Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues. The analysis of protein-interaction networks is essential to an understanding of the regulatory processes in a living cell. Many methods have been developed with a view to predicting protein–protein interactions (PPIs) at a genome-wide level, although the differences obtained using these approaches suggest that there are still factors unaccounted for. Barry Honig and colleagues have developed a new way of predicting PPIs that is based on the proteins' three-dimensional structures and functional data. Tests of several predictions of the new algorithm, known as PREPPI, confirm the accuracy of the results. The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5,6. Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7,8,9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

OBJECTIVE: Macrophage recruitment to adipose tissue is a reproducible feature of obesity. However, the events that result in chemokine production and macrophage recruitment to adipose tissue during states of energetic excess are not clear. Sirtuin 1 (SirT1) is an essential nutrient-sensing histone deacetylase, which is increased by caloric restriction and reduced by overfeeding. We discovered that SirT1 depletion causes anorexia by stimulating production of inflammatory factors in white adipose tissue and thus posit that decreases in SirT1 link overnutrition and adipose tissue inflammation. RESEARCH DESIGN AND METHODS: We used antisense oligonucleotides to reduce SirT1 to levels similar to those seen during overnutrition and studied SirT1-overexpressing transgenic mice and fat-specific SirT1 knockout animals. Finally, we analyzed subcutaneous adipose tissue biopsies from two independent cohorts of human subjects. RESULTS: We found that inducible or genetic reduction of SirT1 in vivo causes macrophage recruitment to adipose tissue, whereas overexpression of SirT1 prevents adipose tissue macrophage accumulation caused by chronic high-fat feeding. We also found that SirT1 expression in human subcutaneous fat is inversely related to adipose tissue macrophage infiltration. CONCLUSIONS: Reduction of adipose tissue SirT1 expression, which leads to histone hyperacetylation and ectopic inflammatory gene expression, is identified as a key regulatory component of macrophage influx into adipose tissue during overnutrition in rodents and humans. Our results suggest that SirT1 regulates adipose tissue inflammation by controlling the gain of proinflammatory transcription in response to inducers such as fatty acids, hypoxia, and endoplasmic reticulum stress.

Adiponectin is secreted from adipose tissue in response to metabolic effectors in order to sensitize the liver and muscle to insulin. Reduced circulating levels of adiponectin that usually accompany obesity contribute to the associated insulin resistance. The molecular mechanisms controlling the production of adiponectin are essentially unknown. In this report, we demonstrate that the endoplasmic reticulum (ER) oxidoreductase Ero1-L alpha and effectors modulating peroxisome proliferator-activated receptor gamma (PPAR gamma) and SIRT1 activities regulate secretion of adiponectin from 3T3-L1 adipocytes. Specifically, adiponectin secretion and Ero1-L alpha expression are induced during the early phase of adipogenesis but are then down-regulated during the terminal phase, coincident with an increased expression of SIRT1. Suppression of SIRT1 or activation of PPAR gamma enhances Ero1-L alpha expression and stimulates secretion of high-molecular-weight complexes of adiponectin in mature adipocytes. Suppression of Ero1-L alpha through expression of a corresponding small interfering RNA reduces adiponectin secretion during the differentiation of 3T3-L1 preadipocytes. Moreover, ectopic expression of Ero1-L alpha in Ero1-L alpha-deficient 3T3 fibroblasts stimulates the secretion of adiponectin following their conversion into adipocytes and prevents the suppression of adiponectin secretion in response to activation of SIRT1 by exposure to resveratrol. These findings provide a framework to understand the mechanisms by which adipocytes regulate secretion of adiponectin in response to various metabolic states.