Identification and functional characterization of long noncoding RNAs in breast cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: W. Lee Kraus
PROVIDER: E-GEOD-63189 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Discovery, Annotation, and Functional Analysis of Long Noncoding RNAs Controlling Cell-Cycle Gene Expression and Proliferation in Breast Cancer Cells.
Sun Miao M Gadad Shrikanth S SS Kim Dae-Seok DS Kraus W Lee WL
Molecular cell 20150730 4
We describe a computational approach that integrates GRO-seq and RNA-seq data to annotate long noncoding RNAs (lncRNAs), with increased sensitivity for low-abundance lncRNAs. We used this approach to characterize the lncRNA transcriptome in MCF-7 human breast cancer cells, including >700 previously unannotated lncRNAs. We then used information about the (1) transcription of lncRNA genes from GRO-seq, (2) steady-state levels of lncRNA transcripts in cell lines and patient samples from RNA-seq, an ...[more]
Similar Datasets
Project description:Foxk proteins are transcriptional regulators implicated in key biological processes such as glycolysis, autophagy and cell cycle regulation, among others. Here we employ targeted morpholino knockdown to deplete Foxk1, Fokx2, and Foxk2-1 proteins in developing zebrafish embryos. We demonstrate that the loss of Foxk transcription factors causes genome-wide transcriptional misregulation, characterised by upregulation of autophagy-related genes and downregulation of cell cycle regulators. The phenotype is embryonic lethal with the majority of embryos not surviving past 24hpf.
2020-12-01 | E-MTAB-9777 | biostudies-arrayexpress
Project description:Functional studies of long noncoding RNAs (lncRNAs) have long been hindered by a lack of methods to assess their evolution. Here, we present lncHOME (lncRNA Homology Explorer), a computational pipeline that identifies a unique coPARSE-lncRNA class with conserved genomic locations and patterns of RNA binding protein (RBP) binding sites. Remarkably, several hundred human coPARSE-lncRNAs can be evolutionarily traced to zebrafish. Using CRISPR-Cas12a knockout and rescue assays, we found that knocking out many human coPARSE-lncRNAs led to cell proliferation defects that were rescued by predicted zebrafish homologs. Knocking down the coPARSE-lncRNAs in zebrafish embryos caused severe developmental delays that were rescued by human homologs. Moreover, we verified that human, mouse, and zebrafish coPARSE-lncRNA homologs tend to bind similar RBPs with their conserved fuctions relying on specific RBP binding sites. Overall, our study demonstrates a comprehensive approach for studying functional conservation of lncRNAs and implicates numerous lncRNAs in regulating cellular physiology.
2024-01-26 | PXD046452 | Pride
Project description:Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. To study a comprehensive and comparable set of lncRNAs expressed in the pluripotent state, we analyzed RNA-Seq data from pluripotent cells derived from several strains and species, and grown in two physiological conditions. First we derived ânaïveâ ES cells (ESCs) from each of three different mice strains: 129SvEv, NOD, and Mus musculus castaneus (cast) mouse, a wild mouse subspecies originally from Thailand, as well as naïve induced pluripotent stem (iPS) cells from rat and human. Next, to facilitate comparisons across pluripotent cells from different species, we also cultured mouse and rat cells in âprimedâ epiblast stem cell (EpiSC) culture conditions, since human iPS cells in culture are much more similar molecularly and functionally to mouse primed EpiSCs rather than to a ground state naïve ESCs. We polyA selected RNA from each cell type and deeply sequenced on HiSeq2500
2016-01-07 | E-GEOD-64818 | biostudies-arrayexpress
Project description:RNA sequencing (RNA-seq) analysis revealed 31 novel lncRNAs in HCASMC, including a vascular cell-enriched lncRNA called SENCR (for Smooth muscle and Endothelial cell long Non-Coding RNA). RT-PCR and hybridization studies show SENCR exists in two isoforms and is transcribed antisense from the 5’ end of the FLI1 gene. Knockdown of SENCR has no effect on FLI1 mRNA or protein expression. Biochemical fractionation and RNA fluorescence in situ hybridization (FISH) studies indicate SENCR is a cytoplasmic lncRNA. RNA-seq experiments in HCASMC where SENCR is attenuated disclose decreased expression of Myocardin and many SMC contractile genes; conversely a pro-migratory gene signature is increased. RT-PCR and Western blotting validated several differentially expressed genes following SENCR knockdown. Loss-of-function studies in scratch wound and Boyden chamber assays support SENCR as an inhibitor of vascular cell migration. Total RNAs of 3 replicates of normal human coronary artery smooth muscle cells (Mock1, Mock2 and Mock3) were sequenced and analyzed for identification of novel lncRNAs. One of identified novel lncRNAs from that experiment is SENCR. To study its function, SENCR knock-down experiment were performed and then RNA-seq profiles of 3 replicates of both SENCR-knockdown samples and corresponding controls were compared.
