RNA sequencing analysis of gene expresssion profiles in spleen and Peyer's patches T follicular helper cells
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE291840 | GEO | 2026/01/28
REPOSITORIES: GEO
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Project description:Purpose: The goals of this study are to investigate the effect of PKM2 KO or TMEM33 overexpression on genes expression. Methods: Fresh PKM2 KO MCF7, PKM2 KO MDA-MB-231 and TMEM33 OE MDA-MB-231 cell pellets, and their respective parental cells, were used for RNA extraction. RNA sequencing libraries were prepared using the Illumina TruSeq RNA Library Prep Kit v2 following the manufacturer’s instructions. Each library was sequenced in single read mode, 1 x 50 bp, using the HiSeq4000 platform. Sequencing reads were aligned to human genome (hg38) by STAR (version 2.5.2b). Expression levels of genes annotated in GENCODE (version 24 for PKM2 KO and their parental cells; version 27 for TMEM33 OE and their control cells) were quantified by RSEM (version 1.3.0). Parameters of STAR and RSEM were set following ENCODE’s long RNA-seq processing pipeline. Differentially expressed genes were identified by DESeq2 (version 1.24.0) under the requirement of at least two-fold changes and an adjusted p-value < 0.05. Results: Differential gene expression analyses identified 269 and 504 significantly up-regulated and 204 and 289 significantly down-regulated genes in MDA-MB-231 and MCF7 PKM2 KO cells compared to their parental cells (>=2-fold, adjusted P value < 0.05); Gene set enrichment analysis (GSEA) showed that cholesterol homeostasis and fatty acid metabolism were among the top down-regulated pathways in MDA-MB-231 TMEM33 OE cells. Moreover, the differential gene expression profiles between PKM2 KO and TMEM33 OE cell lines were positively correlated. Notably, the mRNAs of genes related to cholesterol homeostasis were decreased in both PKM2 KO and TMEM33 OE cells. Conclusions: TMEM33 acts as a downstream effector of PKM2 that regulates activation of SREBPs and lipid metabolism.
2021-08-06 | GSE175956 | GEO
Project description: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.
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