Jörg Grigull

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.

High-definition macromolecular composition of yeast RNA-processing complexes.

A remarkably large collection of evolutionarily conserved proteins has been implicated in processing of noncoding RNAs and biogenesis of ribonucleoproteins. To better define the physical and functional relationships among these proteins and their cognate RNAs, we performed 165 highly stringent affinity purifications of known or predicted RNA-related proteins from Saccharomyces cerevisiae. We systematically identified and estimated the relative abundance of stably associated polypeptides and RNA species using a combination of gel densitometry, protein mass spectrometry, and oligonucleotide microarray hybridization. Ninety-two discrete proteins or protein complexes were identified comprising 489 different polypeptides, many associated with one or more specific RNA molecules. Some of the pre-rRNA-processing complexes that were obtained are discrete sub-complexes of those previously described. Among these, we identified the IPI complex required for proper processing of the ITS2 region of the ribosomal RNA primary transcript. This study provides a high-resolution overview of the modular topology of noncoding RNA-processing machinery.

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

ABSTRACT 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.

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.

Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

BACKGROUND Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. OBJECTIVE Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately. DESIGN, SETTING, AND PARTICIPANTS A total of 94 different subtype cases were analysed. miRNA microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA-specific primers. MEASUREMENTS Microarray data were analysed by standard approaches. Relative expression for qRT-PCR was determined using the ΔΔC(T) method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log(2) value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation. RESULTS AND LIMITATIONS We developed a classification system that can distinguish the different RCC subtypes using unique miRNA signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccRCC) subtype, 97% for papillary RCC (pRCC) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chRCC) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccRCC is more closely related to pRCC, whereas chRCC is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes. CONCLUSIONS MiRNA expression patterns can distinguish between RCC subtypes.

miRNA profiling for clear cell renal cell carcinoma: biomarker discovery and identification of potential controls and consequences of miRNA dysregulation.

PURPOSE Renal cell carcinoma is the most common neoplasm of the adult kidney. Currently to our knowledge there are no biomarkers for diagnostic, prognostic or predictive applications for renal cell carcinoma. miRNAs are nonprotein coding RNAs that negatively regulate gene expression and are potential biomarkers for cancer. MATERIALS AND METHODS We analyzed 70 matched pairs of clear cell renal cell carcinoma and normal kidney tissues from the same patients by microarray analysis and validated our results by quantitative real-time polymerase chain reaction. We also performed extensive bioinformatic analysis to explore the role and regulation of miRNAs in clear cell renal cell carcinoma. RESULTS We identified 166 miRNAs that were significantly dysregulated in clear cell renal cell carcinoma, including miR-122, miR-155 and miR-210, which had the highest over expression, and miR-200c, miR-335 and miR-218, which were most down-regulated. Analysis of previously reported miRNAs dysregulated in RCC showed overall agreement in the direction of dysregulation. Extensive target prediction analysis revealed that many miRNAs were predicted to target genes involved in renal cell carcinoma pathogenesis. In renal cell carcinoma miRNA dysregulation can be attributed in part to chromosomal aberrations, co-regulation of miRNA clusters and co-expression with host genes. We also performed a preliminary analysis showing that miR-155 expression correlated with clear cell renal cell carcinoma size. This finding must be validated in a larger independent cohort. CONCLUSIONS Analysis showed that miRNAs are dysregulated in clear cell renal cell carcinoma and may contribute to kidney cancer pathogenesis by targeting more than 1 key molecule. We identified mechanisms that may contribute to miRNA dysregulation in clear cell renal cell carcinoma. Dysregulated miRNAs represent potential biomarkers for kidney cancer.

Microarray analyses of gene expression during the Tetrahymena thermophila life cycle.

Background The model eukaryote, Tetrahymena thermophila, is the first ciliated protozoan whose genome has been sequenced, enabling genome-wide analysis of gene expression. Methodology/Principal Findings A genome-wide microarray platform containing the predicted coding sequences (putative genes) for T. thermophila is described, validated and used to study gene expression during the three major stages of the organisms life cycle: growth, starvation and conjugation. Conclusions/Significance Of the ∼27,000 predicted open reading frames, transcripts homologous to only ∼5900 are not detectable in any of these life cycle stages, indicating that this single-celled organism does indeed contain a large number of functional genes. Transcripts from over 5000 predicted genes are expressed at levels >5× corrected background and 95 genes are expressed at >250× corrected background in all stages. Transcripts homologous to 91 predicted genes are specifically expressed and 155 more are highly up-regulated in growing cells, while 90 are specifically expressed and 616 are up-regulated during starvation. Strikingly, transcripts homologous to 1068 predicted genes are specifically expressed and 1753 are significantly up-regulated during conjugation. The patterns of gene expression during conjugation correlate well with the developmental stages of meiosis, nuclear differentiation and DNA elimination. The relationship between gene expression and chromosome fragmentation is analyzed. Genes encoding proteins known to interact or to function in complexes show similar expression patterns, indicating that co-ordinate expression with putative genes of known function can identify genes with related functions. New candidate genes associated with the RNAi-like process of DNA elimination and with meiosis are identified and the late stages of conjugation are shown to be characterized by specific expression of an unexpectedly large and diverse number of genes not involved in nuclear functions.

Endometrial Carcinoma Biomarker Discovery and Verification Using Differentially Tagged Clinical Samples with Multidimensional Liquid Chromatography and Tandem Mass Spectrometry

The utility of differentially expressed proteins discovered and identified in an earlier study (DeSouza, L., Diehl, G., Rodrigues, M. J., Guo, J., Romaschin, A. D., Colgan, T. J., and Siu, K. W. M. (2005) Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cleavable ICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 4, 377–386) to discriminate malignant and benign endometrial tissue samples was verified in a 40-sample iTRAQ (isobaric tags for relative and absolute quantitation) labeling study involving normal proliferative and secretory samples and Types I and II endometrial cancer samples. None of these proteins had the sensitivity and specificity to be used individually to discriminate between normal and cancer samples. However, a panel of pyruvate kinase, chaperonin 10, and α1-antitrypsin achieved the best results with a sensitivity, specificity, predictive value, and positive predictive value of 0.95 each in a logistic regression analysis. In addition, three new potential markers were discovered, whereas two other proteins showed promising trends but were not detected in sufficient numbers of samples to permit statistical validation. Differential expressions of some of these candidate biomarkers were independently verified using immunohistochemistry.

Nuclear S100A7 Is Associated with Poor Prognosis in Head and Neck Cancer

Background Tissue proteomic analysis of head and neck squamous cell carcinoma (HNSCC) and normal oral mucosa using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and liquid chromatography-mass spectrometry, led to the identification of a panel of biomarkers including S100A7. In the multi-step process of head and neck tumorigenesis, the presence of dysplastic areas in the epithelium is proposed to be associated with a likely progression to cancer; however there are no established biomarkers to predict their potential of malignant transformation. This study aimed to determine the clinical significance of S100A7 overexpression in HNSCC. Methodology Immunohistochemical analysis of S100A7 expression in HNSCC (100 cases), oral lesions (166 cases) and 100 histologically normal tissues was carried out and correlated with clinicopathological parameters and disease prognosis over 7 years for HNSCC patients. Overexpression of S100A7 protein was significant in oral lesions (squamous cell hyperplasia/dysplasia) and sustained in HNSCC in comparison with oral normal mucosa (ptrend<0.001). Significant increase in nuclear S100A7 was observed in HNSCC as compared to dysplastic lesions (p = 0.005) and associated with well differentiated squamous cell carcinoma (p = 0.031). Notably, nuclear accumulation of S100A7 also emerged as an independent predictor of reduced disease free survival (p = 0.006, Hazard ratio (HR = 7.6), 95% CI = 1.3−5.1) in multivariate analysis underscoring its relevance as a poor prognosticator of HNSCC patients. Conclusions Our study demonstrated nuclear accumulation of S100A7 may serve as predictor of poor prognosis in HNSCC patients. Further, increased nuclear accumulation of S100A7 in HNSCC as compared to dysplastic lesions warrants a large-scale longitudinal study of patients with dysplasia to evaluate its potential as a determinant of increased risk of transformation of oral premalignant lesions.

Global Organization of a Positive-strand RNA Virus Genome

The genomes of plus-strand RNA viruses contain many regulatory sequences and structures that direct different viral processes. The traditional view of these RNA elements are as local structures present in non-coding regions. However, this view is changing due to the discovery of regulatory elements in coding regions and functional long-range intra-genomic base pairing interactions. The ∼4.8 kb long RNA genome of the tombusvirus tomato bushy stunt virus (TBSV) contains these types of structural features, including six different functional long-distance interactions. We hypothesized that to achieve these multiple interactions this viral genome must utilize a large-scale organizational strategy and, accordingly, we sought to assess the global conformation of the entire TBSV genome. Atomic force micrographs of the genome indicated a mostly condensed structure composed of interconnected protrusions extending from a central hub. This configuration was consistent with the genomic secondary structure model generated using high-throughput selective 2′-hydroxyl acylation analysed by primer extension (i.e. SHAPE), which predicted different sized RNA domains originating from a central region. Known RNA elements were identified in both domain and inter-domain regions, and novel structural features were predicted and functionally confirmed. Interestingly, only two of the six long-range interactions known to form were present in the structural model. However, for those interactions that did not form, complementary partner sequences were positioned relatively close to each other in the structure, suggesting that the secondary structure level of viral genome structure could provide a basic scaffold for the formation of different long-range interactions. The higher-order structural model for the TBSV RNA genome provides a snapshot of the complex framework that allows multiple functional components to operate in concert within a confined context.