Correlation network analysis for data integration and biomarker selection.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.

Systematic analysis of essential yeast TAFs in genome-wide transcription and preinitiation complex assembly.

The general transcription factor TFIID is composed of the TATA box binding protein (TBP) and a set of conserved TBP-associated factors (TAFs). Here we report the completion of genome-wide expression profiling analyses of yeast strains bearing temperature-sensitive mutations in each of the 13 essential TAFs. The percentage of the yeast genome dependent on each TAF ranges from 3% (TAF2) to 59-61% (TAF9). Approximately 84% of yeast genes are dependent upon one or more TAFs and 16% of yeast genes are TAF independent. In addition, this complete analysis defines three distinct classes of yeast promoters whose transcriptional requirements for TAFs differ substantially. Using this collection of temperature-sensitive mutants, we show that in all cases the transcriptional dependence for a TAF can be explained by a requirement for TBP recruitment and assembly of the preinitiation complex (PIC). Unexpectedly, these assembly experiments reveal that TAF11 and TAF13 appear to provide the critical functional contacts with TBP during PIC assembly. Collectively, our results confirm and extend the proposal that individual TAFs have selective transcriptional roles and distinct functions.

Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

Human macrophage activation programs induced by bacterial pathogens.

Understanding the response of innate immune cells to pathogens may provide insights to host defenses and the tactics used by pathogens to circumvent these defenses. We used DNA microarrays to explore the responses of human macrophages to a variety of bacteria. Macrophages responded to a broad range of bacteria with a robust, shared pattern of gene expression. The shared response includes genes encoding receptors, signal transduction molecules, and transcription factors. This shared activation program transforms the macrophage into a cell primed to interact with its environment and to mount an immune response. Further study revealed that the activation program is induced by bacterial components that are Toll-like receptor agonists, including lipopolysaccharide, lipoteichoic acid, muramyl dipeptide, and heat shock proteins. Pathogen-specific responses were also apparent in the macrophage expression profiles. Analysis of Mycobacterium tuberculosis-specific responses revealed inhibition of interleukin-12 production, suggesting one means by which this organism survives host defenses. These results improve our understanding of macrophage defenses, provide insights into mechanisms of pathogenesis, and suggest targets for therapeutic intervention.