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Identification of new disease mechanisms and treatments for type 2 diabetes based on genetic variants and gene expression networks

Axelsson, Annika LU (2017)
Abstract
Improved understanding of the disease mechanisms underlying type 2 diabetes (T2D) is needed, and so are new treatments.

A new T2D risk variant was recently identified in ADRA2A, which encodes the α2A-adrenergic receptor. The risk allele leads to receptor overexpression in β-cells that causes increased adrenergic signaling and impaired insulin secretion. We showed that the α2A-adrenergic receptor antagonist yohimbine normalized insulin secretion in risk allele carriers with T2D, whereas it was without effect in non-risk allele carriers. These findings suggest that individualized, genotype-based treatment for T2D is possible.

Next, in an attempt to identify new genes relevant for the pathogenesis of T2D and to... (More)
Improved understanding of the disease mechanisms underlying type 2 diabetes (T2D) is needed, and so are new treatments.

A new T2D risk variant was recently identified in ADRA2A, which encodes the α2A-adrenergic receptor. The risk allele leads to receptor overexpression in β-cells that causes increased adrenergic signaling and impaired insulin secretion. We showed that the α2A-adrenergic receptor antagonist yohimbine normalized insulin secretion in risk allele carriers with T2D, whereas it was without effect in non-risk allele carriers. These findings suggest that individualized, genotype-based treatment for T2D is possible.

Next, in an attempt to identify new genes relevant for the pathogenesis of T2D and to identify new drugs for the treatment of T2D, we utilized microarray gene expression data to gain information about gene coexpression networks. Gene expression in human islets from T2D and non-diabetic donors, and gene expression in liver tissue from hyperglycemic and normoglycemic mice, was analyzed to find groups of coexpressed genes (modules) with disturbed expression in diabetes. “Disease signatures” derived from these modules were used to interrogate publically available microarray data sets. These data sets included gene expression profiles induced by a wide range of drugs and treatments. Data sets with an expression pattern similar to our islet disease signature gave clues to the underlying pathogenic process in β-cell failure, and data sets with a reverse expression pattern to our liver disease signature helped identify drug candidates for treatment of excessive hepatic glucose production.

The islet disease signature was associated with β-cell dedifferentiation and loss of a mature β-cell state. We identified the transcription factor SOX5 as a regulator of the T2D-associated islet module. Overexpression of SOX5 increased the expression of β-cell specific genes in human islets and improved secretory function in islets from donors with T2D.

The liver disease signature was used to rate compounds based on reverse expression compared with the disease signature. The rationale was that compounds with potential to reverse the disease signature might
affect the pathophysiology. Sulforaphane, a sulfur-containing compound found naturally in e.g. broccoli, was identified as the top-rated compound. Sulforaphane reduced glucose production from hepatoma cells via a
mechanism that involves reduced expression of gluconeogenic enzymes. Sulforaphane improved glucose tolerance in animal models of diabetes. Moreover, in a small clinical study, sulforaphane-rich broccoli sprout
extract reduced fasting blood glucose and HbA1c levels in obese T2D patients with poor glycemic control.

