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A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6 (NMR assay)

ABSTRACT:

Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12Ac ChIP-seq data for wild-type and mip6delta strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress.

INSTRUMENT(S): Nuclear Magnetic Resonance (NMR) -

SUBMITTER: Susana Rodriguez Navarro

PROVIDER: MTBLS1320 | MetaboLights | 2020-02-14

REPOSITORIES: MetaboLights

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Project description:The main goal of the project is to develop a new generation of bioinformatics resources for the integrative analysis of multiple types of 'omics data. These resources include both novel statistical methodologies as well as user-friendly software implementations. STATegra methods address many aspects of the 'omics data integration problem such as the design of multi-omics experiments, integrative transcriptional and regulatory networks, integrative variable selection, data fusion, integration of public domain data, and integrative pathway analysis. To support method development STATegra uses a model biological system, namely the differentiation process of mouse pre-B-cells. The STATegra consortium generated data focused on a critical step in the differentiation of B lymphocytes, which are key components of the adaptive immune system. Transcription factors of the Ikaros family are central to the normal differentiation of B cell progenitors and their expression increases in response to developmental stage-specific signals to terminate the proliferation of B cell progenitors and to initiate their differentiation. In particular, a novel biological system that models the transition from the pre-BI stage to the pre-BII subsequent stage, where B cell progenitors undergo growth arrest and differentiation, was used. The approach involves a pre-B cell line, B3, and an inducible version of the Ikaros transcription factor, Ikaros-ERt2. Ikaros factors act to down-regulate genes that drive proliferation and to simultaneously up-regulate the expression of genes that promote the differentiation of B cell progenitors. Hence, in the B3 system, before induction of Ikaros, cells proliferate and their gene expression pattern is similar to proliferating B cell progenitors in vivo. Following Ikaros induction, B3 cells undergo gene expression changes that resemble those that occur in vivo during the transition from cycling to resting pre-B cells, followed by a marked reduction in cellular proliferation and by G1 arrest. On this system the consortium has created a high-quality data collection consisting of a replicated time course using seven different 'omics platforms: RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, Methyl-seq, proteomics and metabolomics, which is used to assess and to validate STATegra methods. The STATegra experimental design consists of a 6 point time course that captures the differentiation of B3 cells containing Ikaros-ERt2 upon Ikaros induction within a 24 hr period. The process was sampled at 0, 2, 6, 12, 18, and 24 hrs respectively after Ikaros induction by Tamoxifen. As control, B3 cells transfected with an empty vector were treated and sampled in the same way as the inducible line. Eight different 'omic technologies were measured on this system: RNA-seq, miRNA-seq, DNase-seq, RRBS-seq, ChIP-seq, scRNA-seq, Proteomics and Metabolomics. Generally, three biological replicates were obtained per platform and were processed as independent batches. Metabolomics analysis was performed using 4 biological batches (7, 8, 9 and 10) and at times 0, 2, 6, 12, 18 and 24 hrs respectively. Each sample was analyzed using gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS).

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A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6 (NMR assay)

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