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RNA-seq of dexamethasone-treated mouse primary hepatocytes infected with shControl or shSETDB2 adenovirus

ABSTRACT: Purpose: The goal of this study is to identify differentially expressed genes in SETDB2 deficient mouse primary hepatocytes (MPH) treated with dexamethasone when compared to control MPH Methods: Total RNA from MPH treated with either shControl or shSETDB2 adenovirus (pool of 3 mice per group) was isolated by the TRIzol method (Life Technologies) according to manufacturer’s instructions. Total RNA was DNase-treated and RNA integrity was evaluated by Biolanalyzer (Agilent). RNA sequencing was performed by the SBP Genomics Core Facility in Lake Nona (Orlando, FL) using the Illumina HiSeq platform and the following parameters: paired-end read length: 100 BP; number of output reads: 300m reads. The Tophat+Cufflinks pipeline was used for the RNA-seq analysis as described previously (Haas et al., 2013; Trapnell et al., 2012). Differentially expressed genes in shSETDB2 vs. shControl MPH were determined using a 2-fold cutoff, p-value of less than 0.05, and FPKM (fragments per kilobase of transcript per million mapped reads) cutoff of 1. The shSETDB2 and shControl RNA-seq reads were mapped to the mm10 genome using Tophat (Trapnell et al., 2009). The mapping results were further analyzed by Cufflinks, which outputs the final differential gene list.

ORGANISM(S): Mus musculus

PROVIDER: GSE85278 | GEO | 2016/08/28

SECONDARY ACCESSION(S): PRJNA338065

REPOSITORIES: GEO

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Preclinical efficacy and safety study of encapsulated human proliferating hepatocyte organoids in the treatment of liver failure

Project description:Purpose: The goals of this study are to analyze the NGS-derived transcriptome profiling (RNA-seq) between the normal, control and experimental group after extended hepatectomy. Results: All sequencing reads from RNA-seq were mapped to the mouse reference genome (GRCm38.84) using hisat2-2.10 (Kim et al., 2015). FPKM (fragments per kilobase of exon per million fragments mapped) values were calculated by Cufflinks v.2.2.1 (Trapnell et al., 2012) using default parameters for gene expression levels. We used htseq-count (Anders et al., 2015) to count reads on genes. Differential expression analysis was performed using DESeq2 (R package, (Love et al., 2014)). Genes were considered differentially expressed if FPKM >=1 in at least one sample and |fold change| >= 2, adjusted p-value < 0.05. The gene ontology analyses was performed using Metascape v3.5 (http://metascape.org/gp/index.html, (Zhou et al., 2019)). Principal component analysis was performed with whole genes using the R package (function: prcomp()). Conclusions: Our study represents the first detailed analysis of liver transcriptomes after extended hepatectomy, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

2022-03-07 | GSE197897 | GEO

Strand-oriented RNAseq of LNCaP prostate cancer cells in culture for 24h in the presence of androgen

Project description:The aim of this study was to use NGS RNAseq deep-sequencing in order to characterize the polyadenylated mRNAs and lncRNAs expressed in LNCaP cells treated with androgen hormone compared with untreated LNCaP cells (GSE79301). Trimmed reads were mapped using the hg19 genome with TopHat v.2.0.12 and Bowtie v.2.2.3. The assembly was guided by a custom GTF file created with transcripts fromhuman lncRNA annotations from GENCODE v19 (Harrow, Frankish et al.2012) and those already annotated as lincRNAs in (Cabili, Trapnell et al. 2011; Prensner, Iyer et al. 2011; Hangauer, Vaughn et al. 2013). The diferencial expression was calculated by the sum of exon read count per gene with HTSeq (Anders, Pyl et al. 2015), followed by a DESeq2 analysis (Love, Huber et al. 2014).

