Dataset Information

Transcriptomic profiling of S. cerevisiae and S. boulardii during growth in host-related conditions

ABSTRACT: In this study, the hypothesis that the different phenotypes exhibited by S. cerevisiae and S. boulardii lay on differential gene expression, based on promoter sequencing variability, is approached. To test this possibility, S. boulardii and S. cerevisiae were grown in intestinal like medium (ILM), optimized to enable the growth of both strains, and their transcriptome-wide expression patterns were evaluated through RNA-sequencing. mRNA profiles were generated by deep sequencing, in triplicate, using Illumina NextSeq. The sequence reads that passed quality filters were analyzed with TopHat followed by HTSeq. RNA-seq data allowed to identify genes whose expression is differentially regulated in both organisms with a log2fold change ≥1 and p value <0.01. The RNA-Seq data was also used to refine the current S. boulardii annotation.

ORGANISM(S): Saccharomyces cerevisiae Saccharomyces boulardii (nom. inval.)

PROVIDER: GSE155032 | GEO | 2021/01/12

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: Identification of cellular pathways regulated by AIF1 in bone marrow derived macrophages, based on gene expression patterns identfied by RNA seq. Methods: Imortalized macrophage cell lines were generated from bone marrow of 8 week old WT and Aif1-/- mice. These cells were subject to RNA sequencing in triplicate, using Illumina Nextseq 500, single end read with high output. The sequence reads that passed quality filters were analyzed as follows; Aligned to GRCm38 mouse genome with Tophat (v2.0.13); Gene hits were counted with HTseq(v0.6.1) under default parameters using release 84 of the Mus_musculus.GRCm38; Differential expression analysis was performed in R/Bioconductor following the DESeq2 workflow and annotated with biomaRt. Results: Using an optimized data analysis workflow, we mapped about 35 million sequence reads per sample to the mouse genome (build GRCm38) and identified 26,934 transcripts in WT and Aif1-/- macrophages with a tophat/htseq/DESeq2 workflow. Approximately 4% of the transcripts showed differential expression between the WT and Aif1-/- macrophages, with a fold change ≥1.0 and p value <0.05. Aif1 transcript expression was confirmed in WT and Aif1-/- macrophages. Using qRT–PCR, we validated altered expression of 5 genes involved in the noradrenaline degradation pathway. Unsupervised hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to macrophage AIF1-mediated immune activation and inflammation. Data analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of transcriptomes of WT and Aif1-/- macrophages, with technical replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that Next generation sequencing offers a comprehensive and accurate quantitative and qualitative evaluation of mRNA content within a cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

Dataset Information

Transcriptomic profiling of S. cerevisiae and S. boulardii during growth in host-related conditions

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets