Vaspin Is an Adipokine Ameliorating ER Stress in Obesity as a Ligand for Cell-Surface GRP78/MTJ-1 Complex

It is unknown whether adipokines derived from adipose tissues modulate endoplasmic reticulum (ER) stress induced in obesity. Here, we show that visceral adipose tissue-derived serine protease inhibitor (vaspin) binds to cell-surface 78-kDa glucose-regulated protein (GRP78), which is recruited from ER to plasma membrane under ER stress. Vaspin transgenic mice were protected from diet-induced obesity, glucose intolerance, and hepatic steatosis, while vaspin-deficient mice developed glucose intolerance associated with upregulation of ER stress markers. With tandem affinity tag purification using HepG2 cells, we identified GRP78 as an interacting molecule. The complex formation of vaspin, GRP78, and murine tumor cell DnaJ-like protein 1 (MTJ-1) (DnaJ homolog, subfamily C, member 1) on plasma membrane was confirmed by cell-surface labeling with biotin and immunoprecipitation in liver tissues and H-4-II-E-C3 cells. The addition of recombinant human vaspin in the cultured H-4-II-E-C3 cells also increased the phosphorylation of Akt and AMP-activated protein kinase (AMPK) in a dose-dependent manner, and anti-GRP78 antibodies completely abrogated the vaspin-induced upregulation of pAkt and pAMPK Vaspin is a novel ligand for cell-surface GRP78/MTJ-1 complex, and its subsequent signals exert beneficial effects on ER stress-induced metabolic dysfunctions. Diabetes 61:2823-2832, 201

Time variations of nitric oxide in the mesosphere and lower thermosphere over Antarctica associated with magnetic storms

The 15th Symposium on Polar Science/Ordinary sessions [OS] Space and upper atmospheric sciences, Wed. 4 Dec. / 3F Conference room, The Institute of Statistical Mathematicsconference outpu

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

GAIA: a gram-based interaction analysis tool – an approach for identifying interacting domains in yeast

<p>Abstract</p> <p>Background</p> <p>Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.</p> <p>Results</p> <p>In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in <it>Saccharomyces cerevisiae </it>(budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.</p> <p>Conclusion</p> <p>GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.</p

A human alpha-fetoprotein enhancer-binding protein, ATBF1, contains four homeodomains and seventeen zinc fingers

We have isolated a full-length cDNA encoding a protein (ATBF1) that binds to an AT-rich motif in the human alpha-fetoprotein gene enhancer. The amino acid sequence deduced from the cDNA revealed that this is the largest DNA-binding protein (306 kDa) known to date, containing four homeodomains, 17 zinc finger motifs, and a number of segments potentially involved in transcriptional regulation. Although the exact function of this protein has not been determined, these structural features suggest that ATBF1 plays a transcriptional regulatory role.</jats:p