Dataset Information

Abundances of transcripts, proteins metabolites and lipids in the cell cycle of budding yeast.

ABSTRACT: This entry contains the proteomic files from a data set of RNAs, proteins, metabolites and lipids analyzed from the same staged samples of S. cerevisiae cells across the cell cycle. Actively growing (un-arrested) diploid cells were collected by elutriation as 8 distinct size pools across the cell cycle in triplicate. Yeast in these 24 aliquots were lysed and soluble and insoluble proteins were prepared for shotgun LC-MS/MS mass spectrometry analysis.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Ophelia Papoulas

LAB HEAD: Edward M. Marcotte

PROVIDER: PXD015273 | Pride | 2019-10-29

REPOSITORIES: Pride

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Project description:MicroRNAs (miRNAs) are single-stranded non-coding RNAs that negatively regulate target gene expression through mRNA cleavage or translational repression. There is mounting evidence that they play critical roles in heart disease. The expression of known miRNAs in the heart has been studied at length by microarray and quantitative PCR but it is becoming evident that microRNA isoforms (isomiRs) are potentially physiologically important. It is well known that left ventricular (patho)physiology is influenced by transmural heterogeneity of cardiomyocyte phenotype, and this likely reflects underlying heterogeneity of gene expression. Given the significant role of miRNAs in regulating gene expression, knowledge of how the miRNA profile varies across the ventricular wall will be crucial to better understand the mechanisms governing transmural physiological heterogeneity. To determinine miRNA/isomiR expression profiles in the rat heart we investigated tissue from different locations across the left ventricular wall using deep sequencing. We detected significant quantities of 145 known rat miRNAs and 68 potential novel orthologs of known miRNAs, in mature, mature* and isomiR formation. Many isomiRs were detected at a higher frequency than their canonical sequence in miRBase and have different predicted targets. The most common miR-133a isomiR was more effective at targeting a construct containing a sequence from the gelsolin gene than was canonical miR-133a, as determined by dual-fluorescence assay. We identified a novel rat miR-1 homolog from a second miR-1 gene; and a novel rat miRNA similar to miR-676. We also cloned and sequenced the rat miR-486 gene which is not in miRBase (v18). Signalling pathways predicted to be targeted by the most highly detected miRNAs include Ubiquitin-mediated Proteolysis, Mitogen-Activated Protein Kinase, Regulation of Actin Cytoskeleton, Wnt signalling, Calcium Signalling, Gap junctions and Arrhythmogenic Right Ventricular Cardiomyopathy. Most miRNAs are not expressed in a gradient across the ventricular wall, with exceptions including miR-10b, miR-21, miR-99b and miR-486. The hearts of 3 male 8 month old Sprague-Dawley rats were rapidly extracted after euthanasia with sodium pentobarbital. A section of the free wall of the left ventricle was dissected into epicardium, mid-myocardium and endocardium by cutting approximately 1 mm from the epicardial and endocardial surfaces. Small RNA was extracted (miRNeasy Kit; Qiagen, Crawley UK), quantified (Nanodrop; Thermo Scientific) and quality assessed for degradation (RNA Nano Chip, Bioanalyser 2100; Aligent Technologies, Wokingham UK; only samples with a RNA integrity no. (RIN) ≥8 were carried forward) and retention of small RNA (Small RNA Chip, Bioanalyser 2100). Small RNA was preferentially ligated with adapters, reverse transcribed into cDNA and amplified with 9 individually tagged primer indices (TruSeq Small RNA Sample Preparation Kit; Illumina, Little Chesterford, UK) and a library of small RNA created for each sample. After gel purification the cDNA products were again analysed on the bioanalyser using a High Sensitivity DNA Chip and assessed for the presence and concentration of the peak corresponding to ligated and tagged miRNA (approximately 147nt). Only samples with suitable RIN values exhibiting good retention of small RNA species were used for library preparation. After pooling, the samples were sequenced by TrinSeq (Trinity Genome Sequencing Lab & Neuropsychiatric Genetics Group, Trinity College Dublin, Ireland (http://www.medicine.tcd.ie/sequencing); using TruSeq SR Cluster Kit v5 (Illumina) and the resultant data trimmed and aligned to miRBase v18 (CLC Genomics Workbench v4.0; CLC bio, Swansea UK).

Dataset Information

Abundances of transcripts, proteins metabolites and lipids in the cell cycle of budding yeast.

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