Dataset Information

Muscle mRNA profiles from subjects enrolled in the ATLAS clinical study (NCT03464500)

ABSTRACT: Purpose: muscle transcriptomics of subjects supplemented with Urolithin A at different doses or placebo for 4 month. Method: RNA-seq was performed using Illumina HiSeq 4000 sequencing; single read 1 x 50 bp . The quantification of mRNA from the RNA-seq FASTQ files was performed using Salmon. Sample-wise quant.sf files containing raw transcript-level read estimates were read into R, v. 4.0.3 and were combined into a data matrix. Transcripts with very low total counts (< 10) across all samples were filtered out. The data was transformed using the variance stabilizing transformation (VST) method of R package DESeq2, v. 1.30.0. Top 10,000 transcripts with the highest variance across all samples were used for principal component analysis (PCA) using DESeq2. Data transformation and PCA was also done separately for each treatment group. Based on the PCAs, probable outlier samples were excluded and new PCAs were plotted without these samples. The raw transcript-level read count estimates were read in R and summarized to gene-level counts based on the provided transcript and gene ID annotations using summarizeToGene function of R package tximport, v. 1.18.0. DESeqDataSetFromTximport function of DESeq2 was then used for constructing a DESeqDataSet object for DE analysis. Pre-filtering was applied before the DE analysis by excluding genes with < 10 total counts across samples. Subset DE analysis was performed, contrasting Visit time D120 with baseline (BL) and by adjusting for the subject effect. The normalization and DE analysis was done separately for the three different treatment groups. Independent filtering option of DESeq2 was enabled (default), filtering out genes with very low counts and thus unlikely to show significant evidence. R package biomaRt. v. 2.46.0 was used for annotating the results with HGNC gene symbols, gene descriptions and gene biotypes. DESeq2-normalised expression values of all the samples in the given comparison were added to the result tables. Non-adjusted p-value 0.05 was used to filter the results by statistical significance. Results were also generated using DESeq2 function lfcShrink that allows for the shrinkage of the log2 fold change (LFC) estimates toward zero when the information for a gene is low (such as in those cases with low counts or high dispersion values) but has little effect on genes with high counts. The shrinked log2FC values were subsequently used for visualisation and ranking the genes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE197273 | GEO | 2022/06/01

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:We established an Arabidopsis thaliana Columbia (Col-0) and Cucumber mosaic virus strain HYY [CMV(HYY)] patho-system and identified a necrotic cell death. The global gene expression analysis of the necrotic cell death and HR cell death developed in cucumber mosaic virus-inoculated leaves of A. thaliana was performed by RNA-seq, respectively. The samples for RNA-seq were collected from the cell death leaves showing HR cell death at 3dpi and showing necrotic cell death at 5dpi, respectively. The sequence data was first preprocessed by FASTQ in basespace of illumine.The sequence reads were processed with Trimmomatic (version 0.38) for adaptor trimming and quality trimming. The processed reads were mapped to the genome sequences of A.thaliana ecotype Col-0, CMV(Y) and CMV(Ho) with STAR (version 2.7) with its default settings. Read counts per each A.thaliana gene were retrieved by the quantification option of STAR. The obtained read counts were normalized and statistically tested using DESeq2 R package 3.7. Adjusted p-values were calculated with Benjamini-Hochberg procedure, and the threshold for adjusted p-value was set to 0.05 in the present study. The independent filtering in DESeq2 was performed to filter out the genes with low mean normalized counts, with an automatically optimized threshold; 5,906 genes which passed the independent filtering in both of the two comparisons (i.e, necrotic cell death vs mock and HR cell death vs control) were further analyzed for differential expression. Genes with adjusted p-value of <0.05 and a fold change of >4 or <0.25 were considered as DEGs. Of the 5,906 genes analyzed, 202 genes showed >4-fold up-regulation upon either HR cell death or necrotic cell death induction (adjusted p < 0.05). Thirty-five of them were common in HR cell death or necrotic cell death induction, while 149 and 18 were unique to HR cell death or necrotic cell death induction, respectively. Sixty-two genes showed <0.25-fold down-regulation upon either HR or necrosis induction (adjusted p < 0.05). Seven of them were common , while 43 and 12 were specific for HR cell death or necrotic cell death induction, respectively. Collectively, the necrotic cell death displayed a distinct gene expression pattern compare to HR cell death.

Dataset Information

Muscle mRNA profiles from subjects enrolled in the ATLAS clinical study (NCT03464500)

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure