Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression profiling of long noncoding RNAs and mRNAs in mouse heart, whole blood (WB), and plasma.

ABSTRACT: Long noncoding RNA profile in the heart, whole blood, and plasma before and heart failure (HF). In the study presented here, we mixed the five pairs of mouse hearts, whole blood, and plasma before and after HF and identified the genome-wide expression level of lncRNAs.

ORGANISM(S): Mus musculus

SUBMITTER: Qinghua Cui

PROVIDER: E-GEOD-48885 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Transcriptome analysis reveals distinct patterns of long noncoding RNAs in heart and plasma of mice with heart failure.

Li Danhua D Chen Geng G Yang Jichun J Fan Xiaofang X Gong Yongsheng Y Xu Guoheng G Cui Qinghua Q Geng Bin B

PloS one 20131029 10

<h4>Objective</h4>To assess the global changes in and characteristics of the transcriptome of long noncoding RNAs (LncRNAs) in heart tissue, whole blood and plasma during heart failure (HF) and association with expression of paired coding genes.<h4>Methods</h4>Here we used microarray assay to examine the transcriptome of LncRNAs deregulated in the heart, whole blood, and plasma during HF in mice. We confirmed the changes in LncRNAs by quantitative PCR.<h4>Results</h4>We revealed and confirmed a ...[more]

PMID: 24205036

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Project description:Background: Heart failure (HF) has been recognized as global pandemic with a high rate of associated hospitalization, morbidity and mortality. Although numerous advances have been made, its representative molecular signatures remain largely unknown, especially the role of genes in HF progression. The aim of the present prospective follow-up study was to reveal potential biomarkers associated with the progression of heart failure. Methods: We collected left ventricular heart tissue from 21 HF patients and 9 healthy donors as study cohort and generated multi-level transcriptomic data. By using Masson staining to calculate the fibrosis percentage for each sample, we applied lasso regression model to identify the genes associated with fibrosis as well as progression. The genes were further validated by immunohistochemistry (IHC) staining in the same cohort and qRT-PCR using another independent cohort (20 HF and 9 healthy donors). Enzyme linked immunosorbent assay (ELISA) was used to measure the plasma level in validation cohort (87 HF patients) for predicting HF progression. Results: Based on the multi-level transcriptomic data, we examined differentially expressed genes [mRNAs, microRNAs, and long non-coding RNAs (lncRNAs)] in study cohort. The follow-up functional annotations and regulatory network reveal their potential roles in regulating extracellular matrix. We further identified several genes that were associated with fibrosis. By using the survival time before transplantation, COL1A1 was identified as a potential biomarker for HF progression and its up-regulation was confirmed by both IHC and qRT-PCR. Furthermore, COL1A1 content >281.7 ng/ml in plasma was found to be associated with poor survival within one year of heart transplantation from heart failure [Hazard Ratio (HR): 27.52, 95% Confidence Interval (CI): 7.474 to 101.3, Log-rank p-value<1.0×10-4]. Conclusions: Our results suggested that COL1A1 is a plasma biomarker of HF and associated with HF progression, especially to predict the 1-year survival from HF onset to transplantation.

Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.

Dataset Information

Expression profiling of long noncoding RNAs and mRNAs in mouse heart, whole blood (WB), and plasma.

Publications

Transcriptome analysis reveals distinct patterns of long noncoding RNAs in heart and plasma of mice with heart failure.

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