation in another samples by quantitative real-time PCR (qRT-PCR), detecting that upregulated AC009509.2 and downregulated LINC01781 in AF patients. Multivariate logistic regression analysis illustrated that left atrial diameter (OR 1.201; 95% CI 1.093-1.320; P=0.000) and AC009509.2 (OR 1.732; 95% CI 1.092-2.747; P=0.020) were related to AF respectively. ROC curve showed that AC009509.2, LINC01781 and left atrial diameter (LAD) were predictors of AF. For LINC01781, the area under the curve (AUC) was 0.654 (95% CI 0.541-0.767, P=0.0113). For AC009509.2, the AUC was 0.710 (95% CI 0.599-0.822, P=0.0005). Bioinformatic methods (GO enrichment, AS analysis and lncRNA-mRNA network construction) were performed to reveal the role of lncRNAs.ConclusionsThis study discussed differentially expressed lncRNA and their potential interaction with mRNA in AF. LncRNA AC009509.2 could be a new potential biomarker for AF prediction.
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Project description:BackgroundType 1 diabetes (T1D, named insulin-dependent diabetes) has a relatively rapid onset and significantly decreases life expectancy. This study is conducted to reveal the long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulatory axises implicated in T1D.MethodsThe gene expression profile under GSE55100 (GPL570 and GPL8786 datasets; including 12 T1D samples and 10 normal samples for each dataset) was extracted from Gene Expression Omnibus database. Using limma package, the differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs), and lncRNAs (DE-lncRNAs) between T1D and normal samples were analyzed. For the DE-mRNAs, the functional terms were enriched by DAVID tool, and the significant pathways were enriched using gene set enrichment analysis. The interactions among DE-lncRNAs, DE-miRNAs and DE-mRNAs were predicted using mirwalk and starbase. The lncRNA-miRNA-mRNA interaction network analysis was visualized by Cytoscape. The key genes in the interaction network were verified by quantitatively real-time PCR.ResultsIn comparison to normal samples, 236 DE-mRNAs, 184 DE-lncRNAs, and 45 DE-miRNAs in T1D samples were identified. For the 236 DE-mRNAs, 16 Gene Ontology (GO)_biological process (BP) terms, four GO_cellular component (CC) terms, and 57 significant pathways were enriched. A network involving 36 DE-mRNAs, 8 DE- lncRNAs, and 15 DE-miRNAs was built, such as TRG-AS1-miR-23b/miR-423-PPM1L and GAS5-miR-320a/miR-23b/miR-423-SERPINA1 regulatory axises. Quantitatively real-time PCR successfully validated the expression levels of TRG-AS1- miR-23b -PPM1L and GAS5-miR-320a- SERPINA1.ConclusionTRG-AS1-miR-23b-PPM1L and GAS5-miR-320a-SERPINA1 regulatory axises might impact the pathogenesis of T1D.
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Project description:BackgroundRNA-Seq has become one of the most widely used applications based on next-generation sequencing technology. However, raw RNA-Seq data may have quality issues, which can significantly distort analytical results and lead to erroneous conclusions. Therefore, the raw data must be subjected to vigorous quality control (QC) procedures before downstream analysis. Currently, an accurate and complete QC of RNA-Seq data requires of a suite of different QC tools used consecutively, which is inefficient in terms of usability, running time, file usage, and interpretability of the results.ResultsWe developed a comprehensive, fast and easy-to-use QC pipeline for RNA-Seq data, RNA-QC-Chain, which involves three steps: (1) sequencing-quality assessment and trimming; (2) internal (ribosomal RNAs) and external (reads from foreign species) contamination filtering; (3) alignment statistics reporting (such as read number, alignment coverage, sequencing depth and pair-end read mapping information). This package was developed based on our previously reported tool for general QC of next-generation sequencing (NGS) data called QC-Chain, with extensions specifically designed for RNA-Seq data. It has several features that are not available yet in other QC tools for RNA-Seq data, such as RNA sequence trimming, automatic rRNA detection and automatic contaminating species identification. The three QC steps can run either sequentially or independently, enabling RNA-QC-Chain as a comprehensive package with high flexibility and usability. Moreover, parallel computing and optimizations are embedded in most of the QC procedures, providing a superior efficiency. The performance of RNA-QC-Chain has been evaluated with different types of datasets, including an in-house sequencing data, a semi-simulated data, and two real datasets downloaded from public database. Comparisons of RNA-QC-Chain with other QC tools have manifested its superiorities in both function versatility and processing speed.ConclusionsWe present here a tool, RNA-QC-Chain, which can be used to comprehensively resolve the quality control processes of RNA-Seq data effectively and efficiently.
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Project description:BackgroundUsing human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.ObjectivesBy investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.MethodsA cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.ResultsThe metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8-90.2%.ConclusionsSerum metabolomics is a powerful way to identify metabolic disturbances. Differences in small-molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.
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