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Int. J. Fundamental Applied Sci. Vol. 2, No. 1 (2013) 23-28
ORIGINAL ARTICLE
Open Access
ISSN 2278-1404
International Journal of Fundamental & Applied Sciences
Bioinformatics assessment of Functional Genes/Proteins Involved in
Obesity-Induced Type 2 Diabetes
Ehab M Abdella1, Rasha R Ahmed1, Mohamed B Ashour2, Osama M Ahmed2,3, Sameh F AbouZid4,
Ayman M Mahmoud2,3*
1Cell Biology and Histology Division, 2 Physiology Division, Zoology Department, Faculty of Science, Beni-Suef University, 3Faculty of Oral & Dental Medicine, Nahda University, Beni-Suef, Egypt, 4Pharmacognocy Department, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt. Abstarct
BACKGROUND & OBJECTIVE: Worldwide, the incidence of type-2 diabetes is rising rapidly, mainly because of
the increase in the incidence of obesity, which is an imporant risk factor for this condition. Both obesity and type-2
diabetes are complex genetic traits but they also share some nongenetic risk factors. Differences among individuals in
their susceptibility to both these conditions probably reflect their genetic constitutions. The dramatic improvements in
genomic and bioinformatic resources are accelerating the pace of gene discovery. It is tempting to speculate the key
susceptible genes/proteins that bridges diabetes mellitus and obesity. METHODOLOGY: In this regard, we
evaluated the role of several genes/proteins that are believed to be involved in the evolution of obesity associated
diabetes through thorough literature search. Also we analyzed the data pertaining to genes of these proteins
extracted from the databases that are available online for free access. RESULTS: The functional cDNA sequences
of these genes/proteins are extracted from National Center for Biotechnology Information (NCBI) and Ensembl
Genome Browser. Our bioinformatic analysis reports 21 genes as ominous link with obesity associated diabetes.
Also this study indicated that, adipose tissue is now known to express and secrete a variety of metabolites, hormones
and cytokines that have been implicated in the development of insulin resistance. CONCLUSION: This
bioinformatic study will be useful for future studies towards therapeutic inventions of obesity associated type-2
diabetes.
Key words: Bioinformatics tools; functional genes; obesity and type 2 diabetes. @2012 BioMedAsia All right reserved
1. Introduction
behaves as a dynamic endocrine organ4. It also plays an important role in energy expenditure, both as depot for energy- Many chronic diseases like type 2 diabetes and its rich triglycerides and as a source for metabolic hormones as complications may be preventable by avoiding factors that well5,6. Adipocytes produce a large number of so-called trigger the disease process (primary prevention) or by use of adipokines, such as leptin, adiponectin, interleukin (IL)-1b, IL- therapies that modulate the disease process before the onset of 6 and tumor necrosis factor-alpha (TNF-a). Some of these clinical symptoms (secondary prevention). Accurate prediction molecules affect energy metabolism and insulin sensitivity in and identification using biomarkers will be useful for disease other tissues such as muscle and liver7. During obesity, lipid prevention and initiation of proactive therapies to those storage in adipocytes is increased, which triggers the release of individuals who are most likely to develop the disease. Recent adipokines8,9. During inflammation, the mature adipocytes of technological advances in genetics, genomics, proteomics and the adipose tissue are responsible for increasing production of bioinformatics offer great opportunities for biomarker pro-inflammatory adipokines10, including mentioned TNF-a, IL -1b, IL-6. That disregulation contributes to obesity and chronic Obesity and its pathological complications, including inflammation11. The local increase of these adipokines have atherosclerosis, hypertension and insulin resistance, have been directly related to insulin resistance, increasing lypolisis increased to reach epidemic dimensions nowadays2. Some important factors for the development of these disorders are The growing incidence of type 2 diabetes with increasing excessive accumulation of abdominal fat, which is known to obesity reflects that obesity is an emerging risk factor for the play an important role in development of chronic inflammation; progression of insulin resistance and subsequently to overt type deposition of lipids into non-adipose tissues such as liver and -2 diabetes. Both in normoglycemic and hyperglycemic states, muscles; atherosclerosis and chronic inflammation that increase obese people exhibit a higher degree of hyperinsulinemia that risk in cardiovascular disorders and diabetes3. correlates with the degree of insulin resistance, in order to Adipose tissue is not just a site of energy storage but also maintain normal glucose tolerance12. Following attainment of certain point, the progressive deterioration of the metabolic milieu leads to eventual failure of hyperinsulinemia to Full Address :
compensate fully for the insulin resistance and thereby Dr. Ayman M Mahmoud
produces impaired glucose tolerance that progress to overt diabetes13. It has been presumed from genetic studies that there Faculty of Science, Beni-Suef University, Faculty of Oral & Dental Medicine, Nahda University, could be subset of genes whose expression changes with obesity and those genes whose expression further changes in the progression to type-2 diabetes14,15,16. However, the molecular basis e-mail: ayman.mahmoud@science.bsu.edu.eg that links obesity and diabetes is still largely unknown. Bioinformatics assessment od diabetic Functional Genes/protein
Int. J. Fundamental Applied Sci. Vol. 2, No. 1 (2013) 23-28
Bioinformatics has been in the focus since recent years for unravel- protein that connects both the metabolic disorders such as
ing the structure and function of complex biological mechanisms. obesity and diabetes.
