Genes Involved In Insulin Signaling Biology Essay

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Obesity, which has long been considered to be a risk factor for cardiovascular diseases, and has been recently included as a component of Metabolic Syndrome, was also found to induce carcinogenesis, by Otto Warburg in the early 1950s. There are a handful of epidemiological reports, suggesting the association between metabolic syndromes and cancer; endometrial, colon and breast cancers being the most common ones. Recent studies show that obesity may also increase the risk of other cancer types, like, pancreatic cancer, advanced stage prostate cancer, gall bladder cancer and liver cancer. The mechanism by which obesity induces carcinogenesis is not clear, but several hypothetical pathways have been reported, which includes alteration in hormonal pathways, insulin signaling pathways, adipocyte regulation and metabolism etc. Another hypothesis also states that, as the level of adiposity increases in obese patients, there results a hypoxic condition, which induces the cells to secrete inflammatory cytokines that acts through an alternative pathway and helps in angiogenesis. Adipose tissue hypoxia may also results in development of insulin resistance, which may alter the adipokine secretion, and stimulates tumour cell progression and micro vessel angiogenesis.

Multitudes of epidemiological studies have been conducted in various populations around the globe, to study the association between body size and various types of cancers. The topic is being discussed more recently, as it has been pointed by International Agency for Research on Cancer [IARC] 2002 that, maintaining a normal body weight can reduce the risk of developing malignancies by several folds, for cancers of colon, endometrial, kidney, esophagus, and post menopausal breast cancer.

Single Nucleotide Polymorphisms, being the prime cause of a good number of genetic variations in the genes, and for majority of disease conditions in humans, epidemiologist focus on identifying nsSNPs in genes, that may results in a disease condition. Nearly all of the SNPs which results in disease condition are nsSNPs, in which, the nucleotide substitution at a particular position will results in an amino acid change. By the advent of bioinformatics tools like SIFT (Sort Intolerant From Tolerant), PolyPhen (Polymorphism Phenotype) etc, it is now possible to detect whether the polymorphisms reported for a particular gene, affects the protein structure or function and is thus damaging or not. For the better selection of polymorphisms (nsSNPs) for epidemiological studies, these bioinformatics tools are often used.

Epidemiologic evidences suggest that variants in obesity related genes, which may lead to increased energy storage, may also lead to carcinogenesis in humans. 28 genes were selected for the study, and were categorized to 3 groups based on the different pathways upon which they act. The SNPs reported in these genes were then identified using SNP500cancer database, and SIFT and PolyPhen tools were used to analyze the nsSNPs in these genes.

Materials and Methods:

Database searching:

The SNPs were selected using SNP500cancer database. The database makes available information on candidate SNPs, which is of use in mapping complex diseases, and is freely accessible via http://snp500cancer.nci.nih.gov/home.cfm. Being a part of NCI's Cancer Genome Anatomy Project, the database identifies in particular, genetic variation in genes critical to cancer.

SIFT Tool:

Sort Intolerant From Tolerant (http://blocks.fhcrc.org/sift/SIFT.html) is a tool which can predict the effect of amino acid substitutions in a protein sequence, by obtaining similar sequences of the proteins, of different species from the database, obtains alignment of the selected sequences and calculates the probability for every amino acid substitution at each position in the alignment. SIFT score is given as Tolerance Index, ranging from 0.00 to 1.00, and a Tolerance Index, less than or equal to 0.05 (TI ≥ 0.05) is predicted to be deleterious or INTOLERENT.

PolyPhen Tool:

SIFT predicts whether an amino acid substitution is tolerant or intolerant by comparing the protein sequences alone, while PolyPhen predicts an amino acid substitution to be damaging, when the substitution results in a change that affects the protein structure or function. The tool Polymorphism Phenotyping (http://www.bork.embl-heidelberg.de/PolyPhen/), calculates the Position Specific Independent Counts (PSIC) scores for each of the two amino acid variants, calculates the PSIC score difference, and predicts a variant to be damaging, if the PSIC score difference is greater than 1.500, or in other words, PSIC score is directly proportional to the functional impact, an individual amino acid substitution is expected to have.

