http://bma.org.in/ijfas.aspx 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
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
(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
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-
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 inRao 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
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ADVERTÊNCIA Este texto não substitui o publicado no Diário Oficial da União Ministério da Saúde Secretaria de Atenção à Saúde PORTARIA Nº 491, DE 23 DE SETEMBRO DE 2010 O Secretário de Atenção à Saúde, no uso de suas atribuições, Considerando a necessidade de se estabelecer parâmetros sobre a doença de Alzheimer no Brasil e de diretrizes nacionais para diagn
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