Transcriptomic analysis of senescent cells upon PTBP1 knockdown and EXOC7 knockdown
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ABSTRACT:
PROVIDER: PRJNA395374 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:IMR90 ER:RAS cells were stably transduced with either an empty vector or 2 deconvoluted shRNAs targeting PTBP1. Following selection with puromycin, the cells were treated with 4OHT to induce senescence. 6 days later the cells were collected for total mRNA analysis. PTBP1 is a regulator of alternative splicing. Our previous experiments had shown that PTBP1 depletion inhibits the expression of pro-inflammatory genes without affecting other senescence-associated phenotypes. By performing RNA-seq we confirmed those observations at a global level and analysed how PTBP1 knockdown alters alternative splicing as a potential mechanism of action.
2018-06-25 | GSE101750 | GEO
Project description:IMR90 ER:RAS cells were transfected with scramble siRNA or 2 deconvoluted siRNAs targeting EXOC7 or an siRNA targeting PTBP1. The next day the cells were treated with 4OHT to induce senescence. 5 days later the cells were collected for total mRNA analysis. Our previous experiments had shown that alternative splicing of EXOC7 depends on PTBP1 and PTBP1 is a target for suppressing pro-inflammatory factors in senescence. By performing RNA-seq we confirmed that the pro-inflammatory factors whose expression depends on PTBP1 equally depends on EXOC7.
2018-06-25 | GSE101758 | GEO
Project description:Transcriptomic analysis of senescent cells upon PTBP1 knockdown.
| PRJNA395363 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-06-25 | GSE101763 | GEO
Project description:Purpose: The goals of this study are to determine possible downstream targets of PTBP1 on day8 of erythroid differentiation with or without PTBP1 knockdown. Methods:gene expression profiling with or without PTBP1 knockdown in differentiated erythroblasts at day8 were generated by deep sequencing, induplicate, using Illumina GAIIx.The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Conclusions: heme metabolism genes were down-regulated after PTBP1 knockdown during erythroid differentiation
2018-10-30 | GSE106566 | GEO
Project description:We identify a lncRNA which low expresses and regulates self-renewal of bladder cancer stem cell. So we named it lncRNA-LBCS (low expresses in bladder cancer stem cell). Further study finds that lncRNA-LBCS bind to hnRNPK and PTBP1. To investigate the genes regulated by these, we knockdown lncRNA-LBCS, hnRNPK and PTBP1 by siRNA, respectively, and perform high-throughput sequence. So we identify a series of genes regulated by lncRNA-LBCS, hnRNPK and PTBP1, respectively.
2017-08-13 | GSE79832 | GEO
Project description:Purpose: To identify gene expression changes in entorhinal cortex layer II (ECII) neurons upon Ptbp1 modulation (silencing and overexpression) Method: bacterial artificial chromosome - Translating Ribosome Affinity Purification (bacTRAP) to isolate actively translated mRNA in ECII neurons, 2 weeks after stereotaxic injection of an AAV1 vector in the EC of ECII-bacTRAP mice; followed by RNAseq. Note: Ptbp1 was significantly overexpressed in the overexpression experiment, but no silencing was achieved with the silencing vector, probably because of tight control of Ptbp1 expression
2020-05-29 | GSE151356 | GEO
Project description:IMR90 ER:RAS cells were transfected with scramble siRNA or 2 deconvoluted siRNAs targeting each of 38 candidate genes or positive control siRNAs targeting the senescence regulators p16 and p53 and the SASP regulators NF-kappaB, p38alpha and CEBPbeta. The next day the cells were treated with 4OHT to induce senescence. 6 days later the cells were collected for total mRNA analysis. Our previous experiments had shown that these 38 genes regulate the secretome of senescent cells without affecting other senescence phenotypes such as the growth arrest. By performing RNA-seq we confirmed that knockdown of these genes affected the expression of multiple pro-inflammatory factors in senescence. More importantly, the secreted factors were differentially regulated across the different knockdown conditions indicating potential for specific manipulation of immune response genes.
