PubMed · 16254553
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2005. Framework for a fully powered risk engine.. https://doi.org/10.1038/ng1105-1153
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In Saccharomyces cerevisiae 6 closely related proteins (Did2p, Vps2p, Vps24p, Vps32p, Vps60p, Vps20p) form part of the extended ESCRT III complex. This complex is required for the formation of multivesicular bodies and the degradation of internalized transmembrane receptor proteins. In contrast the human genome encodes 10 homologous proteins (CHMP1A (approved gene symbol PCOLN3), 1B, 2A, 2B, 3 (approved gene symbol VPS24), 4A, 4B, 4C, 5, and 6). In this study we have performed a series of protein interaction experiments to generate a more comprehensive picture of the human CHMP protein-interaction network. Our results describe novel interactions between known components of the human ESCRT III complex and identify a range of putative binding partners, which may indicate new ways in which the function of human CHMP proteins may be regulated. In particular, we show that two further MIT domain-containing proteins (AMSH/STAMBP and LOC129531) interact with multiple components of the human ESCRT III complex.
The NF-kappaB family of transcription factors plays a critical role in numerous cellular processes, particularly the immune response. Our understanding of how the different NF-kappaB subunits act coordinately to regulate gene expression is based on a limited set of genes. We used genome-scale location analysis to identify targets of all five NF-kappaB proteins before and after stimulation of monocytic cells with bacterial lipopolysaccharide (LPS). In unstimulated cells, p50 and p52 bound to a large number of gene promoters that were also occupied by RNA polymerase II. After LPS stimulation, additional NF-kappaB subunits bound to these genes and to other genes. Genes that became bound by multiple NF-kappaB subunits were the most likely to show increases in RNA polymerase II occupancy and gene expression. This study identifies NF-kappaB target genes, reveals how the different NF-kappaB proteins coordinate their activity, and provides an initial map of the transcriptional regulatory network that underlies the host response to infection.
Genetic association studies of common disease often rely on linkage disequilibrium (LD) along the human genome and in the population under study. Although understanding the characteristics of this correlation has been the focus of many large-scale surveys (culminating in genomewide haplotype maps), the results of different studies have yielded wide-ranging estimates. Since understanding these differences (and whether they can be reconciled) has important implications for whole-genome association studies, in this article we dissect biases in these estimations that are due to known aspects of study design and analytic methodology. In particular, we document in the empirical data that the long-known complicating effects of allele frequency, marker density, and sample size largely reconcile all large-scale surveys. Two exceptions are an underappraisal of redundancy among single-nucleotide polymorphisms (SNPs) when evaluation is limited to short regions (as in candidate-gene resequencing studies) and an inflation in the extent of LD in HapMap phase I, which is likely due to oversampling of specific haplotypes in the creation of the public SNP map. Understanding these factors can guide the understanding of empirical LD surveys and has implications for genetic association studies.