The analysis of primary gene products has further been considered
as diagnostic and screening tool for disease recognition. Such strat- 2. Materials and methods
egies aim at investigating all gene products simultaneously in order The present research aims at finding the genes/proteins re- to get a better overview about disease mechanisms and to find sponsible for obesity associated diabetes in two phases. The suitable therapeutic targets. Recently Gerken et al.17 performed first phase of the research attempts to identify the candidate bioinformatics analysis and reported that the variants in the fat genes/proteins which are involved in these disorders through mass and obesity associated gene are associated with increased thorough literature search. The second phase of the research body mass index in humans. Although Elbers et al.14 identified five analyzes the data pertaining to genes of these proteins ob- overlapping chromosomal regions for obesity and diabetes. These tained from the databases that are available online for free results illustrate the importance of proteomics and bioinformatics access. The functional cDNA sequences of these genes/ approaches for identify new therapeutic invention of obesity is a proteins are extracted from: (1) National Center for Biotech- nology Information (NCBI), (http\\www. ncbi.nih.nlm.gov), This study will therefore focus on potential implications of bioin- (2) Rat Genome Database (RGD) (<http://rgd.mcw.edu/ formatics as a tool to identify novel metabolic patterns or markers rgdweb /search/search.html>), (3) Online Mendelian Inher- associated with disease status. We will exemplify the potential of itance in Man (OMIM), which can be accessed with the En- this method using the association between specific fats and devel- trez database searcher of the National Library of Medicine, opment of obesity associated diabetes as a test case. In the present Ensembl Genome Genome Informatics (MGI) website is study we have employed online bioinformatics tools for the analy- hosted by The Jackson Laboratory, (5) HomoloGene, a tool of sis of 21 genes, which are expected to play major role in obesity the NCBI. and diabetes, we sought to identify the common central gene/ Table I: Showing comparative gene map data of the genes/proteins that have been studied in the present study, which
are believed to be involved in type-2 diabetics and obesity
Rattus norvegicus Mus musculus Homo sapiens
Map name position
number position Map name
position
number position
Map position
number position
Adiponectin
79965888
Mouse genome
23146609
16 B3–B4 human
188043164
assembly 3.1
assembly 36.1
assembly
Resistin
Rat Celera 3566836
Mouse Celera
Human Celera 7605160
Assembly
Assembly
Assembly
Rat Celera 52779315 4
Mouse Celera
29063769
Human Celera 122684619
Assembly
Assembly
Assembly
64647455 10
Mouse Genome 78336352
human genome 23686912
17 q22-q23
assembly 3.1
Assembly 36.1
assembly
Mouse genome 30339701
Human Celera 22752396
assembly 3.1
assembly 36.1
Assembly
Rat Celera 2.33E+08 1
Mouse Celera
38908311
human genome 95341583
10 q23-q24
Assembly
Assembly
assembly
11325546 7
Mouse Celera
80905663
Human Celera 784591
assembly 3.1
Assembly
Assembly
22532512 16
Mouse Celera
71423790
human genome 19841057
assembly 3.1
Assembly
assembly
Mouse genome 113666113 6
human genome 10302433
3 p26-p25
assembly 3.1
assembly 36.1
assembly
Chemerin Rat Celera 72460060 4
Mouse Celera
49080699
Human Celera 144592497
Assembly
Assembly
Assembly
Visfatin
51132285 6
Mouse genome 33505340
human genome 105495899
assembly 3.1
assembly 36.1
assembly
87445119 13
Mouse genome 173448254 1
human genome 157659404
Assembly 3.4
assembly 36.1
assembly
Rat Celera 21377028 12
q11-q12 Mouse Celera
134078340 5
Human Celera 95778744
7 q21.3-q22
Assembly
Assembly
Assembly
219554563 2
Mouse Genome 122598310 3
human genome 120596008
4 q28-q31
assembly 3.1
Assembly 36.1
assembly
Rat Celera 137316681 4
Mouse Celera
117199637 6
Human Celera 12266744
Assembly
Assembly
Assembly
35391672 19
Mouse Genome 108090598 8
human genome 66073974
assembly 3.1
Assembly 36.1
assembly
nSREBF-1 Rat Celera 44264875 10
Mouse Genome 60012591
human genome 17656110
Assembly
Assembly 36.1
assembly
Rat Celera 130806706 2
Mouse Celera
52001471
Human Celera 22187272
Assembly
Assembly
Assembly
11β-HSD1 genome
109252609 13
Mouse Genome 195047834 1
human genome 206266585
1 q32-q41
assembly 3.1
Assembly 36.1
assembly
134460719 X
Mouse Genome 45378323
Human Celera 129165798
assembly 3.1
Assembly 36.1
Assembly
Rat Celera 120432630 6
Mouse Genome 105266979 12
human genome 94023372
14 q32.13
(Serpina12) Assembly
Assembly 36.1
assembly
Abdella ME et al
Int. J. Fundamental Applied Sci. Vol. 2, No. 1 (2013) 23-28
Table II: Showing gene ontology data of the genes/proteins that have been studied in the present study, which
are believed to be involved in type-2 diabetics and obesity
Identifiers
Gene ontology
Molecular
Biological activities
Secreted tissue(s)
Array IDs
function
Positive regulation of I-kappaB kinase/NF-kappaB
cascade.
Adiponectin
Adipose tissue
rc_AI176736_at
Hormone activity
Negative regulation of gluconeogenesis.
Positive regulation of fatty acid metabolic process.
Positive regulation of glucose import.
Brain, cerebral
Increase transcriptional events leading to an increased
Resistin
rc_AA819348_at
Hormone activity
cortex, lung
expression of several pro-inflammatory cytokines.
Serve as a link between obesity and T2DM.
Growth factor
Regulation of insulin secretion.
Adipocytes
D49653_s_at
activity
Regulation of intestinal cholesterol absorption.
Negative regulation of appetite.
Induction of apoptosis via death domain receptors.
Numerous cells, but
Regulation of cell proliferation.
mainly macrophages
rc_AA943494_at
Cytokine activity
Positive regulation of I-kappaB kinase/NF-kappaB
and lymphocytes
cascade.
Negative regulation of glucose import.
Cell-cell signaling
Fibroblasts, lympho-
M26745cds_s_at
Cytokine activity
Positive regulation of cell proliferation
cytes, adipose tissue
Negative regulation of apoptosis
Transporter
Adipocyte tissue
K03045cds_r_at
Transport, visual perception and response to stimulus
activity
The encoded protein is a component of the alternative
Stimulates
complement pathway best known for its role in hu-
glucose transport
moral suppression of infectious agents and the encod-
White fat adipocytes
GE1112269
in fat cells and
ed protein has a high level of expression in fat, sug-
inhibit lipolysis
gesting a role for adipose tissue in immune system
biology.
Lipid transporter
Regulate fatty acid metabolic process
Adipose tissue
L03294_g_at
activity
Regulate lipid catabolic process
Produced by P/D1
Positive regulation of appetite.
cells lining the
A_44_P420046
Hormone activity
Positive regulation of body size.
