Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA sequencing of 968xY0036 fission yeast recombinant library

ABSTRACT: We generated the first recombinant strain library for fission yeast and conducted an RNA-seq based QTL study of the coding, non-coding, and antisense transcriptomes. We cross the lab strain 968 with the lab strain Y0036 to generate segregant. We used RNA-seq for both expression profiling and gentoyping of the segregant and DNA resequencing of the parental strains for variation detection in the whole library.

INSTRUMENT(S): AB SOLiD 4 System

ORGANISM(S): Schizosaccharomyces pombe

SUBMITTER: Mathieu Clement-Ziza

PROVIDER: E-MTAB-2640 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast.

Clément-Ziza Mathieu M Marsellach Francesc X FX Codlin Sandra S Papadakis Manos A MA Reinhardt Susanne S Rodríguez-López María M Martin Stuart S Marguerat Samuel S Schmidt Alexander A Lee Eunhye E Workman Christopher T CT Bähler Jürg J Beyer Andreas A

Molecular systems biology 20141128

Our current understanding of how natural genetic variation affects gene expression beyond well-annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal effects (trans-eQT ...[more]

PMID: 25432776

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Project description:Objective: To define the role of epigenetics, particularly DNA methylation in adaptive vascular growth in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia Methods: Unilateral hindlimb ischemia was induced by ligating femoral artery proximal to the bifurcation of superficial and deep femoral artery. DNA was isolated from ischemic muscles collected at day 7 after ischemia induction using DNeasy Tissue kit (Qiagen). 5 Âµg of genomic DNA was sheared into small fragments with a mean size of 150 bp by using a Covarisâ¢ S2 sonicator System. Quality of the fragmentation was analyzed with Bioanalyzer. Fragmented DNA samples were used for preparation of DNA fragment libraries and sequenced on the SOLiD4 sequencing instrument on one flow cell for 50 bp reads. The sequencing reads were mapped to mus musculus genome build mm9. Data was analyzed by using Bioscope. The samples were normalized with the MEDIPS package in R/B Bioconductor. The analysis of CpG methylation was done primarily in the proximal promoter regions encompassing a region of â1kb upstream of the transcription start site (TSS) and +500 bp downstream of the TSS. Comparisons were performed between hyperlipidemic versus controls and diabetic versus controls to detect differentially methylated regions. R package Limma was used for performing the statistical testing between the groups. Results: When visualizing the whole normalized data the samples did not cluster according to the sample groups. Especially two samples from hyperlipidemic and diabetic ischemic muscles differed clearly from the rest of the samples. To detect the differentially methylated genes, stringent thresholds for p-values and fold change values were chosen to list a reasonable number of genes. Upon filtering, significant differences in the methylation patterns of the sample groups were observed. More importantly, when clustering only the filtered genes, the samples clustered clearly according to the sample groups giving evidence of condition-dependent behavior. Using a threshold of methylation fold change of >1.2 and p value <0.05, we identified 397 and 446 genes to be hypomethylated in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. There were 46 genes commonly shared, but still having a unique pattern of hypomethylation in 371 and 394 genes in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. Similarly, there were 371 and 394 genes hypermethylated in hyperlipidemic and diabetic ischemic respectively compared to controls. Interestingly, we found 264 genes to be commonly hypermethylated, whereas 107 and 130 genes were uniquely hypermethylated in hyperlipidemic and diabetic ischemic muscles respectively. Thus, proximal promoter methylation suggested a shared, yet distinct pattern of DNA methylation in ischemic muscles of hyperlipidemic and type 2 diabetic mice compared to controls. Out of 397 genes that were hypomethylated in hyperlipidemic ischemic muscle, 68 genes were shown to be upregulated in âproinflammatory M1 macrophagesâ as shown by recent studies. Similarly, out of the 371 hypermethylated genes 93 genes were shown to be upregulated in âanti-inflammatory and proangiogenic M2 macrophagesâ as described recently. Out of 446 hypomethylated genes in diabetic ischemic muscle, 65 genes were shown to be upregulated in âproinflammatory M1 macrophagesâ as shown recently. Similarly, out of 394 hypermethylated genes 105 genes were specifically upregulated in âanti-inflammatory and proangiogenic M2 macrophagesâ as shown recently. qRT-PCR analysis suggested an inverse relationship between proximal promoter hypermethylation and mRNA expression in a subset of M2 macrophage specific genes in hyperlipidemic and type 2 diabetic ischemic muscles compared to control ischemic muscles. Conclusions: Our results suggest a role of epigenetics particularly proximal promoter DNA methylation in macrophage polarization and their contribution to angiogenesis and tissue repair in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia. Epigenetics at the level of DNA methylation may act as a deciding factor in promoting a pro or anti-inflammatory phenotype of macrophages critical in cardiovascular diseases. Ischemic skeletal muscle DNA methylation sequencing of triplicate samples from C57BL/6J (WT) mice, hyperlipidemic mice (LDLR-/-ApoB100/100 , C57BL/6J background) and type 2 diabetic mice (IGF-II/LDLR-/-ApoB100/100 , C57BL/6J background) using SoliD4 sequencing platform

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Dataset Information

RNA sequencing of 968xY0036 fission yeast recombinant library

Publications

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast.

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