Effect of Glucocorticoids on lncRNA and mRNA Expression Profile of the Human Bone Microcirculatory Endothelial Cells
Ontology highlight
ABSTRACT: Appropriate gene expression patterns form the basis for bone microcirculatory endothelial cellsM-bM-^@M-^Y function and bone morphology. Although previous studies have elucidated the impact of hydrocortisone on bone microcirculatory endothelial cellsM-bM-^@M-^Y specific gene expression, the exact differential transcriptomes and comprehensive gene expression profiles remain unknown. We have investigated the mRNA and lncRNA expression patterns before and after hydrocortisone administration of bone microcirculatory endothelial cells. At mRNAs level totally 518 differentially expressed genes were identified. Furthermore, we identified 73 upregulated and 166 downregulated long non-coding RNAs after administration of hydrocortisone. These RNAs appeared to be highly important to gene co-expression network. Transcriptomic analysis of bone microcirculatory endothelial cells from human samples is highly informative due to their relevance to the large number of expressed genes. Our study provides a very valuable basis for investigation of genes, regulation and their co-expression network contributing to hydrocortisone induced disorders. Two-condition experiment, hydrocortisone treated vs. untreated bone microcirculatory endothelial cells. Biological replicates: 8 control replicates, 8 treated replicates.
Project description:Long noncoding RNAs (lncRNAs) play a key role in regulating immunological functions. Their impact on the chronic inflammatory disease multiple sclerosis (MS), however, remains unknown. We investigated the expression of lncRNAs in peripheral blood mononuclear cells (PBMCs) of patients with MS and try to explain their possible role in the process of MS. we recruited 26 MS patients according to the revised McDonald Criteria. Then we chosen 6 patients for microarray analysis randomly. Microarray assays identified outstanding differences in lncRNA expression, which were verified through real-time PCR. LncRNA functions were annotated for target genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and regulatory relationships between lncRNAs and target genes were analyzed using the “cis” and “trans” model.
Project description:Appropriate gene expression patterns form the basis for bone microcirculatory endothelial cells’ function and bone morphology. Although previous studies have elucidated the impact of hydrocortisone on bone microcirculatory endothelial cells’ specific gene expression, the exact differential transcriptomes and comprehensive gene expression profiles remain unknown. We have investigated the microRNA expression patterns before and after hydrocortisone administration of bone microcirculatory endothelial cells. Only 5 microRNAs were Benjamini-Hochberg characterized over 368 microRNAs candidates. Transcriptomic analysis of bone microcirculatory endothelial cells from human samples is highly informative due to their relevance to the large number of expressed genes. Our study provides a very valuable basis for investigation of genes, regulation and their co-expression network contributing to hydrocortisone induced disorders.
Project description:Appropriate gene expression patterns form the basis for bone microcirculatory endothelial cells’ function and bone morphology. Although previous studies have elucidated the impact of hydrocortisone on bone microcirculatory endothelial cells’ specific gene expression, the exact differential transcriptomes and comprehensive gene expression profiles remain unknown. We have investigated the mRNA and lncRNA expression patterns before and after hydrocortisone administration of bone microcirculatory endothelial cells. At mRNAs level totally 518 differentially expressed genes were identified. Furthermore, we identified 73 upregulated and 166 downregulated long non-coding RNAs after administration of hydrocortisone. These RNAs appeared to be highly important to gene co-expression network. Transcriptomic analysis of bone microcirculatory endothelial cells from human samples is highly informative due to their relevance to the large number of expressed genes. Our study provides a very valuable basis for investigation of genes, regulation and their co-expression network contributing to hydrocortisone induced disorders.
Project description:Many studies have demonstrated miRNAs as key regulators of inflammatory responses in endothelial cells (Ecs). However, because of the complexity of inflammatory genes and miRNAs, there would be many undiscovered miRNAs involved in inflammatory responses of ECs. Let-7e is an important member of let-7e family and plays key roles in the regulation of inflammation and endothelial cell proliferation. Furthermore, let-7e expression is significantly increased in many cardiovascular diseases including coronary heart disease. Therefore,we speculated that let-7e might play important roles in the regulation of inflammatory responses in endothelial cells by directly or indirectly targeting certain inflammatory genes. In order to reveal the action of let-7e in vascular endothelial cells, the expression profiles of mRNAs and lncRNAs induced by let-7e in human umbilical vein endothelial cells (HUVECs) were investigated using microarray technology.
