A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.In the past few years, gene-expression microarrays and other general molecular profiling technologies have been applied to a wide range of biological problems and have contributed to discoveries about the complex network of biochemical processes underlying living Correspondence should be addressed to E.E.S. (eric_schadt@merck.com). Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. NIH Public Access Author ManuscriptNat Genet. Author manuscript; available in PMC 2010 March 18. Published in final edited form as:Nat Genet. 2005 July ; 37(7): 710-717. doi:10.1038/ng1589. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript systems 1 , common human diseases 2,3 and gene discovery and structure determination [4][5][6] . Microarrays have also helped to identify biomarkers 7 , disease subtypes 3,8,9 and mechanisms of toxicity 10 and, more recently, to elucidate the genetics of gene expression in human populations 11,12 and to reconstruct gene networks by integrating gene-expression and genetic data 13 . The use of molecular profiling technologies as tools to identify genes underlying common, polygenic diseases has been less successful. Hundreds or even thousands of genes whose expression changes are associated with disease traits have been identified, but determining which of the genes cause disease rather than respond to the disease state has proven difficult.Microarray data have recently been combined with other experimental approaches to facilitate identification of key mechanistic drivers of complex traits 3,[13][14][15][16][17] . One such technique involves treating relative transcript abundances as quantitative traits in segregating populations. In this method, chromosomal regions that control the level of expression of a particular gene are mapped as expression quantitative trait loci (eQTLs). Gene-expression QTLs that contain the gene encoding t...

Communication between the 5' cap structure and 3' poly(A) tail of eukaryotic mRNA results in the synergistic enhancement of translation. The cap and poly(A) tail binding proteins, eIF4E and Pab1p, mediate this effect in the yeast S. cerevisiae through their interactions with different parts of the translation factor eIF4G. Here, we demonstrate the reconstitution of an eIF4E/eIF4G/Pab1p complex with recombinant proteins, and show by atomic force microscopy that the complex can circularize capped, polyadenylated RNA. Our results suggest that formation of circular mRNA by translation factors could contribute to the control of mRNA expression in the eukaryotic cell.

Although the cap structure and the poly(A) tail are on opposite ends of the mRNA molecule, previous work has suggested that they interact to enhance translation and inhibit mRNA degradation. Here we present biochemical data that show that the proteins bound to the mRNA cap (eIF‐4F) and poly(A) tail (Pab1p) are physically associated in extracts from the yeast Saccharomyces cerevisiae. Specifically, we find that Pab1p co‐purifies and co‐immunoprecipitates with the eIF‐4G subunit of eIF‐4F. The Pab1p binding site on the recombinant yeast eIF‐4G protein Tif4632p was mapped to a 114‐amino‐acid region just proximal to its eIF‐4E binding site. Pab1p only bound to this region when complexed to poly(A). These data support the model that the Pablp‐poly(A) tail complex on mRNA can interact with the cap structure via eIF‐4G.

Glucose performs key functions as a signaling molecule in the yeast Saccharomyces cerevisiae. Glucose depletion is known to regulate gene expression via pathways that lead to derepression of genes at the transcriptional level. In this study, we have investigated the effect of glucose depletion on protein synthesis. We discovered that glucose withdrawal from the growth medium led to a rapid inhibition of protein synthesis and that this effect was readily reversed upon readdition of glucose. Neither the inhibition nor the reactivation of translation required new transcription. This inhibition also did not require activation of the amino acid starvation pathway or inactivation of the TOR kinase pathway. However, mutants in the glucose repression (reg1, glc7, hxk2, and ssn6), hexose transporter induction (snf3 rgt2), and cAMP-dependent protein kinase (tpk1(w) and tpk2(w)) pathways were resistant to the inhibitory effects of glucose withdrawal on translation. These findings highlight the intimate connection between the nutrient status of the cell and its translational capacity. They also help to define a new area of posttranscriptional regulation in yeast.

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