KR100760297B1 - - Genetic Polymorphisms in the Transforming Growth Factor Beta-Induced Gene Associated with Body Mass Index - Google Patents

Abstract

The present invention relates to genetic polymorphisms in the transforming growth factor beta-inducing gene ( TGFBI ) associated with body mass index (BMI). The present invention provides 28 polymorphic sites in TGFBI , and statistical analysis for investigating the association between the 28 polymorphic sites and type 2 diabetes mellitus (T2DM) and diabetes phenotype, three SNPs ( c. 2011 + 137C> T, c.2589T> G and c.651G> C (p.L217L) provide the fact that they are associated with an obesity-related phenotype. The polymorphism / association information of the present invention is useful for pharmacogenomic studies for further genetic studies and therapeutic development of T2DM as well as other metabolic diseases.

Type 2 Diabetes, TGFBI, SNP, Insulin, BMI

Description

Genetic Polymorphisms in the Transforming Growth Factor Beta-Induced Gene Associated with Body Mass Index}

1A shows a map of polymorphisms identified in the transforming growth factor beta-inducing gene ( TGFBI ).

Figure 1b shows the haplotype of TGFBI in the Korean population.

1C shows LD coefficients (| D '| and r ² ) between SNPs in TGFBI.

2A-2A (27) depict chromatograms of the found polymorphisms.

Chau HM, Ha NT, Cung LX, Thanh TK, Fujiki K, Murakami A, Kanai A. 2003. H626R and R124C mutations of the TGFBI (BIGH3) gene caused lattice corneal dystrophy in Vietnamese people. Br J Ophthalmol 87 (6): 686-9.

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Fujiki K, Hotta Y, Nakayasu K, Yamaguchi T, Kato T, Uesugi Y, Ha NT, Endo S, Ishida N, Lu WN and others. 2000. Sixdifferent mutations of TGFBI (betaig-h3, keratoepithelin) gene found in Japanese corneal dystrophies. Cornea 19 (6): 842-5.

Gibson MA, Kumaratilake JS, Cleary EG. Immunohistochemical and ultrastructural localization of MP78 / 70 (betaig-h3) in extracellular matrix of developing and mature bovine tissues. J Histochem Cytochem 45 (12): 1683-96.

5.Gupta SK, Hodge WG, Damji KF, Guernsey DL, Neumann PE. Lattice corneal dystrophy type 1 in a Canadian kindred is associated with the Arg124-> Cys mutation in the kerato-epithelin gene. sgupta@ogh.on.ca. Am J Ophthalmol 125 (4): 547-9.

6.Ha SW, Bae JS, Yeo HJ, Lee SH, Choi JY, Sohn YK, Kim JG, Kim IS, Kim BW. 2003. TGF-beta-induced protein beta ig-h3 is upregulated by high glucose in vascular smooth muscle cells. J Cell Biochem 88 (4): 774-82.

7. Hedrick PW. 1987. Gametic disequilibrium measures: proceed with caution. Genetics 117 (2): 331-41.

8.Kim HS, Yoon SK, Cho BJ, Kim EK, Joo CK. 2001. BIGH3 gene mutations and rapid detection in Korean patients with corneal dystrophy. Cornea 20 (8): 844-9.

9.Kim JE, Kim SJ, Lee BH, Park RW, Kim KS, Kim IS. 2000. Identification of motifs for cell adhesion within the repeated domains of transforming growth factor-beta-induced gene, betaig-h 3. J Biol Chem 275 (40): 30907-15.

10. Klintworth GK, Valnickova Z, Enghild JJ. 1998. Accumulation of beta ig-h3 gene product in corneas with granular dystrophy. Am J Pathol 152 (3): 743-8.

11.LeBaron RG, Bezverkov KI, Zimber MP, Pavelec R, Skonier J, Purchio AF. 1995. Beta IG-H3, a novel secretory protein inducible by transforming growth factor-beta, is present in normal skin and promotes the adhesion and spreading of dermal fibroblasts in vitro. J Invest Dermatol 104 (5): 844-9.

12.Lee SH, Bae JS, Park SH, Lee BH, Park RW, Choi JY, Park JY, Ha SW, Kim YL, Kwon TH and others. 2003. Expression of TGF-beta-induced matrix protein betaig-h3 is up-regulated in the diabetic rat kidney and human proximal tubular epithelial cells treated with high glucose. Kidney Int 64 (3): 1012-21.

13. Livak KJ. Allelic discrimination using fluorogenic probes and the 5 'nuclease assay. Genet Anal 14 (5-6): 143-9.

14. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. 1985.Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 (7): 412-9.

15. Munier FL, Korvatska E, Djemai A, Le Paslier D, Zografos L, Pescia G, Schorderet DF. 1997. Kerato-epithelin mutations in four 5q31-linked corneal dystrophies. Nat Genet 15 (3): 247-51.

16.Rozzo C, Fossarello M, Galleri G, Sole G, Serru A, Orzalesi N, Serra A, Pirastu M. 1998.A common beta ig-h3 gene mutation (delta f540) in a large cohort of Sardinian Reis Bucklers corneal dystrophy patients. Mutations in brief no. 180. Online. Hum Mutat 12 (3): 215-6.

17. Ruoslahti E. 1996. RGD and other recognition sequences for integrins. Annu Rev Cell Dev Biol 12: 697-715.

18. Schmitt-Bernard CF, Guittard C, Arnaud B, Demaille J, Argiles A, Claustres M, Tuffery-Giraud S. 2000. BIGH3 exon 14 mutations lead to intermediate type I / IIIA of lattice corneal dystrophies. Invest Ophthalmol Vis Sci 1 (6): 1302-8.

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20. Stephens M, Smith NJ, Donnelly P. 2001. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68 (4): 978-89.

21. Warren JF, Abbott RL, Yoon MK, Crawford JB, Spencer WH, Margolis TP. 2003. A new mutation (Leu569 Arg) within exon 13 of the TGFBI (BIGH3) gene causes lattice corneal dystrophy type I. Am J Ophthalmol 136 (5): 872-8.

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Field of technology

The present invention relates to genetic polymorphisms in the transforming growth factor beta-inducing gene ( TGFBI ) associated with body obesity index (BMI).

Prior art

Chromosome with 17 exons (exons) up to 35Kbp transforming growth factor beta on the 5q31 - induced genes (TGFBI; MIM # 601692, also known also BIGH3 search) the person treated with a transforming growth factor beta (TGFB) lung carcinoma (adenocarcinoma ) Was first identified by the screening of cDNA libraries prepared from cell lines [19]. The TGFBI gene encodes an extracellular transforming growth factor-derived protein (TGFBIp), which is composed of an amino terminal secretion sequence (carboxyl-terminal RGD [Arg-Gly-Asp] sequence located at codons 642 to codon 644) and 140 amino acids. It contains four homology domains. The RGD sequence is known to act as an important recognition site for many integrins providing a system for cell attachment [17].

TGFBI has been reported to mediate corneal epithelial cell adhesion [see supra: 9]. Mutation of TGFBI is 5q31- associated person autosomal-dominant corneal dystrophy, such as GCD (granular), RBCD (Reis -Buckler), LCD-1 (lattice type I), ACD (Avellino) corneal dystrophy [supra: 15] and It has been shown to cause Lattice corneal dystrophy type I (LCDI, supra: 1, 3, 5 and 8 above). These diseases are characterized by the progressive accumulation of protein deposits in the cornea, which leads to severe visual damage.

TGFBI is also an adhesion molecule that interacts with integrins. High glucose-induced TGFBI expression in vascular smooth muscle cells and renal proximal tubular epithelial cells has been shown to regulate adhesion and migration of these cells. Thus, TGFBI may play an important role in the development of diabetic angiopathy [6 and 12, supra].