fundus of stomach
Hepatocytes, white
Cell differentia-
Chemerin
rc_AI176061_at
Retinoid metabolic process
adipose tissue
tion activities
Visceral adipose
Cell-cell signaling,
Visfatin
rc_AI177755_at
Cytokine activity
Positive regulation of cell proliferation
Visceral adipose
Sugar binding
Signal transduction
activity
Endopeptidase
Glucose homeostasis
Endothelial cells,
inhibitor activity
GE1137951
Many other biological processes; associated with adiocytes
and plasminogen
diabetes Mellitus
activator activity
Lipid transporter
Increase partitioning of glucose to triacylglycerols and
Adipose tissue
A_43_P11691
activity and fatty
enhance insulin resistance
acid binding
Vascular smooth
Regulate lipid metabolic process
muscle cells, endo-
Transcription
Epithelial cell differentiation
A_42_P462474
thelial cells, adipo-
factor activity
Regulation of fat cell differentiation
Positive regulation of transcription
Receptor binding,
neuro-peptide
Adult feeding behavior, neuro-peptide signaling
Macrophages
A_44_P257522
and hormone
pathway and hormone-mediated signaling
activity
Regulation of lipid metabolic process, steroid metabol-
Transcription
nSREBP-1
Adipose tissue
rc_AI013042_at
ic process, cholesterol metabolic process and regula-
regulator activity
tion of transcription
rc_AA893671_at
Transcription
Regulation of transcription
Adipose tissue
factor activity
Regulation of cell proliferation
Dehydrogenase
Visceral adipose
activity and
11β-HSD-1
Lipid metabolic process
oxidoreductase
activity
Plays a role in regulation of blood pressure
Adipocytes
A_43_P12613
Hormone activity
Stimulate gastric cell proliferation
Regulates glucose tolerance and insulin sensitization
Visceral adipose
A_44_P288224
Hormone activity
NCBI is a system for automated detection of homologs 3. Results
(similarity attributable to descent from a common ancestor) 3.1 First phase (literature search)
among the annotated genes of several completely sequenced From literature search several adipocyte-secreted factors has eukaryotic genomes and (6) GeneCards is a database of human been demonstrated to potentially link obesity, insulin resistance genes that provides genomic, proteomic, transcriptomic, and type 2 diabetes mellitus. These adipocytokines comprise genetic and functional information on all known and predicted mediators (Supplementary Table, S I) such as adiponectin, human genes. GeneCards is being developed and maintained by resistin, leptin (obesity factor), tumor necrosis factor-alpha the Crown Human Genome Center at the Weizmann Institute of (TNF-a), interleukin-6 (IL-6), retinol binding protein-4 (RBP- 4), adipsin, lipoprotein lipase (LPL), ghrelin, chemerin, Abdella ME et al
Int. J. Fundamental Applied Sci. Vol. 2, No. 1 (2013) 23-28
plasminogen activator inhibitor-1 (PAI-1), fatty acid binding expression. Recently, You et al.81 investigated that, the quanti- protein-2 (FABP2), peroxisome proliferators-activated receptor- ty of visceral fat was negatively related to leptin and adiponec- g (PPARγ), Aguti (AgRP), nuclear sterol regulatory element- tin abdominal adipose tissue gene expression. In addition, binding proteins-1c (nSREBP-1), winged-helix-forkhead box hyperinsulinemia, as indicated by fasting insulin and 2 h insu-class O-1 (FOXO-1), 11b-hydroxysteroid dehydrogenase type- lin during the Oral Glucose Tolerance Test, was positively 1 (11b-HSD-1), apelin and vaspin. These adipose derived factors associated with adipose TNF-α and IL-6 gene expression. are presently subjected to intensive research concerning their in- Also, Elbers et al.14 yielded an interesting list of candidate volvement in the regulation of adipose tissue physiology and in genes by investigating the overlapping chromosomal linkage particular, their potential implication in insulin resistance, obesity regions for type 2 diabetes and obesity, using a combination of and diabetes. In addition, most of these mediators may directly or six computational disease gene identification methods. Many indirectly interact with insulin receptors and/or insulin signaling, of these identified genes are excellent candidates to study leading to insulin resistance in liver and peripheral tissues, espe- further for their role in the shared disease etiology between cially in visceral obesity. The roles and mechanisms of some of obesity and type 2 diabetes and a few have already been genet-the most important adipokines were suggested by some publica- ically or functionally associated with both disorders. Current evidence supports that metabolic risk factors, includ- 3.2 Second phase (databases analysis)
ing dyslipidemia, glucose intolerance and hyperinsulinemia, The second phase of the research analyzes the gene orthologs and are linked with circulating levels of inflammatory and throm-
the gene ontology (Tables I and II respectively) of the 21 detected botic cytokines82,83. Relationships between cytokine gene ex-
genes. The data pertaining to these genes/proteins obtained from the pression in adipose tissue and metabolic risk and insulin re-
databases that are available online for free access.