Taken together, the data presented in this thesis demonstrate the opportunities of genotype-based treatment for T2D, and show the usefulness of gene network analysis to identify pathophysiological mechanisms and new potential therapies for T2D. By this approach, we have identified Sox5 as a new regulator of β-cell function, and sulforaphane as a liver-targeting therapy for T2D patients with poor glycemic control. (Less)
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author
supervisor
opponent
  • professor Carlsson, Per-Ola, Uppsala University
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Type 2 diabetes, insulin, ADRA2A, genotype, gene network analysis, SOX5, drug repositioning, sulforaphane, clinical study, typ 2-diabetes, insulin, ADRA2A, genotyp, gennätverksanalys, SOX5, sulforafan, klinisk studie
pages
94 pages
publisher
Lund University, Faculty of Medicine
defense location
Medelhavet, Inga Marie Nilssons gata 53, Skånes universitetssjukhus i Malmö.
defense date
2017-06-07 09:00
ISBN
978-91-7619-473-7
language
English
LU publication?
yes
id
46286eb1-3605-48b1-a174-9cb71c8a4d8f
date added to LUP
2017-05-16 09:42:25
date last changed
2017-05-19 12:45:49
@phdthesis{46286eb1-3605-48b1-a174-9cb71c8a4d8f,
  abstract     = {Improved understanding of the disease mechanisms underlying type 2 diabetes (T2D) is needed, and so are new treatments.<br/><br/>A new T2D risk variant was recently identified in <em>ADRA2A</em>, which encodes the α2A-adrenergic receptor. The risk allele leads to receptor overexpression in β-cells that causes increased adrenergic signaling and impaired insulin secretion. We showed that the α2A-adrenergic receptor antagonist yohimbine normalized insulin secretion in risk allele carriers with T2D, whereas it was without effect in non-risk allele carriers. These findings suggest that individualized, genotype-based treatment for T2D is possible. <br/><br/>Next, in an attempt to identify new genes relevant for the pathogenesis of T2D and to identify new drugs for the treatment of T2D, we utilized microarray gene expression data to gain information about gene coexpression networks. Gene expression in human islets from T2D and non-diabetic donors, and gene expression in liver tissue from hyperglycemic and normoglycemic mice, was analyzed to find groups of coexpressed genes (modules) with disturbed expression in diabetes. “Disease signatures” derived from these modules were used to interrogate publically available microarray data sets. These data sets included gene expression profiles induced by a wide range of drugs and treatments. Data sets with an expression pattern similar to our islet disease signature gave clues to the underlying pathogenic process in β-cell failure, and data sets with a reverse expression pattern to our liver disease signature helped identify drug candidates for treatment of excessive hepatic glucose production. <br/><br/>The islet disease signature was associated with β-cell dedifferentiation and loss of a mature β-cell state. We identified the transcription factor <em>SOX5</em> as a regulator of the T2D-associated islet module. Overexpression of <em>SOX5</em> increased the expression of β-cell specific genes in human islets and improved secretory function in islets from donors with T2D.<br/><br/>The liver disease signature was used to rate compounds based on reverse expression compared with the disease signature. The rationale was that compounds with potential to reverse the disease signature might<br/>affect the pathophysiology. Sulforaphane, a sulfur-containing compound found naturally in e.g. broccoli, was identified as the top-rated compound. Sulforaphane reduced glucose production from hepatoma cells via a<br/>mechanism that involves reduced expression of gluconeogenic enzymes. Sulforaphane improved glucose tolerance in animal models of diabetes. Moreover, in a small clinical study, sulforaphane-rich broccoli sprout<br/>extract reduced fasting blood glucose and HbA1c levels in obese T2D patients with poor glycemic control. <br/><br/>Taken together, the data presented in this thesis demonstrate the opportunities of genotype-based treatment for T2D, and show the usefulness of gene network analysis to identify pathophysiological mechanisms and new potential therapies for T2D. By this approach, we have identified Sox5 as a new regulator of β-cell function, and sulforaphane as a liver-targeting therapy for T2D patients with poor glycemic control.},
  author       = {Axelsson, Annika},
  isbn         = {978-91-7619-473-7},
  keyword      = {Type 2 diabetes,insulin,ADRA2A,genotype,gene network analysis,SOX5,drug repositioning,sulforaphane,clinical study,typ 2-diabetes,insulin,ADRA2A,genotyp,gennätverksanalys,SOX5,sulforafan,klinisk studie},
  language     = {eng},
  pages        = {94},
  publisher    = {Lund University, Faculty of Medicine},
  school       = {Lund University},
  title        = {Identification of new disease mechanisms and treatments for type 2 diabetes based on genetic variants and gene expression networks},
  year         = {2017},
}