Obesity and insulin resistance are associated with inflammation. It has been found that TGF beta1, which induces expression of TGFBI, is increased in obese individuals and that TGF beta genotype is associated with obesity, insulin resistance and fasting glucose levels. These results suggest a possible role of the TGF beta pathway in obesity and type 2 diabetes. Although TGFBI is induced by TGF beta1, little is known about the role of TGFBI in obesity, insulin resistance and type 2 diabetes. Interestingly, however, TGFBI is induced by high glucose and urinary tract TGFBI is increased in patients with type 2 diabetes compared to non-diabetic controls [6]. Thus, we considered TGFBI as a potential candidate gene for type 2 diabetes.

We investigated the TGFBI gene as a potential candidate gene for type 2 diabetes. In addition, the present inventors completed the present invention by screening the human TGFBI gene by direct sequencing to detect polymorphisms and examine the genetic association with the T2DM and diabetic phenotypes through genotyping and statistical analysis.

The present invention aims to provide genetic polymorphisms in the TGFBI gene. It is also an object of the present invention to provide a genetic association between the polymorphisms and the T2DM and diabetic phenotypes.

To achieve this goal, we considered TGFBI as a potential candidate gene for type 2 diabetes (T2DM), and as part of our efforts to identify genetic polymorphisms in potential candidate genes for type 2 diabetes, The inventors sequenced the transforming growth factor beta-derived gene ( TGFBI ) in the Korean T2DM study and examined the association with T2DM and the diabetic phenotype (775 T2DM patients and 316 normal controls). 28 polymorphisms were identified in TGFBI . Although no significant association with the risk of T2DM was detected, one SNP in intron 16 ( c.2011 + 137C> T ) and one SNP in 3 'untranslated region ( c.2589T> G ) were non- There was a significant association between insulin levels and body obesity index (BMI) among diabetic controls. Lower insulin and BMI were observed in individuals with one or two copies of minor alleles. For example, in individuals with the homozygous major alleles (T) of c.2589T> G , those with the highest BMI (24.21 kg / m ² ) (n = 99) and those with the heterozygous allele The lowest BMI (22.69 kg / m ² ) (n = 57, p = 0.005) was observed in individuals with intermediate BMI (23.68 kg / m ² ) (n = 156), and homozygous minor allele (G). . The present invention first provides information on i) genetic polymorphisms in TGFBI and ii) positive associations between these polymorphisms and insulin levels and BMI in the Korean population.

Transforming growth factor-beta (TGFB) plays a key role in the wound healing process by promoting the generation and deposition of extracellular matrices essential for normal tissue repair and metabolism. The biochemical function of the TGFBI product has not been confirmed in detail yet. TGFBI protein is prominently expressed in the extracellular matrix of cornea, skin and many connective tissues [4, 10, 11 and 19], suggesting that the protein may have important functions throughout the body. Little is known about the role of TGFBI in obesity and T2DM. However, TGFBI is an adhesion molecule induced by TGFB1, which is associated with obesity and insulin resistance. Urethral TGFBI levels also increase in patients with T2DM. This observation suggests a potential role of TGFBI in type 2 diabetes.

In the present invention, we found that TGFBI polymorphism showed gene dose-dependent association with insulin levels and BMI in non-diabetic control subjects. The effect of TGFBI polymorphism on the level of metabolic phenotype was not dramatic in this study. Therefore, it can be argued that Bonferroni correction should be applied to the P-value obtained above. If Bonferroni's correction is strictly adopted, the relevant P-value will not be able to maintain significance (the threshold of significance will be 0.001 [10 polymorphisms and 4 phenotypes analyzed]). However, despite the possibility of Type 1 error due to multiple comparisons, 1) the comparisons were not completely independent of each other due to the tight LDs between the SNPs / Haplotypes and related phenotypes, and 2) three related phenotypes. Considering the fact that positive signals (see Table 3) at the same site (insulin and BMI) ( c.2011 + 137C> T and c.2589T> G ) were detected, the significance of the association can be tolerated. Further biological and / or functional evidence will be needed to confirm the suggestive link between BMI and TGFBI polymorphism in the present invention. Although no association between the risk of T2DM and the TGFBI polymorphism was detected in the present invention, considering that only 63% verifiability for detecting genetic effects was obtained in the present invention (10% allele at a significance level of 0.05) Calculated by frequency, 775 patients / 316 controls, and a crossover ratio of 1.5) is worth investigating in a large cohort study.

The present invention is described based on the following examples. The following examples are intended to illustrate the invention, not to limit the invention. All documents disclosed in the present invention are incorporated by reference.

Example

Example 1 Selection and Measurement of Subjects

775 non-related patients with T2DM and 316 non-diabetic non-related controls were recruited. Non-diabetic controls were recruited from non-selective populations who received routine health checks at Seoul National University Hospital (Seoul, Korea). Acceptance criteria were as follows: age 60 years or older, no history of diabetes diagnosis, no diabetes patients in the first village, fasting plasma glucose levels below 6.1 mmol / l, and HbA1c below 5.8%. Diabetic subjects were randomly recruited from patients who visited outpatient clinics at Seoul National University Hospital (Seoul, Korea). Diabetes was diagnosed based on ADA criteria (1997). Subjects with positive GAD antibodies were excluded. All subjects enrolled in this study were Korean ethnicity. This study protocol was approved by the Institutional Review Board of the Clinical Laboratory of Seoul National University Hospital (Seoul, Korea). A written informed consent was obtained from all subjects prior to blood collection. All study subjects were starved overnight and examined in the morning. Height, weight, waist and hip circumference, and blood pressure were measured. Maximum body weight before diabetes was obtained by history listening. Blood samples were taken for biochemical measurements (fasting plasma glucose, glucose 2-hours postprandial, fasting plasma insulin, HbA1c, total cholesterol, triglycerides and high-density lipoprotein [HDL] cholesterol) and DNA extraction. Insulin Resistance Index HOMA _IR (always model evaluation) was calculated as fasting serum insulin (pmol / l) x fasting plasma glucose (mmol / l) / 135 [supra: 14]. The clinical characteristics of the subjects are shown in Table 1.

Table 1. Clinical Features of Type 2 Diabetics and Normal Controls

데이터는 평균 ±SD이다. HOMA-IR, 인슐린 내성의 항상성 모델 평가.

Data is mean ± SD. HOMA-IR, homeostasis model assessment of insulin resistance.

Example 2: Sequence Analysis of Human TGFBI Gene

We used an ABI PRISM 3700 DNA analyzer (Applied Biosystems, Foster City, Calif.) To detect single nucleotide polymorphisms (SNPs) in 24 Korean DNA samples containing exon-intron boundaries and promoter regions (about 1.5 kb). All exons were sequenced. A set of 22 primers for amplification and sequencing were designed based on the GenBank sequence (reference sequence of TGFBI mRNA: NM_000358.1) (see Table 2). Sequence variation was confirmed by chromatogram (see FIGS. 2A-2A).

Table 2. Primer Sequences for TGFBI Sequence Variation Screening

Example 3: Genotyping Using Fluorescence Polarization Detection

For genotyping of polymorphic sites, amplification primers and probes were designed for TaqMan (see Table 3).

Table 3. Amplification Sequence and Taqman Probe Sequence for TGFBI Polymorphism Genotyping

Example 4: Statistical Analysis

The χ ² test was used to determine whether individual variations were in equilibrium at each locus in the colony (Hardy-Weinberg equilibrium). The inventors have investigated the measure of the linkage disequilibrium used extensively between all pairs of biallelic loci, Lewontin's D '(| D' |), supra, and r ² . It was. Haplotypes and their frequencies were inferred using an algorithm developed by Stephens et al. (20) above (PHASE version: 2.0). The algorithm uses a Bayesian approach that incorporates a priori prediction of the haplotype structure based on population genetics and coalescence theory. Crossover ratios (95% confidence intervals) and corresponding P -values were calculated using logistic regression analysis controlling age, BMI and gender as covariates. Multiple regression methods were used to analyze the association of metabolic phenotypes with adjusting age and gender. The energy distribution was normalized by log-transforming insulin and triglyceride levels. Only non-diabetic subjects were used to analyze the association of metabolic phenotypes as treatment for T2DM could affect these values.

result

In an effort to identify genetic polymorphisms in potential candidate genes for T2DM, we analyzed the sequence of the TGFBI gene, including the 5 'flanking region of -1.5 kb in Korean patients, and analyzed the T2DM and diabetic phenotypes. The association of was investigated. 25 polymorphisms were identified in TGFBI : 3 in the 5 'flanking region, 17 in the introns, 7 in the exon (including one non-homologous SNP) and 1 in the 3' UTR. The location and allele frequency of the identified polymorphic sites are shown in FIG. 1A. Mutations Identified in Previous Studies in Selected Samples with Eye Disease (Deletions of R124C, A555W, L569A, phe540, 9-bp Insertion in Exon 14) I 15, I, 16, 18 and 21 Although not detected in these 24 Korean samples, P501T in exon 11 [22] was also identified in one subject. By pair-wise association analysis using 24 Korean samples that were used for direct sequencing, we found that four sets of SNPs were in absolute LD (D ′ = 1 and r ² = 1) and some of the SNPs It was found that the set is at full LD (D '= 1 and r ² ≠ 1) (see FIG. 1C).

1A shows a map of polymorphisms identified in TGFBI . In FIG. 1A, coding exons are indicated by shaded blocks and 5 ′ and 3 ′ UTRs are indicated by white blocks. * Indicates SNPs genotyped in large populations. The frequency of large non-genotypic SNPs is based on sequencing data (n = 24). The first base of the transcription start site was designated as nucleotide +1 (reference sequence of TGFBI mRNA: NM_000358.1). Figure 1b shows the haplotype of TGFBI in the Korean population. The bold typefaces represent the SNPs tagged for each haplotype. 1C shows LD coefficients (| D '| and r ² ) between SNPs in TGFBI.

Table 4. Genotype and Allele Frequencies of 28 SNPs

P values for deviations from HWE between 24 samples or normal controls (n = 316) that were used for sequencing. Contig positions were based on the GenBank sequence (reference genomic sequence: NT_034772.5, published August 19, 2004). For cDNA numbering, the A of the ATG translation initiation codon in the reference sequence was called +1.

Among the polymorphisms identified, eight SNPs ( c.-1170C> T, c.651G> C (p.L217L), c981A> G (p.V327V), c1416C> T (p.L472L), c.1678 + 23G > A, c.1803G> A (p.L601L), c.2011 + 137C> T and c.2589T> G ) for large-scale genotyping based on location, LDs, frequency and haplotype tagging status Selected. Six haplotypes (frequency> 0.05) were constructed in the Korean population using these eight SNPs (see FIG. 1B). The genotyped polymorphic minor allele frequencies were 0.022 ( c.-1170C> T ), 0.345 ( c.651G> C (p.L217L) ), 0.360 () in the Korean population (n = 1,091), respectively. c.981A> G (p.V327V) ), 0.265 ( c.1416C> T (p.L472L) ), 0.218 ( c.1678 + 23G> A ), 0.094 ( c.1803G> A (p.L601L) ) , 0.145 ( c.2011 + 137C> T ) and 0.440 ( c.2589T> G ). Genotype distributions of all loci were in the Hardy-Weinberg equilibrium (P> 0.05, see Table 4). Of the six universal haplotypes (frequency> 0.05) identified in the Korean population, only ht4 and ht6 were used for further analysis because the other haplotypes were tagged by single SNPs (see FIG. 1B). .

Table 5. Logistic Analysis of TGFBI Polymorphism and Risk of Type 2 Diabetes in Patients and Normal Subjects

Genotype distribution, crossover ratio (95% confidence interval), and P-value for logistic analysis of co-dominant models controlling age and gender as covariates. Haplotypes and their frequencies were inferred using the algorithm developed by Stephens et al. [20]. Deletion genotype data was omitted for accurate haplotype construction.

* Heterozygosity and P-values for HWE calculated in the normal control.

Table 6. Regression Analysis of Log-Converted Insulin, Log-Converted Triglycerides, and BMI and TGFBI Polymorphism Controlling Age and Gender as Covariates in Non-Diabetes Subjects

* C / C, C / R and R / R represent homozygotes for universal alleles, and heterozygotes and homozygotes for rare alleles, respectively. Haplotypes and their frequencies were inferred using the algorithm developed by Stephens et al. [20]. Deletion genotype data was omitted for accurate haplotype construction. Genotype and haplotype distributions, mean and standard deviation (SD) of each value, and P-value are shown for regression analysis.

The association between T2DM and diabetes phenotype risk and TGFBI polymorphism was analyzed using logistic and multiple regression with age and gender adjustment. The clinical characteristics of the subjects are listed in Table 1. Because treatments for T2DM can affect test values, only non-diabetic subjects were used for the association analysis of metabolic phenotypes including log-transformed insulin, log-transformed triglycerides and BMI.

No significant association with the risk of T2DM was detected in patients and normal controls (see Table 4). However, SNPs in intron 16 ( c.2011 + 137C> T ) and SNPs in 3 ′ UTR ( c.2589T> G ) showed a significant association between insulin levels and BMI in non-diabetic controls (see Table 5). Lower insulin and BMI were observed in individuals with one or two more copies of minor alleles. For example, the highest BMI (24.21 kg / m2) was found in individuals with homozygous major alleles (T / T) of c.2589T> G (n = 99) and medium BMI (23.68 kg / m ^2). ) Was found in individuals with heterozygotes (G / T) (n = 156), and the lowest BMI (22.69 kg / m ² ) was found in individuals with homozygous minor alleles (G / G) (n = 57; P = 0.005). In addition, one homologous SNP in exon 6, c.651G> C (p.L217L), was found to be significantly associated with BMI (P = 0.05-0.01, see Table 5).

In summary, we identified 28 polymorphic sites in TGFBI and examined the association between T2DM and diabetic phenotypes in Korean T2DM studies. Statistical analysis of three SNPs (c.2011 + 137C> T, c.2589T> G and c.651G> C (p.L217L)) in TGFBI through the obesity-related phenotype revealed that it is associated with.

As discussed above, the association between genetic polymorphisms in the transforming growth factor beta-inducing gene ( TGFBI ) and the T2DM and diabetic phenotypes was confirmed. Polymorphisms in TGFBI identified in the present invention may play an important role in controlling insulin levels and BMI. The polymorphism / association information identified in the present invention will be useful for pharmacogenomic studies for further genetic studies and therapeutic development of other metabolic diseases.

Although the above has been described with reference to a preferred embodiment of the present invention, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention described in the claims below. It will be appreciated.

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Claims (3)

A transforming growth factor beta-induced ( TGFBI ) gene comprising monobasic polymorphism for predicting the risk of type 2 diabetes mellitus (T2DM) and diabetes phenotype, wherein the monobasic polymorphism of the gene is c.2011 + 137C> T, c.2589T> G or c.651G> C (p.L217L) conversion growth factor beta-derived gene. The transforming growth factor beta-inducing gene of claim 1, wherein the transforming growth factor beta-induced gene polymorphism is associated with the T2DM and diabetes phenotype in a gene dose-dependent manner. 3. The transforming growth factor beta-inducing gene of claim 1 or 2, wherein the diabetic phenotype comprises an obesity-related phenotype including insulin levels and body mass index (BMI).

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