2018-06-25 | GSE101766 | GEO
Project description:Transcriptomic analysis for understanding differential gene expression upon FKBP5 knockdown
| PRJNA716642 | ENA
Project description:Background: Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge. Methods: M-bM-^@M-^\N-of-1-pathwaysM-bM-^@M-^] translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanismsM-bM-^@M-^Y relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology. Results: In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within- and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-RatiosM-bM-^IM-%13, p-valuesM-bM-^IM-$1M-CM-^W10-5), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples. Conclusions: N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine. Cell lines, culture conditions: The epithelial human breast cancer cell line MDA-MB231 (ER-/ PR-/ HER2-) were obtained from the American Type Culture Collection (Manassas, VA). The epithelial human ovarian tumor cell line A2780 was received as a generous gift from Dr. Thomas C. Hamilton (Fox Chase Cancer Center, Philadelphia, PA) Cancer Center, Philadelphia, PA). Cells were grown in DMEM supplemented with 10% fetal bovine serum (FBS), 2mM L-glutamine in a humid environment at 37M-BM-0C, with 5% CO2. Both cell lines were free of Mycoplasma species and were maintained for no longer than 10 weeks in culture after recovery from frozen stocks. Mycoplasma levels were checked periodically using the MycoAlertM-BM-. Mycoplasma Detection Kit (Lonza Inc., Allendale, NJ). The authenticity of cell lines was assessed by the ATCC carrying out short tandem repeat (STR) analysis (Verified STR Profiling Service, ATCCM-BM-. 135-XV). Additionally, we compared A2780 to the original STR profile collected by the European Collection of Cell Culture (Catalogue number 93112519). Doxycycline-inducible knockdown of PTBP1 regulated by small hairpin RNA (shRNA): In order to analyze the effect of PTBP1 depletion, two consecutive viral transductions were performed in both MDA-MB231 and A2780 cell lines. Cells were plated on 24-well plate (10-20M-CM-^W104 cells/well), maintained in culture for 16 hours, and then medium containing LV-tTR/KRAB-Red lentiviral particles was added. Following 16 h of incubation, cells were transduced a second time by LV-THM/PTBshRNA or LV-THM/LUCshRNA lentiviral particles. Clones expressing both red and green fluorescent protein (dsRED and GFP respectively) were selected and expanded. Following 16 h of incubation, cells were washed and split in two subcultures, one without doxycycline (PTBP1/-DOX; Control in Figure 1) and the other with Doxycycline (DOX) at a final concentration of 1 M-NM-<g/ml (PTBP1/+DOX; PTBP1-KD in Figure 1). Doxycycline was prepared according to the manufacturerM-bM-^@M-^Ys recommendations (Sigma-Aldrich, St. Louis, MO). Five days later, cells were analyzed by fluorescence microscopy, and PTBP1 gene expression was assessed using PCR and Western Blotting (data not shown). The cells that were transduced by LV-LUCshRNA express PTBP1 regardless of the presence of DOX (LUCshRNA/+DOX). Constructs and lentivirus preparation were performed as previously described in literature [He X et al., Knockdown of polypyrimidine tract-binding protein suppresses ovarian tumor cell growth and invasiveness in vitro, Oncogene 2007, 26(34):4961-4968]. Microarray Analysis: For each of the cell lines, MDA-MB231 and A2780, total RNAs were extracted from four biological replicates of PTBP1-depleted cells, PTBP1-KD (4M-CM-^W PTBP1/+DOX) and eight biological replicates control cells (4M-CM-^W PTBP1/-DOX and 4M-CM-^W LUCshRNA/+DOX) by Direct-zol RNA kit (Zymo Research, Irvine, CA). All paired samples consist of PTBP1-depleted cells (PTBP1-KD) and matched control cells. Qualities of RNA were assessed based on the RNA quality indicator (RQI M-bM-^IM-% 8) using Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA). Gene expression microarray measurements were performed using the GeneChip PrimeView Human Gene Expression Array that contains 49,395 probes and measures 36,000 transcripts and variants per sample. Labeling and hybridization were performed following Affymetrix protocols. The raw data were normalized according to the Robust Multiple-array Average (RMA) technique, using Affymetrix Power Tools (APT). biological replicate: Ovarian_LucsH_DOX_1, Ovarian_LucsH_DOX_2, Ovarian_LucsH_DOX_3, Ovarian_LucsH_DOX_4 biological replicate: Ovarian_PTBsh1_NODOX_1, Ovarian_PTBsh1_NODOX_2, Ovarian_PTBsh1_NODOX_3, Ovarian_PTBsh1_NODOX_4 biological replicate: Ovarian_PTBsh1_DOX_1, Ovarian_PTBsh1_DOX_2, Ovarian_PTBsh1_DOX_3, Ovarian_PTBsh1_DOX_4 biological replicate: Breast_LucsH_DOX_1, Breast_LucsH_DOX_2, Breast_LucsH_DOX_3, Breast_LucsH_DOX_4 biological replicate: Breast_PTBsh1_NODOX_1, Breast_PTBsh1_NODOX_2, Breast_PTBsh1_NODOX_3, Breast_PTBsh1_NODOX_4 biological replicate: Breast_PTBsh1_DOX_1, Breast_PTBsh1_DOX_2, Breast_PTBsh1_DOX_3, Breast_PTBsh1_DOX_4
2013-11-20 | E-GEOD-52493 | biostudies-arrayexpress