sistance have been reported as well84,85. Abdominal adipose gene expression levels of TNF-α86, IL-687 and PAI-186 are 4. Discussion
positively linked with insulin resistance and other cardiovas-cular risk factors, whereas adiponectin gene expression is The emerging epidemic of diabetes in Egypt and around the world negatively associated with metabolic variables85. Our results cannot be ignored. According to the World Health Organization, over were consistent with these previous findings and demonstrated 180 billion people now have diabetes worldwide and this number is that hyperinsulinemia was positively linked to adipose TNF-α expected to double by the year 2030. Similarly alarming is the high and IL-6 gene expression and hyperinsulinemia and glucose prevalence of two factors closely linked with increased risk for diabe- intolerance were negatively linked to adipose adiponectin tes: Metabolic Syndrome (MetS) and obesity68. Several recent studies expression. Although these adipose-derived cytokines are investigated that, a number of common factors including genetic pre- traditionally viewed as the causes of the insulin resistance and disposition, poor dietary patterns, increased physical inactivity and metabolic risk87, recent evidence suggests that an elevated longer life expectancy contribute to the rising prevalence of these TNF-α and IL-6 expression88 and a decreased adiponectin disorders; subclinical inflammation may represent an additional novel expression88 may also be a consequence of hyperinsulinemia. risk factor. In this regard, epidemiologic data suggest that inflammato- However, insulin infusion did not affect adiponectin gene ry biomarkers may serve as important risk indicators for the future expression in either healthy or type 2 diabetic individuals89. development of diabetes16,69,70,71,72,73. Therefore, this study provides information from previous liter- Also, there is growing evidence that the insulin-resistance syn- atures and genome databases of different websites and act as a drome associated to obesity is mainly caused by excessive accu- material for future studies to clarify the underlying mecha- mulation of fat in intra-abdominal adipocytes22,74. It has been ob- nisms of these associations and finding of new therapies of served that the surgical removal of visceral fat improves insulin effect obesity associated type2 diabetes mellitus. on hepatic glucose production in animal models of obesity75. Adipose cells from visceral or subcutaneous depots largely differ concerning their metabolic characteristics as the control of lipolysis and the sensi- Conclusion
tivity to insulin76. Therefore, it would be interesting to define the In conclusion, any rigid assessment of disease patterns will regional adipose differences in the expression of the recently dis- need support from well documented and curated databases. covered proteins, which are candidate links between fat accumula- However, there are also several practical and theoretical con- straints known if applying bioinformatics as a tool for im- Complex traits such as obesity and type-2 diabetes pose special proved understanding and diagnostics of disease patterns. So challenges for genetic analyses because of gene–gene and gene- that, the current study provides evidence that the quantity of environment interactions, genetic heterogeneity and low pene- visceral fat and glucose/insulin complications of obesity is trance of the individual genes. The heterogeneity means that it is related to abdominal subcutaneous adipose tissue cytokine difficult to generalize genome scan results over different popula- gene expression. Moreover, additional research is needed to tions and ethnicities. In addition, the exponential and alarming discern whether abdominal subcutaneous adipocyte gene ex- growth of the obesity epidemic has led scientists to begin to take advantage of proteomics to identify obesity molecular targets and pression is causative for these risk factors or whether there is to study the mechanisms of action of potential obesity therapies. compensatory regulation of adipose tissue gene expression as Proteomics analyses have been proven useful in the characteriza- a result of elevated visceral fat and/or insulin resistance. tion of the adipocyte proteome77, in the identification of obesity Acknowledgment:
targets in different models of experimental obesity and to charac-
The article is a part of a project that was funded by STDF. terize targets of several agents such as the insulin sensitizer rosig- Thus, the authors acknowledge the Science and Technology litazone78. Although they are highly informative, these strategies Development Fund (STDF) Agency, Minister of Scientific often generate large amounts of data and long lists of proteins that Research, Egypt, for funding and following up the project. are difficult to analyze and understand their biological importance. The approach in this article is similar to the one in Rao et al.79 and Park et al.80, but it is more robust to the data here, which are more heterogeneous and encompassing the bioinformatic gene analysis of human, mouse and rat models in addition to other variables. The present bioinformatic analysis showed significant relationships between metabolic and obesity type 2 diabetes dis-ease risk factors and abdominal subcutaneous adipose tissue gene Bioinformatics assessment od diabetic Functional Genes/protein
Int. J. Fundamental Applied Sci. Vol. 2, No. 1 (2013) 23-28
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