The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies.

The size complexity of the human genome has been traditionally viewed as an obstacle that frustrates efforts aimed at identifying the genetic correlates of complex human phenotypes. As such complex phenotypes are attributed to the combined action of numerous genomic loci, attempts to identify the underlying multi-locus interactions may produce a combinatorial sum of false positives that drown out the real signal. Faced with such grim prospects for successfully identifying the genetic basis of complex phenotypes, many geneticists simply disregard epistatic interactions altogether. However, the emerging picture from systems biology is that the cellular programs encoded by the genome utilize nested signaling hierarchies to integrate a number of loosely coupled, semiautonomous, and functionally distinct genetic networks. The current view of these modules is that connections encoding inter-module signaling are relatively sparse, while the gene-to-gene (protein-to-protein) interactions within a particular module are typically denser. We believe that each of these modules is encoded by a finite set of discontinuous, sequence-specific, genomic intervals that are functionally linked to association rules, which correlate directly to features in the environment. Furthermore, because these environmental association rules have evolved incrementally over time, we explore theoretical models of cellular evolution to better understand the role of evolution in genomic complexity. Specifically, we present a conceptual framework for (1) reducing genomic complexity by partitioning the genome into subsets composed of functionally distinct genetic modules and (2) improving the selection of coding region SNPs, which results in an increased probability of identifying functionally relevant SNPs. Additionally, we introduce the notion of 'genomic closure,' which provides a quantitative measure of how functionally insulated a specific genetic module might be from the influence of the rest of the genome. We suggest that the development and use of theoretical models can provide insight into the nature of biological systems and may lead to significant improvements in computational algorithms designed to reduce the complexity of the human genome.

St John's wort and imipramine-induced gene expression profiles identify cellular functions relevant to antidepressant action and novel pharmacogenetic candidates for the phenotype of antidepressant treatment response.

Both the prototypic tricyclic antidepressant imipramine (IMI) and the herbal product St John's wort (SJW) can be effective in the treatment of major depressive disorder. We studied hypothalamic gene expression in rats treated with SJW or IMI to test the hypothesis that chronic antidepressant treatment by various classes of drugs results in shared patterns of gene expression that may underlie their therapeutic effects. Individual hypothalami were hybridized to individual Affymetrix chips; we studied three arrays per group treatment. We constructed 95% confidence intervals for expression fold change for genes present in at least one treatment condition and we considered genes to be differentially expressed if they had a confidence interval excluding 1 (or -1) and had absolute difference in expression value of 10 or greater. SJW treatment differentially regulated 66 genes and expression sequence tags (ESTs) and IMI treatment differentially regulated 74 genes and ESTs. We found six common transcripts in response to both treatments. The likelihood of this occurring by chance is 1.14 x 10(-23). These transcripts are relevant to two molecular machines, namely the ribosomes and microtubules, and one cellular organelle, the mitochondria. Both treatments also affected different genes that are part of the same cell function processes, such as glycolytic pathways and synaptic function. We identified single-nucleotide polymorphisms in the human orthologs of genes regulated both treatments, as those genes may be novel candidates for pharmacogenetic studies. Our data support the hypothesis that chronic antidepressant treatment by drugs of various classes may result in a common, final pathway of changes in gene expression in a discrete brain region.