Guy Augert
Merck & Co.
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Featured researches published by Guy Augert.
Biochemical and Biophysical Research Communications | 2003
Lakshmi Kantham; Lyndal Kerr-Bayles; Nathan Godde; Melissa Quick; Ryan Webb; Terry Sunderland; Judy Bond; Ken Walder; Guy Augert; Gregory Collier
Previously we found elevated beacon gene expression in the hypothalamus of obese Psammomys obesus. Beacon administration into the lateral ventricle of P. obesus stimulated food intake and body weight gain. In the current study we used yeast two-hybrid technology to screen for proteins in the human brain that interact with beacon. CLK4, an isoform of cdc2/cdc28-like kinase family of proteins, was identified as a strong interacting partner for beacon. Using active recombinant proteins and a surface plasmon resonance based detection technique, we demonstrated that the three members of this subfamily of kinases (CLK1, 2, and 4) all interact with beacon. Based on the known sequence and functional properties of beacon and CLKs, we speculate that beacon could either modulate the function of key regulatory molecules such as PTP1B or control the expression patterns of specific genes involved in the central regulation of energy metabolism.
Annals of the New York Academy of Sciences | 2006
Greg R. Collier; Ken Walder; Andrea de Silva; Janette Tenne-Brown; Andrew Sanigorski; David Segal; Lakshmi Kantham; Guy Augert
Abstract: DNA‐based approaches to the discovery of genes contributing to the development of type 2 diabetes have not been very successful despite substantial investments of time and money. The multiple gene‐gene and gene‐environment interactions that influence the development of type 2 diabetes mean that DNA approaches are not the ideal tool for defining the etiology of this complex disease. Gene expression‐based technologies may prove to be a more rewarding strategy to identify diabetes candidate genes. There are a number of RNA‐based technologies available to identify genes that are differentially expressed in various tissues in type 2 diabetes. These include differential display polymerase chain reaction (ddPCR), suppression subtractive hybridization (SSH), and cDNA microarrays. The power of new technologies to detect differential gene expression is ideally suited to studies utilizing appropriate animal models of human disease. We have shown that the gene expression approach, in combination with an excellent animal model such as the Israeli sand rat (Psammomys obesus), can provide novel genes and pathways that may be important in the disease process and provide novel therapeutic approaches. This paper will describe a new gene discovery, beacon, a novel gene linked with energy intake. As the functional characterization of novel genes discovered in our laboratory using this approach continues, it is anticipated that we will soon be able to compile a definitive list of genes that are important in the development of obesity and type 2 diabetes.
Annals of the New York Academy of Sciences | 2006
Ken Walder; David Segal; Sam Chehab; Guy Augert; David Cameron-Smith; Mark Hargreaves; Gregory Collier
Abstract: Objectives/Aim—Microarray (gene chip) technology offers a powerful new tool for analyzing the expression of large numbers of genes in many experimental samples. The aim of this study was to design, construct, and use a gene chip to measure the expression levels of key genes in metabolic pathways related to insulin resistance. Methods—We selected genes that were implicated in the development of insulin resistance, including genes involved in insulin signaling; glucose uptake, oxidation, and storage; fat uptake, oxidation, and storage; cytoskeletal components; and transcription factors. The key regulatory genes in the pathways were identified, along with other recently identified candidate genes such as calpain‐10. A total of 242 selected genes (including 32 internal control elements) were sequence‐verified, purified, and arrayed on aldehyde‐coated slides. Results—Where more than 1 clone containing the gene of interest was available, we chose those containing the genes in the 5′ orientation and an insert size of around 1.5 kb. Of the 262 clones purchased, 56 (21%) were found to contain sequences other than those expected. In addition, 2 (1%) did not grow under standard conditions and were assumed to be nonviable. In these cases, alternate clones containing the gene of interest were chosen as described above. The current version of the Insulin Resistance Gene Chip contains 210 genes of interest, plus 48 control elements. A full list of the genes is available at http://www.hbs.deakin.edu.au/mru/research/gene_chip_tech/genechip_three.htm/. Conclusions—The human Insulin Resistance Gene Chip that we have constructed will be a very useful tool for investigating variation in the expression of genes relevant to insulin resistance under various experimental conditions. Initially, the gene chip will be used in studies such as exercise interventions, fasting, euglycemic‐hyperinsulinemic clamps, and administration of antidiabetic agents.
Diabetes | 2002
Ken Walder; Lakshmi Kantham; Janine McMillan; James L. Trevaskis; Lyndal Kerr; Andrea de Silva; Terry Sunderland; Nathan Godde; Yuan Gao; Natalie Bishara; Kelly Windmill; Janette Tenne-Brown; Guy Augert; Paul Zimmet; Greg R. Collier
Diabetes | 2000
Greg R. Collier; Janine McMillan; Kelly Windmill; Ken Walder; Janette Tenne-Brown; Andrea de Silva; James L. Trevaskis; Sharon Jones; Gregory J. Morton; Scott Lee; Guy Augert; Anthony Civitarese; Paul Zimmet
Diabetes | 2003
Yuan Gao; Ken Walder; Terry Sunderland; Lakshmi Kantham; Helen Feng; Melissa Quick; Natalie Bishara; Andrea de Silva; Guy Augert; Janette Tenne-Brown; Gregory Collier
Archive | 1999
Michel Brunet; Jean Jaques Zeiller; Jean Jaques Berthelon; Francis Contard; Guy Augert; Daniel Guerrier
Regulatory Peptides | 2000
Janine McMillan; Kelly Windmill; Ken Walder; Jim Trevaskis; Janette Tenne-Brown; Sharon Jones; Andrea de Silva; Guy Augert; Anthony Civitarese; Paul Zimmet; Greg R. Collier
Archive | 1998
Diedier Festal; Jean Yves Nioche; Guy Augert; Jacques Deserprit
Archive | 2004
Geneviève Martin; Guy Augert; Maryam Asfari; Hervé Dupont; Daniel Ruggiero; Philippe Durbin