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Differential gene expression induced by Enzalutamide in a prostate cancer xenograft

ABSTRACT: RNA-sequencing data of the prostate cancer xenograft PC346C-DCC-K model. Tumor bearing mice were treated daily with Enzalutamide (N=10, 60mg/kg) or placebo (N=5, vehicle) for a period of 7 days. Tumor were subsequently isolated and snap-frozen. Tumor tissue was lysed and homogenized in QIAzol (ref #79306, Qiagen, Hilden, Germany) using an Ultra-Turrax T25 (Janke & Kunkel, Staufen, Germany). Total RNA was isolated using the miRNA-easy mini kit (ref # 217004, Qiagen), and RNA quality was measured using the Bioanalyzer RNA 6000 Nano assay (ref #5067, Agilent, Santa Clara, California, USA). All RNA samples had a RIN value ≥7. Library Prep was performed using the NEBNext Ultra Directional RNA Kit for Illumina according to the protocol "NEBNext Ultra Directional RNA Library Prep Kit for Illumina" (NEB #E7420S/L). Briefly, mRNA was isolated from total RNA using the oligo-dT magnetic beads. After mRNA fragmentation, cDNA synthesis was performed and used for ligation with adapters and PCR amplification of the resulting product. The quality and yield after sample preparation was measured with the Fragment Analyzer. One sample was re-sequenced due high frequency of multi-mapped reads implicating inadequate depletion of ribosomal RNA. Paired-end RNA-Seq data of enzalutamide PDX samples (N = 15) was analyzed using UCSC human genome build hg38 and GENCODE annotation release v26 (GRCh38.p10) and mouse genome build mm10 (reference strain C57BL/6J) with GENCODE annotation release M15 (GRCm38.p5) for downstream disambiguation of human/mouse data. FASTQC (v0.11.5) was applied on the paired-end FASTQ files for quality control, both before and after running trimmomatic (v0.36), which removed TrueSeq adapter sequences. STAR (v2.5.3a) was used as aligner, with 2-pass mapping for each sample separately. AstraZeneca’s disambiguation algorithm (Python variant, 2013) for reads aligned to two species has been used to assess the best alignments and disambiguate the BAM files. Mapping quality plot was generated and checked based on sambamba Flagstat (v0.6.7) statistics. Count files, with the number of reads for each gene were created with subread FeatureCounts (v1.5.2) on the disambiguated BAM files. R (version 3.4.3) was used for further statistical analyses and data visualization. Differential expression analysis was performed with condition ‘enzalutamide treated’ (N = 10) versus ‘untreated’ (N = 5) using the DESeq2 package (v1.18.1) and the Wald-test. P values were adjusted using the Benjamini-Hochberg procedure. Gene set enrichment analysis (GSEA, v4.1.0) was performed using normalized gene expression values (count per million) of the individual tumor samples, with condition ‘enzalutamide treated’ versus ‘untreated’ and applied to the Molecular Signatures Database (MSigDB) hallmark gene set collection.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE185587 | GEO | 2021/11/07

