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8 - mRNA profiling of pancreatic beta-cells: investigating mechanisms of diabetes

Published online by Cambridge University Press:  05 September 2009

By
Leentje Van Lommel ,
Yves Moreau ,
Daniel Pipeleers ,
Jean-Christophe Jonas  and
Frans Schuit
Edited by
Wolf-Karsten Hofmann
Leentje Van Lommel
Affiliation:
Gene Expression Unit, Department of Molecular Cell Biology, K. U. Leuven, Belgium
Yves Moreau
Affiliation:
Department of Electrical Engineering, ESAT–SCD, K. U. Leuven, Belgium
Daniel Pipeleers
Affiliation:
Diabetes Research Center, Vrije Universiteit Brussel, Belgium
Jean-Christophe Jonas
Affiliation:
Unit of Endocrinology and Metabolism, Faculty of Medicine, Université Catholique de Louvain, Brussels, Belgium
Frans Schuit
Affiliation:
Gene Expression Unit, Department of Molecular Cell Biology, K. U. Leuven, Belgium
Wolf-Karsten Hofmann
Affiliation:
Charite-University Hospital Benjamin Franklin, Berlin
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Summary

The pancreatic beta-cell in health and disease

Diabetes is one of the most common health problems of our age, affecting more than 150 million patients worldwide today and perhaps will affect twice as many a few decades on from now [1]. In the vast majority of patients, the exact cause of their disease state is unknown. Diabetes can be caused by autoimmune destruction of insulin-producing beta-cells in the pancreas (type 1 diabetes) or by a failure of the beta-cells to produce/release insulin in sufficient amounts to meet the metabolic demand (type 2 diabetes). The latter group represents about 90% of all diabetic patients and today is one of the most prevalent existing health problems. The high prevalence, chronic nature, and unknown cause of diabetes are strong arguments for studying the beta-cell in order to identify mechanisms by which beta-cells are destroyed or made dysfunctional.

Identification of key aspects of beta-cell dysfunction at the molecular detail requires that the molecules in the normal beta-cell, as well as their mode of action, are known. The rationale to build up such knowledge in a biomedical context is dual. First, it is expected that such insight will assist in the process of finding new ways of drug treatment for type 2 patients, so that beta-cell dysfunction can be corrected in a more efficient way than is possible today. Second, this type of knowledge will define the endpoint of investigations that aim to generate new beta-cells from pancreatic or extra-pancreatic precursor cells.

Type
Chapter
Information
Gene Expression Profiling by Microarrays
Clinical Implications
 , pp. 187 - 211
Publisher: Cambridge University Press
Print publication year: 2006

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References

Zimmet, P., Alberti, K. G., and Shaw, J.Global and societal implications of the diabetes epidemic. Nature 2001; 414: 782–7.CrossRefGoogle ScholarPubMed
Newgard, C. B. and McGarry, J. D.Metabolic coupling factors in pancreatic beta-cell signal transduction. Annu. Rev. Biochem. 1995; 64: 689–719.CrossRefGoogle ScholarPubMed
Henquin, J. C.Triggering and amplifying pathways of regulation of insulin secretion by glucose. Diabetes 2000; 49: 1751–60.CrossRefGoogle Scholar
Schuit, F. C., Huypens, P., Heimberg, H., and Pipeleers, D. G.Glucose sensing in pancreatic beta-cells: a model for the study of other glucose-regulated cells in gut, pancreas, and hypothalamus. Diabetes 2001; 50: 1–11.CrossRefGoogle Scholar
Creutzfeldt, W. and Ebert, R.New developments in the incretin concept. Diabetologia 1985; 28: 565–73.CrossRefGoogle ScholarPubMed
Holst, J. J. and Gromada, J.Role of incretin hormones in the regulation of insulin secretion in diabetic and nondiabetic humans. Am. J. Physiol. Endocrinol. Metab. 2004; 287: E199–206.CrossRefGoogle ScholarPubMed
Hinke, S. A., Hellemans, K., and Schuit, F. C.Plasticity of the beta cell insulin secretory competence: preparing the pancreatic beta cell for the next meal. J. Physiol. 2004; 558: 369–80.CrossRefGoogle ScholarPubMed
Webb, G. C., Akbar, M. S., Zhao, C., and Steiner, D. F.Expression profiling of pancreatic beta cells: glucose regulation of secretory and metabolic pathway genes. Proc. Natl Acad. Sci. USA 2000; 97: 5773–8.CrossRefGoogle ScholarPubMed
Welsh, M., Scherberg, N., Gilmore, R., and Steiner, D. F.Translational control of insulin biosynthesis. Evidence for regulation of elongation, initiation and signal-recognition-particle-mediated translational arrest by glucose. Biochem. J. 1986; 235: 459–67.CrossRefGoogle Scholar
Lilla, V., Webb, G., Rickenbach, K.et al. Differential gene expression in well-regulated and dysregulated pancreatic beta-cell (MIN6) sublines. Endocrinology 2003; 144: 1368–79.CrossRefGoogle ScholarPubMed
Flamez, D., Berger, V., Kruhoffer, M., Orntoft, T., Pipeleers, D., and Schuit, F. C.Critical role for cataplerosis via citrate in glucose-regulated insulin release. Diabetes 2002; 51: 2018–24.CrossRefGoogle ScholarPubMed
Farfari, S., Schulz, V., Corkey, B., and Prentki, M.Glucose-regulated anaplerosis and cataplerosis in pancreatic beta-cells: possible implication of a pyruvate/citrate shuttle in insulin secretion. Diabetes 2000; 49: 718–26.CrossRefGoogle ScholarPubMed
Lu, D., Mulder, H., Zhao, P.et al. 13C NMR isotopomer analysis reveals a connection between pyruvate cycling and glucose-stimulated insulin secretion (GSIS). Proc. Natl Acad. Sci. USA 2002; 99: 2708–13.CrossRefGoogle Scholar
Schuit, F., Flamez, D., Vos, A., and Pipeleers, D.Glucose-regulated gene expression maintaining the glucose-responsive state of beta-cells. Diabetes 2002; 51 (Suppl. 3): S326–32.CrossRefGoogle ScholarPubMed
Zhou, Y. P., Marlen, K., Palma, J. F.et al. Overexpression of repressive cAMP response element modulators in high glucose and fatty acid-treated rat islets. A common mechanism for glucose toxicity and lipotoxicity?J. Biol. Chem. 2003; 278: 51316–23.CrossRefGoogle ScholarPubMed
El Assaad, W., Buteau, J., Peyot, M. L.et al. Saturated fatty acids synergize with elevated glucose to cause pancreatic beta-cell death. Endocrinology 2003; 144: 4154–63.CrossRefGoogle ScholarPubMed
Gauthier, B. R., Brun, T., Sarret, E. J.et al. Oligonucleotide microarray analysis reveals PDX1 as an essential regulator of mitochondrial metabolism in rat islets. J. Biol. Chem. 2004; 279: 31121–30.CrossRefGoogle ScholarPubMed
Shalev, A., Pise-Masison, C. A., Radonovich, M.et al. Oligonucleotide microarray analysis of intact human pancreatic islets: identification of glucose-responsive genes and a highly regulated TGFbeta signaling pathway. Endocrinology 2002; 143: 3695–8.CrossRefGoogle Scholar
Cardozo, A. K., Kruhoffer, M., Leeman, R., Orntoft, T., and Eizirik, D. L.Identification of novel cytokine-induced genes in pancreatic beta-cells by high-density oligonucleotide arrays. Diabetes 2001; 50: 909–20.CrossRefGoogle ScholarPubMed
Cardozo, A. K., Heimberg, H., Heremans, Y.et al. A comprehensive analysis of cytokine-induced and nuclear factor-kappa B-dependent genes in primary rat pancreatic beta-cells. J. Biol. Chem. 2001; 276: 48879–86.CrossRefGoogle ScholarPubMed
Rasschaert, J., Liu, D., Kutlu, B.et al. Global profiling of double stranded RNA- and IFN-gamma-induced genes in rat pancreatic beta cells. Diabetologia 2003; 46: 1641–57.CrossRefGoogle ScholarPubMed
Kutlu, B., Cardozo, A. K., Darville, M. I.et al. Discovery of gene networks regulating cytokine-induced dysfunction and apoptosis in insulin-producing INS-1 cells. Diabetes 2003; 52: 2701–19.CrossRefGoogle ScholarPubMed
Johansson, U., Olsson, A., Gabrielsson, S., Nilsson, B., and Korsgren, O.Inflammatory mediators expressed in human islets of Langerhans: implications for islet transplantation. Biochem. Biophys. Res. Commun. 2003; 308: 474–9.CrossRefGoogle ScholarPubMed
Wang, J., Webb, G., Cao, Y., and Steiner, D. F.Contrasting patterns of expression of transcription factors in pancreatic alpha and beta cells. Proc. Natl Acad. Sci. USA 2003; 100: 12660–5.CrossRefGoogle ScholarPubMed
Mizusawa, N., Hasegawa, T., Ohigashi, I.et al. Differentiation phenotypes of pancreatic islet beta- and alpha-cells are closely related with homeotic genes and a group of differentially expressed genes. Gene 2004; 331: 53–63.CrossRefGoogle Scholar
Parton, L. E., Diraison, F., Neill, S. E.et al. Impact of PPARgamma overexpression and activation on pancreatic islet gene expression profile analyzed with oligonucleotide microarrays. Am. J. Physiol. Endocrinol. Metab. 2004; 287: E390–404.CrossRefGoogle ScholarPubMed
Storey, J. D. and Tibshirani, R.Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 2003; 100: 9440–5.CrossRefGoogle ScholarPubMed
Bordin, S., Amaral, M. E., Anhe, G. F.et al. Prolactin-modulated gene expression profiles in pancreatic islets from adult female rats. Mol. Cell Endocrinol. 2004; 220: 41–50.CrossRefGoogle ScholarPubMed
Hui, H., Wang, C., Li, H.et al. Gene expression profiling of cultured human islet preparations. Diabetes Technol. Ther. 2004; 6: 481–92.CrossRefGoogle ScholarPubMed
Henry, G. L., Zito, K., and Dubnau, J.Chipping away at brain function: mining for insights with microarrays. Curr. Opin. Neurobiol. 2003; 13: 570–6.CrossRefGoogle ScholarPubMed
Pipeleers, D. G., in't Veld, P. A., Van de, W. M., Maes, E., Schuit, F. C., and Gepts, W.A new in vitro model for the study of pancreatic A and B cells. Endocrinology 1985; 117: 806–16.CrossRefGoogle Scholar
Hohmeier, H. E. and Newgard, C. B.Cell lines derived from pancreatic islets. Mol. Cell Endocrinol. 2004; 228: 121–8.CrossRefGoogle ScholarPubMed
Schuit, F. C., in't Veld, P. A., and Pipeleers, D. G.Glucose stimulates proinsulin biosynthesis by a dose-dependent recruitment of pancreatic beta cells. Proc. Natl Acad. Sci. USA 1988; 85: 3865–9.CrossRefGoogle ScholarPubMed
Kiekens, R., In,'., V., Mahler, T., Schuit, F., Van de, W. M., and Pipeleers, D.Differences in glucose recognition by individual rat pancreatic B cells are associated with intercellular differences in glucose-induced biosynthetic activity. J. Clin. Invest. 1992; 89: 117–25.CrossRefGoogle ScholarPubMed
Schuit, F., Moens, K., Heimberg, H., and Pipeleers, D.Cellular origin of hexokinase in pancreatic islets. J. Biol. Chem. 1999; 274: 32803–9.CrossRefGoogle ScholarPubMed
Cole, S. W., Galic, Z., and Zack, J. A.Controlling false-negative errors in microarray differential expression analysis: a PRIM approach. Bioinformatics 2003; 19: 1808–16.CrossRefGoogle ScholarPubMed
Robison, G. A., Butcher, R. W., and Sutherland, E. W.Cyclic AMP. Annu. Rev. Biochem. 1968; 37: 149–74.CrossRefGoogle ScholarPubMed
Pipeleers, D. G., Schuit, F. C., in't Veld, P. A.et al., Interplay of nutrients and hormones in the regulation of insulin release. Endocrinology 1985; 117: 824–33.CrossRefGoogle ScholarPubMed
Schravendijk, C. F., Foriers, A., Hooghe-Peters, E. L.et al. Pancreatic hormone receptors on islet cells. Endocrinology 1985; 117: 841–8.CrossRefGoogle ScholarPubMed
Schuit, F. C. and Pipeleers, D. G.Regulation of adenosine 3′,5′-monophosphate levels in the pancreatic B cell. Endocrinology 1985; 117: 834–40.CrossRefGoogle ScholarPubMed
Moens, K., Heimberg, H., Flamez, D.et al. Expression and functional activity of glucagon, glucagon-like peptide I, and glucose-dependent insulinotropic peptide receptors in rat pancreatic islet cells. Diabetes 1996; 45: 257–61.CrossRefGoogle ScholarPubMed
Schuit, F. C., Derde, M. P., and Pipeleers, D. G.Sensitivity of rat pancreatic A and B cells to somatostatin. Diabetologia 1989; 32: 207–12.CrossRefGoogle Scholar
Schuit, F. C. and Pipeleers, D. G.Differences in adrenergic recognition by pancreatic A and B cells. Science 1986; 232: 875–7.CrossRefGoogle ScholarPubMed
Allemeersch, J., Durinck, S., Vanderhaeghen, R.et al. Benchmarking the CATMA microarray. A novel tool for Arabidopsis transcriptome analysis. Plant Physiol. 2005; 137: 588–601.CrossRefGoogle ScholarPubMed
Mills, J. C. and Gordon, J. I.A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucl. Acids Res. 2001; 29: E72.CrossRefGoogle ScholarPubMed
Aris, V. M., Cody, M. J., Cheng, J.et al. Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer. BMC Bioinformatics 2004; 5: 185.CrossRefGoogle ScholarPubMed
Kerr, M. K., Martin, M., and Churchill, G. A.Analysis of variance for gene expression microarray data. J. Comput. Biol. 2000; 7: 819–37.CrossRefGoogle ScholarPubMed
Lee, M. L., Kuo, F. C., Whitmore, G. A., and Sklar, J.Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl Acad. Sci. USA 2000; 97: 9834–9.CrossRefGoogle ScholarPubMed
Ludbrook, J.Statistics in physiology and pharmacology: a slow and erratic learning curve. Clin. Exp. Pharmacol. Physiol. 2001; 28: 488–92.CrossRefGoogle ScholarPubMed
Long, A. D., Mangalam, H. J., Chan, B. Y., Tolleri, L., Hatfield, G. W., and Baldi, P.Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J. Biol. Chem. 2001; 276: 19937–44.CrossRefGoogle Scholar
Delongchamp, R. R., Bowyer, J. F., Chen, J. J., and Kodell, R. L.Multiple-testing strategy for analyzing cDNA array data on gene expression. Biometrics 2004; 60: 774–82.CrossRefGoogle ScholarPubMed
Smet, F., Moreau, Y., Engelen, K., Timmerman, D., Vergote, I., and Moor, B.Balancing false positives and false negatives for the detection of differential expression in malignancies. Br. J. Cancer 2004; 91: 1160–5.CrossRefGoogle ScholarPubMed
Gao, F., Foat, B. C., and Bussemaker, H. J.Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC. Bioinformatics 2004; 5: 31.CrossRefGoogle ScholarPubMed
Venken, K., Schuit, F., Lommel, L.et al. Growth without growth hormone receptor: estradiol is a major growth hormone-independent regulator of hepatic IGF-1 synthesis. J. Bone Miner. Res. 2005; 20: 2138–49.CrossRefGoogle ScholarPubMed
Baldi, P. and Long, A. D.A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 17: 509–19.CrossRefGoogle ScholarPubMed

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