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Intrahepatic MAIT cell gene expression revealed by RNA-seq

ABSTRACT: RNA-seq was carried out as described by Simone Picelli et al. with minor modifications (Genome Res 2014;24:2033-40). Briefly, RNA was extracted from 5000 cells using a miRNeasy Micro Kits (Qiagen, German). RNA quality was assessed with an Agilent RNA 6000 Pico Kit (Agilent Technologies, cat#5067-1513, USA) on an Agilent 2100 Bioanalyzer. Each library was prepared from 2ng of total RNA. After reverse transcription, cDNA was amplified for 8 cycles followed by Agencourt AMPure XP purification, the quality of cDNA library was checked on an Agilent High Sensitivity DNA Kit (Agilent Technologies, cat#5067-4626). The cDNA was sheared to a 200-500 bp size range using the Covaris AFA system. The final library was carried out by using the NEB Next ULTRA II DNA Prep Kit, and their quality and size were checked using the Agilent High Sensitivity DNA Kit. The libraries were sequenced using a Hiseq 4000 system (Illumina, USA). At least million reads were obtained for each sample. For all RNA-seq reads, we cut the 5’ adaptor (AAGCAGTGGTATCAACGCAGAGTACAT GGG) using cutadapt (version 1.10) with the parameter –e 0.17, and then aligned to the hg19 genome using OSA (version 2.10.8). The aligned data were merged and count by Samtools (version 0.1.19+), and differentially expressed genes were found based on the edgeR with default parameters (Bioinformatics 2010;26:139-40). A gene was differentially expressed if (1) p < 0.05 (2) |log2(fold change)| > 1. For hierarchical clustering, the RPKM values of differentially expressed genes were log2-transformed and standardized. 1- Pearson correlation was then used as the distance for clustering. Differentially expressed genes were selected for GO analysis using R package clusterProfiler (Omics 2012;16:284-7).

ORGANISM(S): Homo sapiens

PROVIDER: GSE117627 | GEO | 2018/12/01

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: Zinc Finger MIZ-Type Containing 1 (Zmiz1) is a member of the PIAS family of protein and function as a transcriptional coactivator of Notch, Androgen Receptor (AR), p53, Estrogen Receptor (ER), and Smad3/4 . Despite Zmiz1 critical role in angiogenesis, its role in lymphatic vasculature is unknown. Here, we use HDLECs cell line to profile transcriptional changes upon Zmiz1 knockdown using siRNA. Methods: Total RNA was extracted from control and Zmiz1 siRNA transfected HDLECs using GeneJET RNA Purification Kit (Thermo Fisher Scientific, K0732) following the manufacturer instructions. RNA concentration and RNA integrity (RIN) number were determined using Qubit RNA High Sensitivity Assay Kit (Thermo Fisher Scientific, Q32852) and Bioanalyzer RNA 6000 Nano assay kit (Agilent, 5067-1511) respectively. RNA library was prepared using TruSeq RNA Library Prep Kit v2 (Illumina, RS-122-2001) according to the manufacturer instructions. mRNA library was quantified using Qubit dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific, Q32851) and verified using the Bioanalyzer DNA1000 assay kit (Agilent, 5067-1505). Verified samples were sequenced using the NextSeq1000/2000 P2 Reagents (200 Cycles) v3 (Illumina, 20046812) on a Nextseq1000/2000. Sequenced reads were aligned to the human (hg19) reference genome with RNA-Seq alignment tool (STAR aligner). The aligned reads were used to quantify mRNA expression and determine differentially expressed genes using the RNA-Seq Differential Expression tool (version 1.0.1). Both alignment and differential expression analysis were performed using the tools in the illumina BaseSpace Sequence Hub. Results: We found 2,228 differentially expressed genes of which 1,115 genes were upregulated while 1,113 genes were downregulated. Downregulated genes were enriched in biological processes such as lymph vessel development, cell migration and heart valve development. Conclusions: We identify Zmiz1 regulates Prox1 in lymphatic endothelial cells

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Intrahepatic MAIT cell gene expression revealed by RNA-seq

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