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Biomedical subjects

Jörg Grigull

Publications and source records attributed to Jörg Grigull.

4 recordsLinked to original sources

Exploration of essential gene functions via titratable promoter alleles.

Nearly 20% of yeast genes are required for viability, hindering genetic analysis with knockouts. We created promoter-shutoff strains for over two-thirds of all essential yeast genes and subjected them to morphological analysis, size profiling, drug sensitivity screening, and microarray expression profiling. We then used this compendium of data to ask which phenotypic features characterized different functional classes and used these to infer potential functions for uncharacterized genes. We identified genes involved in ribosome biogenesis (HAS1, URB1, and URB2), protein secretion (SEC39), mitochondrial import (MIM1), and tRNA charging (GSN1). In addition, apparent negative feedback transcriptional regulation of both ribosome biogenesis and the proteasome was observed. We furthermore show that these strains are compatible with automated genetic analysis. This study underscores the importance of analyzing mutant phenotypes and provides a resource to complement the yeast knockout collection.

Alleles↗

Genome-wide analysis of mRNA stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis factors.

Using DNA microarrays, we compared global transcript stability profiles following chemical inhibition of transcription to rpb1-1 (a temperature-sensitive allele of yeast RNA polymerase II). Among the five inhibitors tested, the effects of thiolutin and 1,10-phenanthroline were most similar to rpb1-1. A comparison to various microarray data already in the literature revealed similarity between mRNA stability profiles and the transcriptional response to stresses such as heat shock, consistent with the fact that the general stress response includes a transient shutoff of general mRNA transcription. Genes encoding factors involved in rRNA synthesis and ribosome assembly, which are often observed to be coordinately down-regulated in yeast microarray data, were among the least stable transcripts. We examined the effects of deletions of genes encoding deadenylase components Ccr4p and Pan2p and putative RNA-binding proteins Pub1p and Puf4p on the genome-wide pattern of mRNA stability after inhibition of transcription by chemicals and/or heat stress. This examination showed that Ccr4p, the major yeast mRNA deadenylase, contributes to the degradation of transcripts encoding both ribosomal proteins and rRNA synthesis and ribosome assembly factors and mediates a large part of the transcriptional response to heat stress. Pan2p and Puf4p also contributed to the degradation rate of these mRNAs following transcriptional shutoff, while Pub1p preferentially stabilized transcripts encoding ribosomal proteins. Our results indicate that the abundance of ribosome biogenesis factors is controlled at the level of mRNA stability.

Gene Expression Profiling↗

A panoramic view of yeast noncoding RNA processing.

Predictive analysis using publicly available yeast functional genomics and proteomics data suggests that many more proteins may be involved in biogenesis of ribonucleoproteins than are currently known. Using a microarray that monitors abundance and processing of noncoding RNAs, we analyzed 468 yeast strains carrying mutations in protein-coding genes, most of which have not previously been associated with RNA or RNP synthesis. Many strains mutated in uncharacterized genes displayed aberrant noncoding RNA profiles. Ten factors involved in noncoding RNA biogenesis were verified by further experimentation, including a protein required for 20S pre-rRNA processing (Tsr2p), a protein associated with the nuclear exosome (Lrp1p), and a factor required for box C/D snoRNA accumulation (Bcd1p). These data present a global view of yeast noncoding RNA processing and confirm that many currently uncharacterized yeast proteins are involved in biogenesis of noncoding RNA.

Cells, Cultured↗

FunSpec: a web-based cluster interpreter for yeast.

BACKGROUND: For effective exposition of biological information, especially with regard to analysis of large-scale data types, researchers need immediate access to multiple categorical knowledge bases and need summary information presented to them on collections of genes, as opposed to the typical one gene at a time. RESULTS: We present here a web-based tool (FunSpec) for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations (e.g. functional roles, biochemical properties, localization). FunSpec is available online at http://funspec.med.utoronto.ca CONCLUSION: FunSpec is helpful for interpretation of any data type that generates groups of related genes and proteins, such as gene expression clustering and protein complexes, and is useful for predictive methods employing "guilt-by-association."

Cluster Analysis↗