Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.

We report molecular dynamics simulations that induce, over periods of 40-500 ps, the unbinding of biotin from avidin by means of external harmonic forces with force constants close to those of AFM cantilevers. The applied forces are sufficiently large to reduce the overall binding energy enough to yield unbinding within the measurement time. Our study complements earlier work on biotin-streptavidin that employed a much larger harmonic force constant. The simulations reveal a variety of unbinding pathways, the role of key residues contributing to adhesion as well as the spatial range over which avidin binds biotin. In contrast to the previous studies, the calculated rupture forces exceed by far those observed. We demonstrate, in the framework of models expressed in terms of one-dimensional Langevin equations with a schematic binding potential, the associated Smoluchowski equations, and the theory of first passage times, that picosecond to nanosecond simulation of ligand unbinding requires such strong forces that the resulting protein-ligand motion proceeds far from the thermally activated regime of millisecond AFM experiments, and that simulated unbinding cannot be readily extrapolated to the experimentally observed rupture.

Using DNA microarrays together with quantitative proteomic techniques (ICAT reagents, two-dimensional DIGE, and MS), we evaluated the correlation of mRNA and protein levels in two hematopoietic cell lines representing distinct stages of myeloid differentiation, as well as in the livers of mice treated for different periods of time with three different peroxisome proliferative activated receptor agonists. We observe that the differential expression of mRNA (up or down) can capture at most 40% of the variation of protein expression. Although the overall pattern of protein expression is similar to that of mRNA expression, the incongruent expression between mRNAs and proteins emphasize the importance of posttranscriptional regulatory mechanisms in cellular development or perturbation that can be unveiled only through integrated analyses of both proteins and mRNAs. Molecular & Cellular Proteomics 3:960 -969, 2004.Genome-wide mRNA expression profiling by means of DNA microarrays has proven to be a powerful approach in characterizing the changes in biological processes such as disease states, developmental stages, and responses to drugs or genetic perturbations (1). However, DNA arrays measure only the changes at the mRNA level. Most biological functions are executed by the proteins rather than mRNAs. While the expression of many genes is controlled at the transcriptional level, other genes also employ posttranscriptional regulation processes involving mRNA stability, translation initiation, and protein stability. An important issue is the extent to which the changing expression patterns of mRNAs reflect corresponding changes in their cognate proteins. Recent advances in quantitative proteomics, especially the application of ICAT reagents in conjunction with MS, have made possible simultaneous quantitative comparison of hundreds of proteins between two complex mixtures (2). Integrated analyses of mRNA and protein expression data by concurrent measurement of both have revealed moderate to poor correlation in yeast and Halobacteria (3-5). Discordant expression of protein and mRNA was also observed in lung adenocarcinomas (6). However, these analyses examined only one aspect of a biological system, i.e. the steady-state levels of mRNAs and proteins. Another important aspect that concerns the kinetic process of perturbation and how the correlation of mRNA and protein evolves during this process was not addressed. Here, we evaluated the correlation of mRNA and protein expression in mammalian systems under two experimental conditions. In the first, we compared steady-state levels of mRNAs and proteins between two related cell lines representing distinct hematopoietic stages, i.e. multipotent myeloid precursors versus lineage-committed promyelocytic cells. In the second condition, we used a mouse model to demonstrate the kinetic changes in liver mRNA and protein levels in response to treatment with three different drugs. In both cases, we observed a moderate correlation between mRNA and protein levels with the expression of m...

Modern medicine faces the challenge of developing safer and more effective therapies to treat human diseases. Many drugs currently in use were discovered without knowledge of their underlying molecular mechanisms. Understanding their biological targets and modes of action will be essential to design improved second-generation compounds. Here, we describe the use of a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. Specifically, lanosterol synthase in the sterol biosynthetic pathway was identified as a target of the antianginal drug molsidomine, which may explain its cholesterol-lowering effects. Further, the rRNA processing exosome was identified as a potential target of the cell growth inhibitor 5-fluorouracil. This genome-wide screen validated previously characterized targets or helped identify potentially new modes of action for over half of the compounds tested, providing proof of this principle for analyzing the modes of action of clinically relevant compounds.

No abstract

The Rosetta error model captures the variance-intensity relationship for various types of microarray technologies, such as single-color arrays and two-color arrays. This error model conservatively estimates intensity error and uses this value to stabilize the variance estimation. We present two commonly used error models: the intensity error-model for single-color microarrays and the ratio error model for two-color microarrays or ratios built from two single-color arrays. We present examples to demonstrate the strength of our error models in improving statistical power of microarray data analysis, particularly, in increasing expression detection sensitivity and specificity when the number of replicates is limited.

The results provide evidence that genes involved in tissue remodeling and repair are differentially regulated in the lungs of obstructed smokers and suggest that they are potential therapeutic targets. Data deposited in GEO at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8500.

A common inflammatome signature, as well as disease-specific expression patterns, was identified from 11 different rodent inflammatory disease models. Causal regulatory networks and the drivers of the inflammatome signature were uncovered and validated.