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:Stable cisplatin- and vincristine-tolerant Group 3 and SHH cell lines were generated by continuous drug exposure with dose escalation to identify mechanisms driving resistance to standard-of-care medulloblastoma therapy. Next-generation sequencing revealed a vastly different transcriptomic landscape following chronic drug exposure, including a drug-tolerant gene expression signature, common to all sequenced drug-tolerant cell lines.
Project description:The transcription factor RUNX1 is frequently mutated in myelodysplastic syndrome and leukemia. RUNX1 mutations can be early events, creating pre-leukemic stem cells that expand in the bone marrow. Here we show that, counter-intuitively, Runx1 deficient hematopoietic stem and progenitor cells (HSPCs) have a slow growth, low biosynthetic, small cell phenotype and markedly reduced ribosome biogenesis (Ribi). The reduced Ribi involves decreased levels of rRNA and many mRNAs encoding ribosome proteins. Runx1 appears to directly regulate Ribi; Runx1 is enriched on the promoters of genes encoding ribosome proteins, and binds the ribosomal DNA repeats. Runx1 deficient HSPCs have lower p53 levels, reduced apoptosis, an attenuated unfolded protein response, and accordingly are resistant to genotoxic and endoplasmic reticulum stress. The low biosynthetic activity and corresponding stress resistance provides a selective advantage to Runx1 deficient HSPCs, allowing them to expand in the bone marrow and outcompete normal HSPCs. Comparison of the phenotypic and molecular properties of normal (Runx1f/f, or WT) versus Runx1 deficient (Mut) hematopoietic stem cells.
Project description:Two medulloblastoma cell lines (ONS-76 and HDMB-03) were grown in 3D hyaluronan hydrogels for three weeks. We observed nodules forming showing different behavior and wanted to evaluate if these different nodules (slow vs fast vs non-growing, migrating and invading cells) are also characterised by different gene expression patterns. We performed this experiment on a SHH (ONS-76) and on a group 3 MB (HDMB-03) cell line to compare if certain subpopulations would be unique for the subgroups.
Project description:In this study,we demonstrated the transcription factor EGR1 is activated by TCM YYJD and such activation mediated YYJD-induced apoptosis in lung cancer cells and provided a novel insight to understand the anti-tumor mechanism of Chinese herb YYJD.
Project description:This study contains single cell RNA-seq of the non-small cell lung cancer line HCC827 in three conditions. Firstly we have sequenced the RNA of single cells of a culture of HCC827 cells grown in normal conditions (POT). In our study we then evolved two arms of the cell line, one in the presence of the drug gefitinib (40nM, G1) and the other in the presence of trametinib (100nM, T4) in HYPERflasks to avoid replating. These data were analysed using cellRanger using GRCh38. Output from cellRanger was filtered for a minimum number of detected genes and UMIs. Mitochondrial reads were excluded from analysis (Acar_2020_single_cell_raw_data.txt). The data was then imported and scaled using the package Seurat. Scaled data was then filtered for low expression of housekeeping genes. Additionally genes expressed in fewer than 20 cells were excluded from analysis. Data was then renormalised using linear normalisation and scaling on the filtered raw data (Acar_2020_single_cell_processed_data.txt). Principal component analysis was run on variable genes identified using Seurat and the top 44 component were used as input for t-SNE analysis. Clusters were then identified using FindClusters in Seurat (Acar_2020_single_cell_metadata.txt). For a full description see the associated publication.
Project description:The yeast S. cerevisiae does not require selenium for growth, so it is an excellent model to investigate the toxicity of this element without interference from its requirement as growth factor as occurs in animal cells. Exposure to selenite interferes with the yeast iron metabolism. Aft2 is a transcription factor related (with Aft1) to maintenance of iron homeostasis. We have found that aft2 cells are highly sensitive to selenite (a common environmental source for selenium). In these experiments we investigate the transcriptional response to a sub-lethal dose of selenite in wild type and aft2 cells. Our results show that the aft2 mutation strongly potentiates the transcriptional response to selenite, showing induction of many genes related to the responses to oxidative stress and DNA damage. Wild type (W303-1A) background and its MML1086 derivative (Daft2) were exposed to 1 mm selenite and samples taken at time 0, 1, 3 and 5 h of selenite treatment. Two biological replicates were prepared for each time-point. Therefore, there are 8 samples per replicate (4 samples for WT and 4 samples for aft2 cells). The WT strain is used here as reference.