2014-05-01 | E-GEOD-51878 | biostudies-arrayexpress
Project description:Background: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results: We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from ten healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at one or more than one month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in 2 independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions: A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. We used PolyA+ RNA-seq data from human primary granulocytes of 10 healthy individuals to de novo annotate lncRNAs and mRNAs in this cell type and ribosomal depleted (total) RNA-seq data from seven of these individuals sampled three times to analyze lncRNA amd mRNA expression variability
2016-01-05 | E-GEOD-70390 | biostudies-arrayexpress
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups 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.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:We have performed a Proteogenomics meta-analysis of data sets deposited in ProteomeXchange: PXD000265, PXD000313, PXD000923, PXD001030, PXD001058, PXD002291, PXD002739, PXD002740 and PXD003156 and using 29 RNA-Seq data sets on rice (Oryza sativa). We created a search database comprising translated reads that had been mapped onto the rice genome, as well as officially annotated rice proteins sequences. The RNA Seq database was pre-processed to identify “novel transcripts” for those not mapping fully to an existing exon, and “novel junctions” for those reads mapped with a gap, implying a potential novel splice site that was not annotated in the official gene set. Confidentially identified “novel peptides” i.e. those mapping to a novel junction or novel transcript were post-processed to ensure that there were no other better explanations for the corresponding spectra e.g. peptide from a canonical gene with a modification or amino acid substitution. Data were exported from the pipeline in PSI mzIdentML 1.2 format, containing chromosomal coordinates, and further converted to PSI proBed format for genome visualisation. Novel peptides were searched against other plant databases using BLAST to see if they had predicted in genes from other species. A total of 1584 novel peptides were identified, mapping to ~700 genomic loci in which either new genes have been predicted (~100) or updates to existing gene models have been predicted (~600).
2018-10-30 | PXD008960 | Pride
Project description:Long noncoding RNAs (lncRNAs) have emerged as key players in different cellular processes and are required for diverse functions in vivo. However, fundamental aspects of lncRNA biology remain poorly characterized, including their subcellular localization, abundance and variation at a single cell resolution. Here, we used single molecule, single-cell RNA fluorescence in situ hybridization (RNA FISH) to survey 61 lncRNAs, chosen by properties such as conservation, tissue specific expression, and expression abundance, and to catalog their abundance and cellular localization patterns in three human cell types. Our lncRNAs displayed diverse sub-cellular localization patterns ranging from strictly nuclear localization to almost exclusive cytoplasmic localization, with the majority localized primarily in the nucleus. The low abundance of these lncRNAs as measured in bulk cell populations cannot be explained by high expression in a small subset of 'jackpot' cells. Simultaneous analysis of lncRNAs and mRNAs from corresponding divergently transcribed loci showed that divergent lncRNAs do not present a distinct localization pattern and are not always co-regulated with their neighbor. Overall, our study highlights important differences and similarities between lncRNAs and mRNAs. The rich set of localization patterns we observe are consistent with a broad range of potential functions for lncRNA, and assists in hypothesis generation for mechanistic studies. Here we provide the RNA-Seq expression matrix, as well as RNA-Seq raw data, which we used for comparison with RNA FISH molecule counts. We estimate FPKM of coding genes and lncRNAs across HeLa, human lung fibroblasts and human foreskin. This study includes data from human foreskin fibroblasts (hFF), human lung fibroblasts (hLF), and HeLa cells. An hFF sample (GSM1376178) and the hLF samples (GSM1376175-GSM1376177) were previously submitted and are available in GSE30554 as GSM759893 and GSM759890-GSM759892, respectively. The HeLa samples (GSM591670-GSM591671) were previously submitted and are available in GSE23316. The complete dataset representing: (1) the hFF Samples, including the re-analysis of the hFF Sample from GSE30554, (2) the re-analysis of the hLF Samples from GSE30554, and (3) the re-analysis of the HeLa Samples from GSE23316, is linked below as a supplementary file.
2015-01-07 | E-GEOD-57049 | biostudies-arrayexpress
Project description:Ananas comosus var. bracteatus has high ornamental value and widespread application because of its chimeric leaves. However, little is known about the molecular mechanism regulating this characteristic. Here, comparative transcriptomic and proteomic analyses of the white parts (Whs) and green parts (Grs) of the chimeric leaves were performed to identify differentially expressed genes (DEGs) and differentially expressed proteins (DEPs). In total, 1,685 DEGs, including 712 up- and 973 down-regulated ones, and 5,428 DEPs, including 1,018 up- and 795 down-regulated ones, were identified between the Whs and Grs. Comparisons with the GO and KEGG annotations revealed that the DEGs were involved mostly in carbon fixation, porphyrin and chlorophyll metabolism and oxidative phosphorylation. The DEPs were mainly involved in ribosomes, photosynthesis, photosynthesis antennas, and porphyrin and chlorophyll metabolism. Combined analysis showed that nine proteins related to chlorophyll biosynthesis, photosynthetic pigments, and photosynthesis were unchanged at mRNA level but suppressed at protein level. These results indicated that the albino phenotype of the Whs was caused by the proteomic-level suppression of key enzymes involved in the chlorophyll biosynthesis pathway and that translational and post-translational regulation may play important roles in both the biosynthesis of photosynthetic pigments and photosynthesis. Biological significance: Leaves of Ananas comosus var. bracteatus serve as the best materials for the study of albino mechanism. Because the chemic trait of A. comosus var. bracteatus is unstable and the molecular mechanism of the albino cells was poorly understood, we performed comparative analyses both at the transcriptome and proteome levels. This work revealed suppressed proteomic-level and translational and post-translational regulation contribute to the albino phenotype formation. Our results provide better information concerning the molecular mechanism within the chimeric leaves of A. comosus var. bracteatus.
2019-06-10 | PXD010375 | Pride
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.
2017-12-08 | E-MTAB-6193 | biostudies-arrayexpress