2017-12-31 | GSE102305 | GEO

K562 polyA RNA-Seq

2012-11-14 | GSE34740 | GEO

Analysis of Wild Type and Top2b-/- Retinal Transcriptomes

Project description:Purpose: Identify Top2b-dependent genes by comparing retinal transcriptome profiling (RNA-seq) Methods: Retinal mRNA profiles of neonatal (postnatal day 0 and 6) wild-type (WT) and Topoisomerase IIbeta (Top2b) conditional knockout (Top2b−/−, or cKO) mice were generated using the SOLiD System (Applied Biosystems). Data analysis was performed according to published protocols (Trapnell et al., 2012) with minor modifications. Briefly, colorspace data of raw 50 bp reads was aligned to the mouse genome (mm9 or GRCm37) using Bowtie (Langmead et al., 2009). Gene expression levels were analyzed with Cufflinks, with differentially expressed genes determined by Cuffdiff (Trapnell et al., 2010). Results: After sequencing, ~120 million 50 bp reads for each time point and each group were obtained and mapped to the mouse genome (MGI, as of Feb 15, 2012). Over 62,000 transcripts were identified; these include ~22,000 annotated genes (including alternative-spliced variants) which cover 76% of the whole mouse genome. Among all the annotated genes, 8.80% (1,935/22,000) for P0 and 1.25% (274/22,000) for P6 showed differential expression between the control and cKO samples (p-value ≤ 0.05, q-value ≤ 0.05). Conclusions: All genes with differential expression levels in Top2b cKO samples are direct or indirect dependent to Top2b.

2017-02-12 | GSE86187 | GEO

RNA-seq from ENCODE/LICR

Project description:Using RNA-Seq (Mortazavi et al., 2008), high-resolution genome-wide maps of the mouse transcriptome across multiple mouse (C57Bl/6) tissues and primary cells were generated. Cells were grown according to the approved ENCODE cell culture protocols (http://genome.ucsc.edu/ENCODE/protocols/cell/mouse). RNA-Seq RNA samples from tissues and primary cells were extracted from Trizol® according to protocol (Invitrogen). PolyA+ RNA was purified with the Dynabeads mRNA purification kit (Invitrogen). The mRNA libraries were prepared for strand-specific sequencing as described previously (Parkhomchuk et al., 2009). Sequencing and Analysis Samples were sequenced on Illumina Genome Analyzer II, Genome Analyzer IIx and HiSeq 2000 platforms for 36 cycles. Image analysis, base calling and alignment to the mouse genome version mm9 were performed using Illumina's RTA. Alignment to the mouse genome was performed using TopHat (Trapnell et al., 2009). Wig files were generated by TopHat and expression levels were calculated with Cufflinks (Trapnell et al., 2010).

2012-05-09 | E-GEOD-36026 | biostudies-arrayexpress

Role of the mRNA-binding protein HuR in the global transcriptomic status of cells stimulated with electrophiles and oxidants

Project description:HuR has been implicated as a stress-responsive protein: cytoplasmic translocation of HuR is promoted by several small-molecule stressors, e.g., H2O2, arsenite, and the cyclopentenone prostaglandin A2 (PGA2), a Michael acceptor RES (Wang et al., 2000; Yang et al., 2004). Treatment with PGA2 also increases the affinity of HuR for p21 mRNA (Wang et al., 2000). Intrigued by these reports, we launched HuR-dependent gene-expression profiling studies in the presence and absence of small-molecule stress signals. HNE—a native RES with a reactive core chemically similar to that of PGA2—was selected as a representative bioactive RES (Jacobs and Marnett, 2009; Schopfer et al., 2011) and H2O2 was selected as a representative ROS. shHuR and shControl HEK293T cells were first generated and then treated with HNE or H2O2 under the conditions where cell viability is largely unaffected. We subsequently sequenced their RNA post ribosomal RNA (rRNA) depletion (see details below). Significant differential expression was evaluated with CuffDiff (Trapnell et al., 2013).

2020-02-10 | GSE127444 | GEO

K562 polyA RNA-Seq

Project description:RNA-Seq reads and TopHat (Trapnell et al. Bioinformatics 2009) alignments of K562 cell-line transcriptome. These were used to validate the expression of short peptides idenitified by Mass-Spectrometry in K562 cells. K562 polyA+ RNA (Batch 1) and total RNA (batch 2) was purchased from Ambion. We used oligo (dT)-selected polyA+ RNA to construct libraries for RNA-Seq.We then profiled the transcriptome of polyadenylated mRNA-Seq using Illumina sequencing platforms. We then used the sequenced reads to reconstruct the transcriptome using the Cufflinks de-novo assembler (Trapnell et al. Nat.Bio.Tech. 2010). Recent computational and ribosome profiling analyses suggest that many short open reading frames (sORFs) in eukaryotic genomes are translated. However, evidence that these sORFs produce stable polypeptides is lacking. Here we develop a strategy to discover and validate novel sORF-encoded polypeptides (SEPs) in human cells. In total, we detect 117 SEPs, 114 of which are novel, varying in length from 15 to 149 amino acids. Of these, 10 SEPs (0.5%) are derived from long intergenic non-coding RNAs (lincRNAs). We also observe the presence of polycistronic genes and the widespread use of non-AUG start codons, which is a phenomenon historically thought to be rare in the mammalian genome. Quantitative measurements reveal that SEPs can be found at concentrations between ~10-2000 copies per cell, which is within the range of typical cellular proteins. We confirm the translation of a number of these SEPs through heterologous expression of their encoding cDNAs. We also discover that several SEPs possess properties characteristic of functional proteins. These results demonstrate that human sORFs produce numerous stable polypeptides, revealing that the human proteome is larger and more diverse than previously appreciated.

