Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA-Seq of bone marrow derived macophages (BMDMs) and brain tissue isolated from wild type and Irgm1 knockout mice

ABSTRACT: To understand the molecular mechanism by which Irgm1 controls innate immune homoestasis

INSTRUMENT(S): Illumina HiSeq 4000

ORGANISM(S): Mus musculus

SUBMITTER: Dr. Santosh Chauhan

PROVIDER: E-MTAB-9164 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Autoimmunity gene IRGM suppresses cGAS-STING and RIG-I-MAVS signaling to control interferon response.

Jena Kautilya Kumar KK Mehto Subhash S Nath Parej P Chauhan Nishant Ranjan NR Sahu Rinku R Dhar Kollori K Das Saroj Kumar SK Kolapalli Srinivasa Prasad SP Murmu Krushna C KC Jain Ashish A Krishna Sivaram S Sahoo Bhabani Sankar BS Chattopadhyay Soma S Rusten Tor Erik TE Prasad Punit P Chauhan Swati S Chauhan Santosh S

EMBO reports 20200727 9

Activation of the type 1 interferon response is extensively connected to the pathogenesis of autoimmune diseases. Loss of function of Immunity Related GTPase M (IRGM) has also been associated to several autoimmune diseases, but its mechanism of action is unknown. Here, we found that IRGM is a master negative regulator of the interferon response. Several nucleic acid-sensing pathways leading to interferon-stimulated gene expression are highly activated in IRGM knockout mice and human cells. Mecha ...[more]

PMID: 32715615

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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].

Dataset Information

RNA-Seq of bone marrow derived macophages (BMDMs) and brain tissue isolated from wild type and Irgm1 knockout mice

Publications

Autoimmunity gene IRGM suppresses cGAS-STING and RIG-I-MAVS signaling to control interferon response.

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