REPOSITORIES: GEO

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Project description:RNA-seq analysis was performed to understand the role of type I IFN response during SARS CoV-2 infection using transgenic mice. Each sample was collected from an individual C57BL/6J mouse. The total RNA was extracted from uninfected and SARS-CoV-2 infected mice lung tissue using RNeasy mini kit (QIAGEN #74104). The quantity of RNA was determined using Qubit RNA assay kit with Qubit 4.0 and the quality of RNA was tested using agarose gel electrophoresis and High Sensitivity Tape station Kit (Agilent 2200, #5067-5576, #5067-5577 and #5067-5578). After assessing the quality of RNA, ~900 ng of total RNA was taken for library preparation using NEBNext®Ultra™ II Directional RNA Library kit for Illumina (# E7760L) and NEBNext Poly (A) mRNA Magnetic Isolation Module (# E7490L) as per manufacturer's protocol. The prepared library was quantified using Qubit dsDNA assay kit (Invitrogen, Q32851) followed by quality check (QC) and fragment size distribution using a High Sensitivity Tape station Kit (Agilent 2200, #5067-5584 and #5067-5585). The library was sequenced using the HiSeq 4000 Illumina platform. The paired-end (PE) reads quality checks for each sample were carried out using FastQC v.0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The adapter sequence was trimmed using the BBDuk version 37.58 version 37.58 and the alignment was performed using STAR v.2.5.3a with default parameters with human hg38 genome build, gencode v21 gtf 9GRCh38) from the gencode. The duplicates were discarded using Picard-2.9.4 (https://broadinstitute.github.io/picard/) from the aligned bam files and read counts were generated using featureCount v.1.5.3 from subread-1.5.3 package (https://bioinf.wehi.edu.au/) with Q = 10 for mapping quality. The count files were used as input for downstream differential gene expression analysis with DESeq2 version 1.14.1 9. The genes with read counts of ≤ 10 in any comparison were discarded followed by count transformation and statistical analysis using DESeq “R”. The “P” value were adjusted using the Benjamini and Hochberg multiple testing correction and the differentially expressed genes were identified (fold change of ≥1.5, P-value < 0.05). A unified non-redundant gene list was made for different comparisons and subjected to gene ontology (GO) analysis using the reactome database (https://reactome.org/). The top pathways (p < 0.05) were used for generating heat maps using Complexheatmap (Version 2.0.0) through unsupervised hierarchical clustering. The expression clusters were annotated based on enriched GO terms. Normalized gene expression was used to generate the boxplots with a median depicting the trends in the expression across the different conditions using ggplot2 [version 3.3.5]. The pathways analysis was performed using Metascape database (https://metascape.org/gp/index.html#/main/step1). The top pathways (p < 0.05) were taken for constructing bubble plots using ggplot2 [version 3.3.5].

Project description:Purpose: We identified novel methyl reader domain protein in P. falciparum. Using Next Generation Sequencing, we studied the differential gene expression in PfCDP conditionally deleted P. falciparum versus wild type. Methods: Transcriptome analysis of PfCDP KO vs WT P. falciparum using high throughput RNA sequencing in triplicate (Three controls and three KO samples). The total RNA was isolated from P. falciparum WT and PfCDP KO lines, the quality of RNA was assessed using Nanodrop2000, and Bioanalyzer 2100. RNA sequencing libraries were prepared with Illumina-compatible NEBNext® Ultra™ II Directional RNA Library Prep Kit. The double stranded cDNA was purified using JetSeq Beads and the purified cDNA was end-repaired, adenylated and ligated to Illumina multiplex barcode adapters as per NEBNext® Ultra™ II Directional RNA Library Prep protocol. The libraries were sequenced on IlluminaHiSeq 4000 sequencer for 150 bp paired-end chemistry. The data obtained from the sequencing run was de-multiplexed using Bcl2fastq software v2.20 and FastQ files were generated based on the unique dual barcode sequences. The sequencing quality was assessed using the FastQC software ver. 0.11.9. The adaptor sequences and low quality bases (phred quality cutoff set at 30) were trimmed from the reads using the Trim Galore tool ver. 0.6.5. The reads that survived quality check were aligned to the Plasmodium falciparum 3D7 isolate genome (ver. 46) using the Hisat2 read alignment tool. The library strandedness was set as firststrand in accordance with the strand specific library preparation protocol. The alignement output files in the sam format were sorted and converted to the bam format using the Samtools utilities (ver 1.10). Results: The transcript abundance count data was generated using the HTSeq-count function (ver. 0.12.4). Differential gene expression analysis was performed using the DESeq2 package (ver. 3.11) in R programming environment. Genes were classified as deregulated on the basis of log2fold-change cutoff of +1 (for upregulation) and -1 (for downregulation) and p-value cutoff of 0.05. Gene Ontology analysis was performed using the GO tool hosted by the PlasmoDB web database. Data visualisation of the DGE was performed using custom scripts in R programming environment and with DESeq2 package utilities. The DGE analysis has revealed that up regulation of a subsets virulence genes family with significant p values. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, 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.

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Differential gene expression induced by Enzalutamide in a prostate cancer xenograft

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