Project description:Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common form of leukemia in children. Multiple lncRNAs participate in normal and may be implicated in malignant hematopoiesis associated with blood cell cancers, such as leukemia. Currently, the expression profile of lncRNAs in pediatric AML is unclear. In this study, we evaluated the lncRNA expression profile in the tissue of three pediatric AML patients with lncRNA microarray techniques. In order to gain insight into the function of targets of lncRNAs, GO term and KEGG pathway annotation were applied to the target gene pool. qPCR was performed to evaluate the expression patterns of dys-regulated lncRNAs. Bone marrow specimens were obtained at the time of diagnosis during routine clinical assessment of 3 pediatric patients with AML, who presented at the Department of Hematology and Oncology, Children's Hospital of Soochow University between 2000 and 2011. Additionally, bone marrow samples from 3 healthy donors were analyzed as controls. Human LncRNA Array analysis was performed by KangChen Bio-tech, Shanghai P.R. China. Total RNA from each sample was quantified by the NanoDrop ND-1000 and RNA integrity was assessed by standard denaturing agarose gel electrophoresis. For microarray analysis, Agilent Array platform was employed. The sample preparation and microarray hybridization were performed based on the manufacturerâs standard protocols with minor modifications. Briefly, mRNA was purified from total RNA after removal of rRNA (mRNA-ONLY⢠Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3â bias utilizing a random priming method. The labeled cRNAs were hybridized onto the Human LncRNA Array v2.0 (8 x 60K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v12.0 software package (Agilent Technologies). After quantile normalization of the raw data, LncRNAs and mRNAs that at least 4 out of 6 samples have flags in Present or Marginal (âAll Targets Valueâ) were chosen for further data analysis. Differentially expressed LncRNAs and mRNAs with statistical significance between the two groups were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable LncRNAs and mRNAs expression pattern among samples.
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.
Project description:We performed proteomics analysis of Yorkshire Seminal plasma extracellular vesicles(SPEVs) with high or low sperm motility to investigate specific biomarker affecting sperm motility.Twelve Yorkshire boars with extremely high (H) or low (L) sperm motility were selected, with six individuals in each group.
Project description:The miRNA microarray analysis was performed to explore the expression profiles of miRNAs using the same liver tissues of NFD-fed mice and HFD-fed mice Summary: An abstract of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: miRCURY™ LNA expression array information. Data Analysis for miRNAs: 1. Low intensity filtering and data normalization: After low intensity miRNAs filtering, raw signal intensities are normalized in Median method. (miRNAs that intensities>=30 in all samples are chosen for calculating normalization factor) 2. Quality assessment of miRNA data after filtering: Contains box plot, Correlation Matrix and scatter plot for miRNAs after normalization. 3. Differentially expressed miRNAs screening: Contains significant differentially expressed miRNAs that pass Volcano Plot filtering. (Fold Change>=1.5, P-value<=0.05) 4. Heat map and hierarchical clustering: Hierarchical clustering on the significant differentially expressed miRNAs that passed Volcano Plots filtering. Sample RNA Quality Control: Sample quality control data file from Nanodrop 1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction for microarray, experiment, and data analysis. FAQ: Frequently asked question Additional miRNA Array Analysis (charge an extra fee): Prediction Analysis for Microarrays (PAM analysis) miRNA Target Gene Prediction and Functional Analysis Additional files provided: Graphs (*.jpg) Raw Intensity File(*.xls, raw miRNAs signal intensity) Layout File (*.gal, the files contain information on the positioning of the capture probes on the array and microRNA annotations for your species of interest) Raw data files produced by GenePix Pro 6.0
Project description:Long non-coding RNAs (lncRNAs) are master regulators of gene expression and have recently emerged as potential innovative therapeutic targets. The deregulation of lncRNA expression patterns has been associated with age-related and noncommunicable diseases, including osteoporosis and bone tumors. However, the specific role of lncRNAs in physiological or pathological conditions in the bone tissue still needs to be further clarified, for their exploitation as therapeutic tools. In the present study, we evaluate the potential of the lncRNA CASC2 as a regulator of osteogenic differentiation and mineralization. Results show that CASC2 expression is decreased during osteogenic differentiation of human bone marrow-derived Mesenchymal Stem/Stromal cells (MSCs). CASC2 knockdown using small interfering RNA (siCASC2) increases the expression of the late osteogenic marker Bone Sialoprotein (BSP), but does not impact ALP staining levels, or the expression of early osteogenic transcripts including RUNX2 and OPG. Although siCASC2 does not impact hMSC proliferation nor apoptosis, it promotes the mineralization of hMSC cultured under osteogenic-inducing conditions, as shown by the increase of calcium deposits. Mass spectrometry-based proteomic analysis revealed that 89 proteins are regulated by CASC2 at late osteogenic stages, including proteins associated with bone diseases or anthropometric and musculoskeletal traits. Specifically, the Cartilage Oligomeric Matrix Protein (COMP) is highly enhanced by CASC2 knockdown at late stages of osteogenic differentiation, at either transcriptional and protein level. Inhibition of COMP impairs osteoblasts mineralization as well as the expression of BSP levels. The results indicate that lncRNA CASC2 regulates late osteogenesis and mineralization in hMSC via COMP and BSP. In conclusion, this study suggests lncRNA CASC2 as a potential new therapeutic target in bone mineralization.