Results:

Based on literature review, 25 genes were selected, which plays an important role in obesity and Insulin resistance and also plays a role in tumour progression. By means of a public database SNP500cancer (http://snp500cancer.nci.nih.gov/home.cfm), 47 nsSNPs were identified for 25 genes of interest. SNP selection was limited to SNP500cacner database, as the database includes chosen SNPs in genes, from public databases and reports that have been implicated in one or more cancers. Of the 25 genes selected, 7 genes, INSR, IGF1, GHRL, UCP1, UCP3, LEP and MC3R, lacked any proposed nsSNPs in the database.

Genes involved in Insulin Signaling:

Gene

Protein ID

SNP identifier

AA

change

SIFT Score

SIFT prediction

PolyPhen Score

PolyPhen prediction

INS

NP_001035835

rs17883142

rs12807478

P112T

P28S

0.37

0.04

Tolerated

Damaging

1.35

1.80

Benign

POS

IGF2

NP_001007140

rs14367

rs12993

rs17883406

rs17883142

rs1003483

rs1003484

K120N

K180N

S106I

P112T

F125V

T150A

0.00

0.01

0.14

0.33

0.52

1.00

Damaging

Damaging

Tolerated

Tolerated

Tolerated

Tolerated

1.404

1.628

1.350

1.800

1.125

0.999

Benign

POS

Benign

POS

Benign

Benign

IGF1R

NP_001096082

rs1521484

rs27153

E169Q

V142A

0.06

1.00

Tolerated

Tolerated

1.421

Benign

IRS1

NP_005535

rs180118

rs1801278

rs1801276

M209T

G971R

A512P

0.00

0.29

0.06

Damaging

Tolerated

Tolerated

2.841

1.717

1.195

PRB

POS

Benign

IRS2

NP_003740

rs1805097

rs35927012

rs1056078

G1057D

V999N

R362Q

0.09

0.08

0.00

Tolerated

Tolerated

Damaging

1.317

1.970

1.595

Benign

POS

POS

IGFBP3

NP_001013416

rs9282734

rs4619

rs2854746

P158H

I253M

A32G

0.05

0.04

0.42

Damaging

Damaging

Tolerant

1.067

0.574

0.669

Benign

Benign

Benign

IGFBP6

NP-003569

rs9658625

rs9658616

E14G

R128G

0.36

0.09

Tolerant

Tolerant

1.677

1.892

POS

POS

TNFα

NP_000585

rs4645843

rs3179060

rs2229092

rs3130062

P84L

H52N

L51P

G224R

0.00

0.00

0.21

0.03

Damaging

Damaging

Tolerated

Damaging

1.665

1.49

2.467

2.452

POS

Benign

PRB

PRB

Genes involved in energy expenditure and homeostasis

Gene

Protein ID

SNP identifier

AA change

SIFT Score

SIFT prediction

PolyPhen Score

PolyPhen prediction

ADRB1

NP_000675

rs35720093

rs1801253

A29T

G389A

0.47

0.00

Tolerated

Damaging

1.227

0.285

Benign

Benign

ADRB2

NP_000015

rs1042713

rs1042714

R16G

Q27E

1.00

1.00

Tolerated

Tolerated

1.179

0.322

Benign

Benign

ADRB3

NP_000016

rs4994

rs4995

W64R

T265M

1.00

0.13

Tolerated

Tolerated

2.233

0.01

PRB

Benign

ADIPOQ

NP_004788

rs13061862

rs17366743

G54V

Y111H

0.00

0.09

Damaging

Tolerated

3.069

0.059

PRB

Benign

LPL

NP_000228

rs298

rs300

V370M

T379A

No prediction

No prediction

0.783

1.082

Benign

Benign

POMC

NP_001030333

rs8192606

rs28932472

P132A

R236G

0.125

0.00

Tolerated

Damaging

0.942

2.917

Benign

PRB

Genes involved in regulation of adipocyte metabolism

Gene

Protein ID

SNP identifier

AA change

SIFT Score

SIFT prediction

PolyPhen Score

PolyPhen prediction

LEPR

NP_002294

rs1137100

rs1137101

rs8179183

K109R

Q223R

K656N

0.05

0.07

0.00

Damaging

Tolerated

Damaging

0.063

0.407

2.05

Benign

Benign

PRB

PPARG

NP_619726

rs1805192

rs1800571

P12A

P85Q

No prediction

No prediction

1.664

1.999

POS

POS

MC4R

NP_005903

rs2282556

rs2229616

G98R

V103I

0.00

0.03

Damaging

Damaging

0.743

0.227

POS

Benign

DBH

NP_000778

rs4531

rs5320

A318S

A211T

0.09

0.22

Tolerated

Tolerated

0.157

0.178

Benign

Benign

DRD2

NP_057658

NP_848605

rs1800496

rs1800497

P281S

E713K

0.33

0.36

Tolerated

Tolerated

0.33

0.36

Benign

Benign

IL6

NP_000591

rs13306435

rs11544633

rs2069860

rs2069830

D162E

L119P

D162V

P32S

0.31

0.00

0.14

0.02

Tolerated

Damaging

Tolerated

Damaging

0.776

2.517

1.023

0.225

Benign

PRB

Benign

Benign

IL13

NP_002179

rs20541

rs4986964

Q144R

C27R

1.00

0.04

Tolerated

Damaging

1.242

3.571

Benign

PRB

A total of 54 nsSNPs were identified, of which 20 polymorphisms were reported to be damaging by SIFT tool, having a Tolerance Index ≥ 0.05. No predictions were obtained for 4 nsSNPs due of the lack of homologous sequences in the database for multiple alignments. All the 54 nsSNPs reported by SIFT database were used in PolyPhen approach, to find whether the polymorphisms have any damaging effect on the protein structure or function. Of these 21, (39%) were predicted to be "probably damaging", or "possibly damaging' with a PSIC score difference greater than 1.800, remaining were predicted to be benign.W64R polymorphism in ADRB3 gene, was predicted to be tolerant by SIFT tool but showed a PSIC score difference of 2.223, and was predicted to be "probably damaging" by PolyPhen tool. This difference in the prediction may be because PolyPhen, along with the sequence information, uses structural information also for predicting a variant to be damaging.

Discussion:

A total of 54 nsSNPs were identified, of which 21 SNPs were scored as Damaging by both SIFT and PolyPhen. 9 of the 21 were scored to be "probably damaging" and were distributed among the genes IRS1, TNF α, ADRB3, ADIPOQ, POMC, LEPR, IL6 and IL13; 2 in TNF α, and one each among others. 12 nsSNPs were scored as "possibly damaging", with a PSIC score difference between 1.5-1.8. Studies are being conducted to know the association between these obesity related gene and cancer in different parts of the globe. A study conducted by Michael Nokobia et al., in Nigerian women showed LEPR Gln223Arg polymorphism to be associated with an increased risk of premenopausal breast cancer. LEPR K109R is another widely studied polymorphism but the results are conflicting as it is found to be associated with breast cancer in some populations while no associations were found in others. Polymorphism in PPARγ gene Pro12Ala, which is considered to be a relevant marker of obesity in Tunisian population, is found to have a direct effect on leptin gene transcription, and in some population it is found to be positively associated with the development of colorectal cancer, and negatively associated with breast cancer development.

To conclude, using bioinformatics tools to identify nsSNPs in genes, and using these nsSNPs which were predicted to have negative on the protein structure or function, for the epidemiologic association studies will provide us with more knowledge as to how these genetic variants are associated with disease risk.

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