2012-11-14 | E-GEOD-34740 | biostudies-arrayexpress

RNA-Seq analysis identifies pancreatic lncRNAs

Project description:Aim: Identification of long noncoding RNAs using transcriptional analysis of e15.5 mouse embryonic pancreas and adult mouse islets Methods: rRNA-depleted total RNA from e15.5 embryonic mouse pancreata and 12-week-old mouse islets was prepared using the Ribo-Zero rRNA removal kit. Biological replicates indicate that each RNA-seq data set was generated from individual mice (islets) or 3-4 pooled e15.5 embryonic pancreata. Libraries were prepared with Illumina TruSeq RNA sample preparation kit and then sequenced with 60 million, 2 x 100 paired reads on an Illumina HiSeq 2000 V3 instrument. To reconstruct the transcriptomes, we first mapped all reads of total RNAs to the mouse reference genome (mm10) with TopHat v1.3.2 (Trapnell et al., 2012). Cufflinks (v1.2.1) was subsequently applied to assemble the whole transcriptome and to identify all possible transcripts. Significantly differentially expressed genes were calculated using DEseq2.

2019-10-10 | GSE122033 | GEO

RNASeq of RNA from blasted CD4+ T cells from individuals with TCF3 mutations and healthy controls

Project description:RNA-seq was performed using RNA extracted from blasted CD4+ T cells from individuals with TCF3 (haploinsufficiency [HI], null, or dominant negative [DN] mutations and healthy controls for the following study groups: healthy controls (HC, n=4) and patients with TCF3 mutations (HI=4, null=1, DN=1 ). Libraries were prepared using the AmpliSeq for Illumina Transcriptome Human Gene Expression panel. RNA-seq was performed on the Illumina HiSeq 2500 (Illumina; AmpliSeq for Illumina/HiSeq 2500). Demultiplexed reads were mapped to the hg19 genome using the splice-aware aligner Tophat (Trapnell et al., 2009). Gene-level counts data were generated using the Rsubread feature counts a read summarization program that counts mapped reads for genomic features such as genes (Liao et al., 2019). Differential expression analysis was performed using R (v.3.5.3) and DESeq2 (v.1.22.2)(Love et al., 2014).

2022-12-01 | GSE218787 | GEO

Quantitative analysis of chromatin occupancy (ChIP-Seq) from wild-type mice and Kdm5c KO mice in standard conditions (H3K4me3, H3K4me1, and Kdm5C) or upon novelty exposure (H3K4me3), and control mice and Kdm5c ifKO mice in basal state (H3K4me3).

Project description:Chromatin immunoprecipitation (ChIP) experiments were conducted as previously described (Ito et al, 2013) using anti-H3K4me3 (Millipore, #07-473), anti-H3K4me1 (Abcam, #ab8895), or anti-Kdm5C (Iwase et al., 2016). Hippocampi derived from two different animals were pooled together for each sample and two independent biological replicates per condition were sequenced according to manufacturer instructions in a HiSeq2500 apparatus (Illumina, Inc). Information on library preparation method, size of the libraries, and mapping to reference genome can be found in Supplementary Material accompanying the manuscript. ChIP-seq reads were aligned to the mouse genome (Mus_musculus.GRCm.38.83) using bowtie2 (v2.2.9) (Langmead and Salzberg, 2012) and further processed using samtools (v1.3.1) (Li et al., 2009). Peak calling was performed using MACS2 (v2.1.0) (Zhang et al., 2008) with default parameters except for Kdm5c that were as follows: -q 0.01 --nomodel --extsize 131 --broad --broad-cutoff 0.1. Read counts on aligned bam files were performed using Rsubread (v1.22.3) (Liao et al., 2014). Differential peak methylation analysis for H3K4me3 chromatin mark was performed using DESeq2 (v1.10.0) (Love et al., 2014) of the bioconductor suite (Huber et al., 2015) in the R (v3.3) statistical computing platform. For consideration of differentially methylated regions between conditions, we used adjusted p-value < 0.05 as indicated in the manuscript.

2017-08-18 | GSE85873 | GEO

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