Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Diverse Environmental Stresses Elicit Distinct Responses at the Level of Pre-mRNA Processing in Yeast.

ABSTRACT: Gene expression in Eukaryotic cells is profoundly shaped by the post-transcriptional processing of mRNAs, including the splicing of introns in the nucleus and both nuclear and cytoplasmic degradation pathways. Here we report the use of a splicing isoform specific microarray platform to investigate the effects of a host of diverse stress conditions on both splicing pre-mRNA fate. Interestingly, We find that diverse stresses cause distinct patterns of changes at the level of pre- mRNA processing. The responses we observed are most dramatic for the RPGs and can be categorized into three major classes. The first is characterized by accumulation of RPG pre-mRNA and is seen in multiple types of amino acid starvation regimes; the magnitude of splicing inhibition correlates with the severity of the stress. The second class is characterized by a rapid decrease in both pre- and mature RPG mRNA and is seen in many stresses that inactivate the TORC1 kinase complex. These decreases depend on nuclear turn-over of the intron-containing pre-RNAs. The third class is characterized by a decrease in RPG pre-mRNA with only a modest reduction in the mature species; this response is observed in hyperosmotic and cation-toxic stresses. We show that casein kinase 2 (CK2) makes important contributions to the changes in pre-mRNA processing, particularly for the first two classes of stress responses. In total, our data suggest that complex post-transcriptional programs cooperate to fine-tune expression of intron-containing transcripts in budding yeast. Splicing-specific microarrays were used to assay the changes to splicing caused by a wide variety of environmental stresses and nutrient conditions.

ORGANISM(S): Saccharomyces cerevisiae

SUBMITTER: Gregg Whitworth

PROVIDER: E-GEOD-28919 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:All classes of RNA undergo processing. The two largest RNA processing complexes are those involved in rRNA biogenesis and mRNA splicing. Because of their complexity and essential roles, it has been difficult to dissect the functional interactions within these machines as well as to identify possible cross-talk with other steps in gene expression. Here we report the results of a high-density, quantitative genetic interaction map comprising 107,155 individual interactions involving 552 mutants, 166 of which are hypomorphic alleles of essential genes (This data is available at The Krogan Lab Interactome Database). Our data allow unique insights into the functional organization of the RNA processing machines. For example, we identify unexpected connections involving a component of the 19S proteasome, Sem1/Dss1. We show that Sem1 has genetic, physical and functional interactions with the mRNA export complex, Thp1-Sac3; it also interacts physically with a putative component of the COP9 signalosome, Csn12. We show using splicing-specific microarrays that Csn12 has an unanticipated role in mRNA splicing. Finally, we demonstrate the utility of this dataset in determining functions for uncharacterized ORFs. We show that deletion of the uncharacterized YNR004W causes a very similar splicing defect to that caused by deletion of TGS1, the enzyme that trimethylates the caps of snRNAs and snoRNAs. Keywords: E-MAP, RNA Processing, splicing, splicing-specific microarray, CSN12, COP9 signalosome Splicing-specific microarrays were used to assay the changes to splicing caused by deletion of several non-essential genes which had no previously characterized role in mRNA splicing but were suggested to be involved in this process by high-throughput genetic and/or protein-protein interaction data in Saccharomyces cerevisiae. The data includes samples collected from some mutants in log phase growth at 30 degrees C and from some mutants shifted to 16 degrees C, all competitively hybridized against wild type samples collected under the same conditions. .

Project description:MicroRNAs (miRNAs) in body fluids are candidate diagnostics for a variety of conditions and diseases, including breast cancer. One premise for using extracellular miRNAs to diagnose disease is the notion that the abundance of the miRNAs in body fluids reflects their abundance in the abnormal cells causing the disease. As a result, the search for such diagnostics in body fluids has focused on miRNAs that are abundant in the cells of origin. Here we report that released miRNAs do not necessarily reflect the abundance of miRNA in the cell of origin. We find that release of miRNAs from cells into blood, milk and ductal fluids is selective and that the selection of released miRNAs may correlate with malignancy. In particular, the bulk of miR-451 and miR-1246 produced by malignant mammary epithelial cells was released, but the majority of these miRNAs produced by non-malignant mammary epithelial cells was retained. Our findings suggest the existence of a cellular selection mechanism for miRNA release and indicate that the extracellular and cellular miRNA profiles differ. This selective release of miRNAs is an important consideration for the identification of circulating miRNAs as biomarkers of disease. miRNA microarrays were screened by LC Sciences. One microgram of MCF7 cellular RNA preparation was labeled with Cy3, and one microgram of the extracellular RNA samples was labeled with Cy5. In brief, the RNA was labeled with Cy5 or Cy3 and hybridized to LCSciences standard arrays for mature miRNA of all species available in the Sanger miRBase database (Release 12.0). The data were analyzed including background subtraction, using a LOWESS (locally weighted regression) method on the background-subtracted data. Only transcripts with a signal intensity higher than 3X (background SD) and spot CV<0.5. CV was calculated by (SD)/(signal intensity), and in which repeating probes on the array produced signals from at least 50% of the repeating probes are above detection level.

Dataset Information

Diverse Environmental Stresses Elicit Distinct Responses at the Level of Pre-mRNA Processing in Yeast.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure