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.

The plasma boundary in Single Helical Axis RFP plasmas

Single Helical Axis (SHAx) states obtained in high current reversed field pinch (RFP) plasmas display, aside from a dominant mode in the m=1 spectrum, also a dominant m=0 mode, with the same toroidal mode number as the m=1 one. The two modes have a fixed phase relationship. The island chain created by the m=0 mode across the reversal surface gives rise, at shallow reversal of the toroidal field, to an X-point structure which separates the last closed flux surface from the first wall, creating a divertor-like configuration. The plasma-wall interaction is found to be related to the connection length of the field lines intercepting the wall, which displays a pattern modulated by the dominant mode toroidal periodicity. This configuration, which occurs only for shallow toroidal field reversal, could be exploited to realize an island divertor in analogy to stellarators.Comment: 12 pages, 9 figures Submitted to Nuclear Fusio

Plasma folate levels are associated with the lipoprotein profile: a retrospective database analysis

BACKGROUND: Several studies demonstrated an association of homocysteine plasma levels and the plasma lipoprotein profile. This cross-sectional pilot study aimed at analyzing whether blood levels of the two important cofactors of homocysteine metabolism, folate and vitamin B12, coincide with the lipoprotein profile. METHODS: In a retrospective single center approach, we analyzed the laboratory database (2003-2006) of the University Hospital Bonn, Germany, including 1743 individuals, in whom vitamin B12, folate and at least one lipoprotein parameter had been determined by linear multilogistic regression. RESULTS: Higher folate serum levels were associated with lower serum levels of low density lipoprotein cholesterol (LDL-C; Beta = -0.164; p < 0.001), higher levels of high density lipoprotein cholesterol (HDL-C; Beta = 0.094; p = 0.021 for trend) and a lower LDL-C-C/HDL-C-ratio (Beta = -0.210; p < 0.001). Using ANOVA, we additionally compared the individuals of the highest with those of the lowest quartile of folate. Individuals of the highest folate quartile had higher levels of HDL-C (1.42 +/- 0.44 mmol/l vs. 1.26 +/- 0.47 mmol/l; p = 0.005), lower levels of LDL-C (3.21 +/- 1.04 mmol/l vs. 3.67 +/- 1.10 mmol/l; p = 0.001) and a lower LDL-C/HDL-C- ratio (2.47 +/- 1.18 vs. 3.77 +/- 5.29; p = 0.002). Vitamin B12 was not associated with the lipoprotein profile. CONCLUSION: In our study sample, high folate levels were associated with a favorable lipoprotein profile. A reconfirmation of these results in a different study population with a well defined status of health, diet and medication is warranted

Transcriptome Kinetics Is Governed by a Genome-Wide Coupling of mRNA Production and Degradation: A Role for RNA Pol II

Transcriptome dynamics is governed by two opposing processes, mRNA production and degradation. Recent studies found that changes in these processes are frequently coordinated and that the relationship between them shapes transcriptome kinetics. Specifically, when transcription changes are counter-acted with changes in mRNA stability, transient fast-relaxing transcriptome kinetics is observed. A possible molecular mechanism underlying such coordinated regulation might lay in two RNA polymerase (Pol II) subunits, Rpb4 and Rpb7, which are recruited to mRNAs during transcription and later affect their degradation in the cytoplasm. Here we used a yeast strain carrying a mutant Pol II which poorly recruits these subunits. We show that this mutant strain is impaired in its ability to modulate mRNA stability in response to stress. The normal negative coordinated regulation is lost in the mutant, resulting in abnormal transcriptome profiles both with respect to magnitude and kinetics of responses. These results reveal an important role for Pol II, in regulation of both mRNA synthesis and degradation, and also in coordinating between them. We propose a simple model for production-degradation coupling that accounts for our observations. The model shows how a simple manipulation of the rates of co-transcriptional mRNA imprinting by Pol II may govern genome-wide transcriptome kinetics in response to environmental changes

The functional landscape of mouse gene expression

BACKGROUND: Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function. RESULTS: We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis. CONCLUSIONS: We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics