Open Access

Genetic variants of numb gene were associated with elevated total cholesterol level and low density lipoprotein cholesterol level in Chinese subjects, in Xinjiang, China

  • Mayila Abudoukelimu1, 2, 4, 5,
  • Zhen-Yan Fu1, 2, 4, 5,
  • Yang Xiang1, 2, 4, 5,
  • Yi-Tong Ma1, 2, 4, 5Email author,
  • Qing Zhu1, 2, 4, 5,
  • Minawaer Abudu3, 4,
  • Dilare Adi1, 2, 4, 5,
  • Yi-Ning Yang1, 2, 4, 5,
  • Xiao-Mei Li1, 2, 4, 5,
  • Xiang Xie1, 2, 4, 5,
  • Fen Liu2, 5 and
  • Bang-Dang Chen2, 5
Contributed equally
Diagnostic Pathology201510:141

https://doi.org/10.1186/s13000-015-0373-2

Received: 9 June 2015

Accepted: 28 July 2015

Published: 12 August 2015

Abstract

Background

Hypercholesterolemia is one of the most common risk factors for Coronary Artery Disease (CAD), which is the leading cause of death worldwide. As Numb is an important regulating factor regarding intestinal cholesterol absorption and plasma cholesterol level, the aim of the present study is to investigate the relationship between human Numb gene polymorphism and cholesterol level in Chinese subjects.

Methods

All participants came from the First Affiliated Hospital of Xinjiang Medical University (Male: 1052 and Female: 596), and four tagging SNPs (rs2108552, rs12435797, rs1019075 and rs17781919) of Numb gene were genotyped by using TaqMan® assays and analyzed in an ABI 7900HT Fast Real-Time PCR System. Further, general liner model was applied for assessing the relationship between cholesterol level and genotypes.

Results

By analyzing a dominant model, recessive model and an additive model, we have found that SNP rs2108552 was associated with total cholesterol (TC) and low density lipoprotein-cholesterol level (LDL-C) (P = 0.000 and P = 0.007; P =0.042 and P =0.009; P = 0.006 and P = 0.030). C allele of SNP rs17781919 had significantly lower plasma TC level (3.46 ± 0.74 mmol/L vs 4.27 ± 1.1 mmol/L) and LDL-C level (0.98 ± 0.55 mmol/L vs 2.64 ± 0.93 mmol/L) when compared with T allele. Additionally, SNP rs12435797 was associated with TC level and SNP rs1019075 was associated with LDL-C level by analyses of a dominant model, recessive model and an additive model (P = 0.000, P = 0.005 and P = 0.004; P = 0.016, P = 0.008 and P = 0.033). Further, the association of rs2108552, rs12435797, rs1019075 and rs17781919 with aforementioned different kinds of cholesterol levels remained statistically significant after multivariate adjustment of ethnicity, gender, age, smoking and obesity.

Conclusions

Our results indicated that both rs2108552 and rs17781919 in the Numb gene were associated with total cholesterol level and density lipoprotein-cholesterol level in Chinese subjects.

Background

Coronary artery disease (CAD) is the leading cause of morbidity and mortality in developed countries, and becoming increasingly prevalent in developing countries [13]. Regarding Coronary artery disease, strong evidence indicates that the risk increasement of CAD [47] is closely related to the rise of plasma cholesterol level.

Studies have demonstrated that a reduction in plasma total cholesterol (TC) and triglycerides (TG) concentration independently reduces the risk of CAD [8, 9]. In addition, convincing data also shows that 1 % decrease in low density lipoprotein-cholesterol (LDL-C) concentration reduces the risk of CAD for approximately 1 % [10], and 1 % increase in high density lipoprotein-cholesterol (HDL-C) concentration reduces the risk of CAD for approximately 2 % [11]. Further, other than being determined by environmental factors, the levels of plasma cholesterol are also influenced by the genetic constitution of each individual, such as single nucleotide polymorphisms (SNPs) [1215].

Numb is a member of clathrin-associated sorting proteins, and combines with several other endocytic proteins [1619]. Pei-Shan Li et al. [20] has revealed that Numb is an essential regulating factor for maintaining human cholesterol homeostasis. Numb regulates cholesterol absorption, and thus plays a crucial role in the development of atherosclerosis and indicated that pharmacologically targeting the Numb-NPC1L1 interaction could be a way to decrease cholesterol absorption and cholesterol level. Further, Jian Wei et al. [21] has confirmed that there does exist a remarkable correlation between Numb polymorphism G595D (rs17781919),-a nonsynonymous variant in the coding region of Numb gene- and low concentration of LDL-C among humans. They demonstrated that rs17781919 influences Numb activity of driving NPC1L1 internalization and decreases cholesterol absorption and leading to low plasma cholesterol level.

Unfortunately, the relationship between human Numb gene and lipid profile has not been thoroughly studied yet. Therefore, the aim of the present study is to reveal the relationship between some genetic variants of human Numb gene and lipid profile among Chinese population in Xinjiang, China, hoping to provide additional information to characterize the genetic factors that influence plasma cholesterol levels.

Methods

Subjects

Altogether, 1742 (Male: n = 1117; Female: n = 625) were randomly selected from the First Affiliated Hospital of Xinjiang Medical University that belong to a time period ranges from January 2007 to December 2013 for DNA analysis. The inclusion and exclusion criteria was such as: The analysis presented in this study was based on 1648 subjects (Male: n = 1052; Female: n = 596) who had passed the eligibility criteria and had complete data on Numb genotype. Further, all of these subjects live in Xinjiang Uighur Autonomous Region of China, and none of them suffers from impaired malignancy, connective tissue disease, renal function, valvular disease or chronic inflammatory disease. Moreover, subjects also are free from thyroid disease, or any history of taking lipid-lowering drugs. In addition we excluded 92 hypercholesterolemia (fasting plasma TC ≥ 6.26 mmol/L or fasting plasma LDL-C ≥ 4.14 mmol/L [22]) patients during the analysis.

Lifestyle measurements and blood biochemical analysis

Height and body weight were measured as described previously [23], and body mass index (BMI) was calculated by dividing the weight in kilogram to the square of height in meter. The status of smoking was described as smokers (including current and ex-smokers) or non-smokers. Further, WHO Asia-Pacific Area criterion- BMI ≥25 kg/m2 was used to define obesity as described in detail previously [23]. Finally, blood urea nitrogen (BUN), creatinine (Cr), uric acid, glucose, total cholesterol (TC), triglyceride (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein-cholesterol (HDL-C) were measured by using chemical analysis equipment (Dimension AR/AVL Clinical Chemistry System, Newark, NJ) in Clinical Laboratory Department of the First Affiliated Hospital of Xinjiang Medical University [24, 25].

Ethical approval of the study protocol

All participants have given their written informed consent and explicit permission for DNA analysis as well as for the collection of relevant clinical data. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Urumqi, China) and was conducted by strictly following the requirements of the Declaration of Helsinki.

SNP selection

The human Numb gene includes phosphotyrosine-binding (PTB) domain and proline-rich region (PRR) domain [2629] and has four transcript variants that encode four different isoforms, which are p72 (PTBLPRRL), p71 (PTBSPRRL), p65 (PTBSPRRS), and p66 (PTBLPRRS). Isoform p72 of the Numb gene, which has the longest length among the four isoforms, consists of 651 amino acids. It is located on chromosome 14q24.3 and contains 13 exons which are further separated by 12 introns. There are 3781 different kinds of SNPs of human Numb gene as listed in the National Center for Biotechnology Information SNP database (http://www.ncbi.nlm.nih.gov/SNP). For the current study, we have screened the HapMap phase I & II database and Haploview 4.0 software for the tag SNPs of Numb gene and selected three SNPs (rs2108552, rs12435797, and rs1019075). Meanwhile, we also included rs17781919 from the Numb gene which was associated with LDL-C [21]. SNPs with relatively high minor allele frequencies (MAFs) have been shown to be useful as genetic markers in genetic association studies [30] and all four tag SNPs that selected for the present study had a MAF of ≥0.1. In addition, linkage disequilibrium (LD) is the non-random association of alleles at different loci, and we described LD patterns with r2 ≥ 0.8 as a cut-off for the tag SNPs [31].

Genotyping

Blood samples were taken from all participants by using anticoagulant ethylene diaminetetraacetic acid (EDTA) tube, and standard phenol-chloroform method was used to extract genomic DNA from peripheral leukocytes [32]. Further, genotyping was undertaken by using TaqMan® assays from Applied Biosystems following the manufacturer’s suggestions and analyzed in an ABI 7900HT Fast Real-Time PCR System [33], and the detailed information of the TaqMan SNP Genotyping method was as follows. Allele-specific fluorogenic probes were hybridised to the template in the first step of the 5′ nuclease assay. Then, during the polymerase chain reaction (PCR), the 5′ nuclease activity of the Taq polymerase made it possible for discrimination. In addition, the probes include a 3′ minor groove binding group that hybridises to single-stranded targets and has greater sequence specificity when compared to the original DNA probes. This reduces nonspecific probe hybridization which leads to low background fluorescence for the 5′ nuclease PCR assay (TaqMan; Applied Biosystems). Cleavage results in the increased emission of a reporter dye. Two unlabeled PCR primers and two allele-specific probes were required for each 5′ nuclease assay. At the 5′ end, each probe is labeled with two reporter dyes. In the present study, VIC and FAM were used as the reporter dyes. The probes and primers used in TaqMan®SNP Genotyping Assays (ABI) were selected according to the information on ABI website (http://www.appliedbiosystems.com/absite/us/en/home.html). Finally, PCR amplification was performed by using 0.05 μL probes, 3 μL of TaqMan Universal Master Mix, 1 μL DNA, and 1.95 ddH2O in a 6 μL final reaction volume. In addition, thermal cycling conditions for PCR amplification were 95 °C for 10 min, 45 cycles of 95 °C for 10 s and 60 °C for 1 min. Moreover, thermal cycling was undertaken by using Applied Biosystems7900HT Standard Real-Time PCR System, and all 96 well plates were read according to Sequence Detection Systems (SDS) automation controller software v2.3 (ABI).

Quality control

There was at least one negative and one positive control per 96-well DNA plate in our assays. We have found 100 % concordance between the genotyped duplicate samples (10 %) for each of the SNPs by testing the accuracy of the genotyping. The rate of genotyping success for each SNP was 98 %.

Statistical analysis

All statistical analyses were performed via using SPSS16.0 software for Windows (SPSS Institute, Chicago, IL, USA). Further, Hardy-Weinberg equilibrium was assessed by χ2 analysis for all subjects, and all of the continuous variables were expressed via mean ± standard deviation. Furthermore, differences among the frequency of obesity, smoking and Numb genotypes were analyzed through χ2 test or Fisher’s exact test. Finally, after adjusting confounding variables, general linear model analysis was undertaken to test the association between Numb genotypes and lipid profile. In addition, P < 0.05was considered as a criterion for statistical significance.

Results

Characteristics of the subjects

The study cohort includes 1648 subjects (1052 male, 596 female), and all of them were selected from Out-patient and In-patient department of the First Affiliated Hospital of Xinjiang Medical University that belong to a time period ranges from January 2007 to December 2013.

Results of outcome measures

  1. 1.
    The clinical and metabolic characteristics of the study population are shown separately for male and female in Table 1. In females, plasma concentration of BUN is higher than to males (P = 0.036). Further, in males, plasma concentration of creatinine, prevalence of smoking is higher than females (all P = 0.000). In addition, there were no significant differences between males and females on plasma concentration of uric acid, glucose, TC,TG, LDL-C and HDL-C or prevalence of obesity (All P > 0.05).
    Table 1

    Clinical and metabolic characteristics of subjects

    Risk factor

    No. (%) or mean (SD)

     
     

    Total

    Male

    Female

    P value

    Age (years)

    56.33 (9.83)

    55.28 (10.09)

    58.22 (9.03)

    0.000*

    BUN (mmol/L)

    5.07 (1.82)

    5.01 (1.85)

    5.20 (1.78)

    0.036*

    Cr (mmol/L)

    71.04 (17.18)

    72.26 (17.29)

    68.98 (16.79)

    0.000*

    Uric acid (mmol/L)

    313.70 (89.96)

    316.76 (87.43)

    308.51 (93.79)

    0.078

    Glucose (mmol/L)

    5.68 (2.43)

    5.74 (2.41)

    5.58 (2.39)

    0.189

    Total cholesterol (mmol/L)

    4.27 (1.11)

    4.33 (1.18)

    4.23 (1.07)

    0.083

    Triglyceride (mmol/L)

    1.97 (1.49)

    2.01 (1.81)

    1.94 (1.28)

    0.401

    LDL-C (mmol/L)

    2.65 (0.91)

    2.69 (0.99)

    2.62 (0.86)

    0.188

    HDL-C (mmol/L)

    1.05 (0.55)

    1.04 (0.55)

    1.06 (0.54)

    0.378

    Obesity (%)

    1026 (63.1)

    641 (61.6)

    385 (66.0)

    0.077

    Smoking (%)

    1031 (62.4)

    737 (69.9)

    293 (49.2)

    0.000*

    Continuous variables are expressed as mean ± SD. Categorical variables are expressed as percentages

    The P value of the continuous variables was calculated by the independent samples t test. The P value of the categorical variables was calculated by x 2 test

    BUN blood urea nitrogen, Cr Creatinine, LDL-C low density lipoprotein-cholesterol, HDL-C high density lipoprotein-cholesterol

    * P < 0.05

     
  2. 2.
    The distribution of genotypes of SNPs (all genotyped frequencies of SNPs were in Hardy-Weinberg equilibrium, data not shown) of Numb gene is shown in Table 2. We observed that there were no significant differences between males and females for 4 SNPs (All P > 0.05; genotypes, dominant, recessive and additive models). In addition, for the alleles, we found that there was significant difference between males and females for SNP rs12435797 (P = 0.031). Further, we found that there was no significant difference between males and females for rs2108552, rs1019075, and rs17781919 (All P > 0.05).
    Table 2

    Distribution of SNPs of Numb gene for study population

    Varients

     

    Total N (%)

    Male N (%)

    Female N (%)

    P value

    rs2108552 (SNP1)

    Genotype

    C/C

    281 (17.0)

    174 (16.5)

    107 (18.0)

    0.732

     

    G/G

    568 (34.4)

    367 (34.8)

    201 (33.7)

     
     

    C/G

    799 (48.6)

    511 (48.7)

    288 (48.3)

     

    Dominant model

    CC

    281 (17.0)

    174 (16.5)

    107 (18.0)

    0.464

     

    CG + GG

    1367 (83.0)

    878 (83.5)

    489 (82.0)

     

    Recessive model

    GG

    568 (34.4)

    367 (34.8)

    201 (33.7)

    0.634

     

    CG + CC

    1080 (65.6)

    685 (65.2)

    395 (66.3)

     

    Additive model

    CG

    799 (48.6)

    511 (48.7)

    288 (48.3)

    0.922

     

    CC + GG

    849 (51.4)

    541 (51.3)

    308 (51.7)

     

    Allele

    C

    1361 (41.3)

    859 (40.8)

    502 (42.1)

    0.471

     

    G

    1935 (58.7)

    1245 (59.2)

    690 (57.9)

     

    rs12435797 (SNP2)

    Genotype

    T/T

    532 (32.3)

    323 (30.7)

    209 (35.1)

    0.111

     

    G/G

    348 (21.1)

    235 (22.3)

    113 (19.0)

     
     

    G/T

    768 (46.6)

    495 (47.0)

    273 (45.9)

     

    Dominant model

    TT

    532 (32.3)

    323 (30.7)

    209 (35.1)

    0.063

     

    GT + GG

    1116 (67.7)

    730 (69.3)

    386 (64.9)

     

    Recessive model

    GG

    348 (21.1)

    235 (22.3)

    113 (19.0)

    0.112

     

    GT + TT

    1300 (78.9)

    818 (77.7)

    482 (81.0)

     

    Additive model

    GT

    768 (46.6)

    495 (47.0)

    273 (45.9)

    0.660

     

    TT + GG

    880 (53.4)

    558 (53.0)

    322 (54.1)

     

    Allele

    T

    1832 (55.6)

    1141 (54.2)

    691 (58.1)

    0.031*

     

    G

    1464 (44.4)

    965 (45.8)

    499 (41.9)

     

    rs1019075 (SNP3)

    Genotype

    C/C

    176 (10.7)

    115 (10.9)

    61 (10.2)

    0.846

     

    T/T

    848 (51.5)

    536 (51.0)

    312 (52.3)

     
     

    C/T

    624 (37.9)

    401 (38.1)

    223 (37.4)

     

    Dominant model

    CC

    176 (10.7)

    115 (10.9)

    61 (10.2)

    0.660

     

    CT + TT

    1472 (89.3)

    937 (89.1)

    535 (89.8)

     

    Recessive model

    TT

    848 (51.5)

    536 (51.0)

    312 (52.3)

    0.585

     

    CT + CC

    800 (48.5)

    516 (49.0)

    284 (47.7)

     

    Additive model

    CT

    624 (37.9)

    401 (38.1)

    223 (37.4)

    0.778

     

    CC + TT

    1024 (62.1)

    651 (61.9)

    373 (62.6)

     

    Allele

    C

    976 (29.6)

    631 (30.0)

    345 (28.9)

    0.527

     

    T

    2320 (70.4)

    1473 (70.0)

    847 (71.1)

     

    rs17781919 (SNP4)

    Genotype

    C/C

    1621 (98.4)

    1034 (98.0)

    587 (99.0)

    0.133

     

    C/T

    27 (1.6)

    21 (2.0)

    6 (1.0)

     

    Allele

    C

    3269 (99.2)

    2089 (99.0)

    1180 (99.5)

    0.135

     

    T

    27 (0.8)

    21 (1.0)

    6 (0.5)

     

    The P value of genotype was calculated by Fisher’s exact test

    *P <0.05

     
  3. 3.
    Numb genotypes and lipid profile
    1. i.
      We found that the rs2108552 was significantly associated with plasma TC and LDL-C level in a dominant model, additive model, or recessive model before (All P < 0.05) and after multivariate adjustment of ethnicity, gender, age, smoking and obesity (All P < 0.05; Table 3). Further, the rs2108552 was significantly associated with plasma HDL-C level in a dominant model before (P = 0.000) and after multivariate adjustment (P = 0.012; Table 3).
      Table 3

      Associations between rs2108552 and lipid parameters

       

      Mean ± SD

      Model 1

      Model 2

       

      Homozygous for wild allele (C)

      Heterozygous

      Homozygous for rare allele (G)

      P (Dom)

      P (Rec)

      P (Add)

      P (Dom)

      P (Rec)

      P (Add)

      TC (mmol/L)

      4.35 ± 1.24

      4.34 ± 1.02

      3.89 ± 1.01

      0.000

      0.042

      0.006

      0.000

      0.007

      0.015

      LDL-C (mmol/L)

      2.64 ± 0.96

      2.70 ± 0.90

      2.51 ± 0.83

      0.007

      0.009

      0.030

      0.000

      0.004

      0.011

      HDL-C (mmol/L)

      1.02 ± 0.56

      1.02 ± 0.48

      1.15 ± 0.67

      0.000

      0.213

      0.120

      0.012

      0.913

      0.083

      TG (mmol/L)

      1.99 ± 1.62

      1.97 ± 1.41

      1.91 ± 1.47

      0.478

      0.676

      0.889

      0.386

      0.503

      0.954

      TC total cholesterol, LDL-C low density lipoprotein-cholesterol, HDL-C high density lipoprotein-cholesterol, TG triglycerides, Dom dominant model, Rec recessive model, Add additive model

      Model 1: Unadjusted model; Model 2: Analysis of covariance adjusted for ethnicity, gender, age, smoking and obesity

       
    2. ii.
      By analyzing a dominant model, additive model, or recessive model, we found that the rs12435797 was significantly associated with plasma TC level before (All P < 0.05) and after multivariate adjustment of ethnicity, gender, age, smoking and obesity. (All P < 0.05; Table 4). In addition, we also found that rs12435797 was associated with HDL-C level in a dominant and an additive model (P = 0.001, P = 0.010). However, the difference did not remain statistically significant after multivariate adjustment.
      Table 4

      Associations between rs12435797 and lipid parameters

       

      Mean ± SD

      Model 1

      Model 2

       

      Homozygous for wild allele (T)

      Heterozygous

      Homozygous for rare allele (G)

      P (Dom)

      P (Rec)

      P (Add)

      P (Dom)

      P (Rec)

      P (Add)

      TC (mmol/L)

      4.30 ± 1.28

      4.36 ± 1.09

      4.13 ± 1.02

      0.000

      0.005

      0.004

      0.007

      0.006

      0.016

      LDL-C (mmol/L)

      2.60 ± 0.96

      2.70 ± 0.96

      2.60 ± 0.80

      0.171

      0.279

      0.030

      0.808

      0.545

      0.527

      HDL-C (mmol/L)

      1.03 ± 0.66

      1.01 ± 0.38

      1.11 ± 0.67

      0.001

      0.494

      0.010

      0.874

      0.791

      0.833

      TG (mmol/L)

      1.94 ± 1.35

      2.02 ± 1.60

      1.93 ± 1.43

      0.506

      0.758

      0.381

      0.651

      0.942

      0.545

      TC total cholesterol, LDL-C low density lipoprotein-cholesterol, HDL-C high density lipoprotein-cholesterol, TG triglycerides, Dom dominant model, Rec recessive model, Add additive model

      Model 1: Unadjusted model; Model 2: Analysis of covariance adjusted for ethnicity, gender, age, smoking and obesity

       
    3. iii.
      We observed that the rs1019075 was significantly associated with plasma LDL-C and HDL-C level by analyses of a dominant model, additive model, or recessive model before (All P < 0.05) and after multivariate adjustment of ethnicity, gender, age, smoking and obesity (All P < 0.05; Table 5).
      Table 5

      Associations between rs1019075 and lipid parameters

       

      Mean ± SD

      Model 1

      Model 2

       

      Homozygous for wild allele (C)

      Heterozygous

      Homozygous for rare allele (T)

      P (Dom)

      P (Rec)

      P (Add)

      P (Dom)

      P (Rec)

      P (Add)

      TC (mmol/L)

      4.25 ± 1.02

      4.30 ± 1.23

      4.22 ± 1.10

      0.568

      0.553

      0.331

      0.324

      0.120

      0.029

      LDL-C (mmol/L)

      2.68 ± 0.90

      2.63 ± 0.91

      2.57 ± 0.97

      0.016

      0.008

      0.033

      0.029

      0.007

      0.043

      HDL-C (mmol/L)

      1.02 ± 0.77

      1.01 ± 0.46

      1.08 ± 0.55

      0.022

      0.322

      0.047

      0.031

      0.469

      0.062

      TG (mmol/L)

      1.97 ± 1.45

      1.95 ± 1.55

      2.01 ± 1.47

      0.662

      0.936

      0.719

      0.367

      0.707

      0.353

      TC total cholesterol, LDL-C low density lipoprotein-cholesterol, HDL-C high density lipoprotein-cholesterol, TG triglycerides, Dom dominant model, Rec recessive model, Add additive model

      Model 1: Unadjusted model; Model 2: Analysis of covariance adjusted for ethnicity, gender, age, smoking and obesity

       
    4. iv.
      We found that rs17781919 was significantly associated with TC, LDL-C and HDL-C level (All P ≤ 0.000) and the difference remained statistically significant after (All P ≤ 0.001; (Table 6) multivariate adjustment of ethnicity, gender, age, smoking and obesity.
      Table 6

      Associations between rs17781919 and lipid parameters

       

      Mean ± SD

      Model 1

      Model 2

      Wild allele (T)

      Rare allele (C)

        

      TG (mmol/L)

      1.97 ± 1.50

      1.66 ± 0.93

      0.315

      0.330

      TC (mmol/L)

      4.27 ± 1.11

      3.46 ± 0.74

      0.000

      0.001

      LDL-C (mmol/L)

      2.64 ± 0.93

      0.98 ± 0.55

      0.000

      0.000

      HDL-C (mmol/L)

      1.05 ± 0.49

      1.92 ± 0.49

      0.000

      0.000

      TC total cholesterol, LDL-C low density lipoprotein-cholesterol, HDL-C high density lipoprotein-cholesterol, TG triglycerides

      Model 1: Unadjusted model; Model 2: Analysis of covariance adjusted for ethnicity, gender, age, smoking and obesity

       
    5. v.

      In addition, there exist no significant difference between different genotypes and the alleles of four SNPs in study cohorts for TG level (all P > 0.05, respectively; Tables 36).

       
     

Discussion

Findings

In this study, we have genotyped four kinds of SNP of Numb gene, and found that variation in Numb gene associates with cholesterol levels among Chinese subjects. This is the first endeavor to study the common allelic variant in Numb gene and its association with four types of lipid parameters.

Hypercholesterolemia has been observed to be the cause of multiple physiologic outcomes, such as coronary artery disease, diabetes, and obesity [3436]. Previous study indicates that Numb gene may provide a therapeutic target for hypercholesterolemia [20]. Meanwhile, Numb genetic variant was directly responsible for the decreased LDL-C concentration [21]. However, few researches have been conducted regarding the relationship between polymorphism of Numb gene and lipid profile.

Our study shows that rs2108552 was significantly associated with plasma TC and LDL-C level by analyses of a dominant model, additive model, or recessive model. Such association was remained significant after multivariate adjustment. Further, individuals with the C allele of rs2108552 had significantly higher plasma TG and LDL-C level when compared with G allele. To our knowledge, a high level of LDL-C and TC concentration in plasma increase the risk of atherosclerotic cardiovascular disease. In previous study, we have found that rs2108552 of Numb gene was associated with CAD among Chinese subjects, and thus assumed CC genotype might be a risk genetic marker for CAD. Together, these results might provide convincing evidence for assuming people who carry C allele may have higher probabilities of suffering from atherosclerotic cardiovascular disease (CVD) than people who carry G allele of rs2108552 of Numb gene.

We also observed that C allele of SNP rs17781919 had significantly lower plasma TC level, LDL-C level and high HDL-C level when compared with T allele and further proved that this variation was associated with low level of LDL-C among large population. Moreover, these results also indicate that people who carry C allele reduces the risk of atherosclerotic cardiovascular disease (CVD) when compared to the people who carry T allele.

Similarly, we observed that the rs12435797 was significantly associated with plasma TC level, and rs1019075 was significantly associated with LDL-C and HDL-C level. Further, individuals with GG genotype of rs12435797 demonstrate low TC level compared with individuals with TT genotype, and individuals with TT genotype of rs1019075 also demonstrate low LDL-C level and high HDL-C level compared with individuals with CC genotype. Such association was also remained significant after multivariate adjustment. Therefore, we assumed that people who carry G allele of rs12435797 and T allele of rs1019075 may have lower probabilities of suffering from atherosclerotic cardiovascular disease (CVD) when compared to the people who carry T allele of rs12435797 and C of rs1019075 allele.

In addition, we have not observed a remarkable difference between TG level and three kinds of SNPs of Numb gene. This might indicate Numb gene does not have as strong correlation with TG concentration as it does with cholesterol concentration. Nevertheless, the relationship between TG concentration and Numb gene needs further study.

Limitations and shortcomings

First, the source of subjects was limited to the First Affiliate Hospital of Xinjiang Medical University, and these subjects may possess some risk factors of cardiovascular disease and different kinds of lifestyles that both might positively influence lipid profile. Second, a longitudinal epidemiological study, over a reasonably long time span, is required to obtain evidence with higher quality and reliability.

Conclusion

Our results indicate Numb gene rs2108552 and rs17781919 polymorphisms closely associate with cholesterol level, and this kind of association could be a crucial genetic marker and determinant in clinical research. Nevertheless, our study requires further epidemiological survey by resorting to large numbers of samples.

Notes

Abbreviations

CAD: 

Coronary artery disease

LDL-C: 

Low density lipoprotein-cholesterol

HDL-C: 

High density lipoprotein-cholesterol

TC: 

Total cholesterol

TG: 

Triglycerides

SNPs: 

Single nucleotide polymorphisms

BMI: 

Body mass index

BUN: 

Blood urea nitrogen

Cr: 

Creatinine

PTB: 

Phosphotyrosine-binding domain

PRR: 

Proline-rich region domain

MAF: 

Minor allele frequency

DNA: 

Deoxyribonucleic acid

EDTA: 

Ethylenediaminetetraacetic acid

PCR: 

Polymerase chain reaction

SDS: 

Sequence detection systems

CVD: 

Cardiovascular disease

Declarations

Acknowledgements

This work was supported by grants from Science and Technology Program of Xinjiang Uighur Autonomous Region (2013911111).

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Cardiology, the First Affiliated Hospital of Xinjiang Medical University
(2)
Xinjiang Key Laboratory of Cardiovascular Disease Research
(3)
The First Affiliated Hospital of Xinjiang Medical University
(4)
Present address: Department of Cardiology, the First Affiliated Hospital of Xinjiang Medical University
(5)
Present address: Xinjiang Key Laboratory of Cardiovascular Disease Research

References

  1. Kreisberg RA, Oberman A. Clinical review 141: lipids and atherosclerosis: lessons learned from randomized controlled trials of lipid lowering and other relevant studies. In J Clin Endocrinol Metab. 2002;87(2):423–37.View ArticleGoogle Scholar
  2. Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, et al. Heart disease and stroke statistics-2011 update: a report from the American Heart Association. Circulation. 2011;123:e18–209.PubMed CentralPubMedView ArticleGoogle Scholar
  3. Writing Group M, Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, et al. Heart disease and stroke statistics--2010 update: a report from the American Heart Association. Circulation. 2010;121:e46–215.Google Scholar
  4. Mackay J, Mensah GA. The Atlas of Heart Disease and Stroke (World Health Organization) Geneva; 2004.Google Scholar
  5. Law MR, Wald NJ, Rudnicka AR. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. Br Med J. 2003;326(7404):1423.View ArticleGoogle Scholar
  6. Kuulasmaa K, Tunstall-Pedoe H, Dobson A, Fortmann S, Sans S, Tolonen H, et al. Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet. 2000;355:675–87.PubMedView ArticleGoogle Scholar
  7. Clarke R, Emberson JR, Parish S, Palmer A, Shipley M, Linksted P, et al. Cholesterol fractions and apolipoproteins as risk factors for heart disease mortality in older men. Arch Intern Med. 2007;167:1373–8.PubMedView ArticleGoogle Scholar
  8. Bhagavan NV. Medical Biochemistry. 4th ed. San Diego: Harcourt/Academic Press; 2002. p. 1016.Google Scholar
  9. Nordestgaard BG, Benn M, Schnohr P, Tybjaerg-Hansen A. Non-fasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. J Am Med Assoc. 2007;298:299–308.View ArticleGoogle Scholar
  10. Grundy SM, Cleeman JI, Merz CN, Brewer Jr HB, Clark LT, Hunninghake DB, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation. 2004;110:227–39.PubMedView ArticleGoogle Scholar
  11. Gotto Jr AM, Brinton EA. Assessing low levels of high-density lipoprotein cholesterol as a risk factor in coronary heart disease: a working group report and update. J Am Coll Cardiol. 2004;43:717–24.PubMedView ArticleGoogle Scholar
  12. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009;41:47–55.PubMed CentralPubMedView ArticleGoogle Scholar
  13. Chasman DI, Paré G, Zee RY, Parker AN, Cook NR, Buring JE, et al. Genetic loci associated with plasma concentration of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A1, and apolipoprotein B among 6382 white women in genome-wide analysis with replication. Circ Cardiovasc Genet. 2008;1:21–30.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Edmondson AC, Braund PS, Stylianou IM, Khera AV, Nelson CP, Wolfe ML, et al. Dense genotyping of candidate gene loci identifies variants associated with high-density lipoprotein cholesterol. Circ Cardiovasc Genet. 2011;4:145–55.PubMed CentralPubMedView ArticleGoogle Scholar
  15. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009;41:56–65.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Roncarati R, Sestan N, Scheinfeld MH, Berechid BE, Lopez PA, Meucci O, et al. The gamma-secretase-generated intracellular domain of beta-amyloid precursor protein binds Numb and inhibits Notch signaling. Proc Natl Acad Sci U S A. 2002;99:7102–7.Google Scholar
  17. Dho SE, Jacob S, Wolting CD, French MB, Rohrschneider LR, McGlade CJ. The mammalian numb phosphotyrosine-binding domain. Characterization of binding specificity and identification of a novel PDZ domain-containing numb binding protein, LNX. J Biol Chem. 1998;273:9179–87.PubMedView ArticleGoogle Scholar
  18. Nishimura T, Kaibuchi K. Numb controls integrin endocytosis for directional cell migration with aPKC and PAR-3. Dev Cell. 2007;13:15–28.PubMedView ArticleGoogle Scholar
  19. Tong X, Zitserman D, Serebriiskii I, Andrake M, Dunbrack R, Roegiers F. Numb independently antagonizes Sanpodo membrane targeting and Notch signaling in Drosophila sensory organ precursor cells. Mol Biol Cell. 2010;21:802–10.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Li PS, Fu ZY, Zhang YY, Zhang JH, Xu CQ, Ma YT, et al. The clathrin adaptor Numb regulates intestinal cholesterol absorption through dynamic interaction with NPC1L1. Nat Med. 2014;20:80–6.Google Scholar
  21. Wei J, Fu ZY, Li PS, Miao HH, Li BL, Ma YT, et al. The clathrin adaptor proteins ARH, Dab2, and numb play distinct roles in Niemann-Pick C1-Like 1 versus low density lipoprotein receptor-mediated cholesterol uptake. J Biol Chem. 2014;289:33689–700.Google Scholar
  22. SoRelle R. ATP III, calls for more intensive low-density lipoprotein lowering in target groups. Circulation. 2002;106:e9068–8.PubMedView ArticleGoogle Scholar
  23. Patel S, Flyvbjerg A. Koza’kova’ M, Frystyk J, Ibrahim IM, Petrie JR, et al. Variation in the ADIPOQ gene promoter is associated with carotid intima media thickness independent of plasma adiponectin levels in healthy subjects. Eur Heart J. 2008;29:386–93.PubMedView ArticleGoogle Scholar
  24. Zheng YY, Xie X, Ma YT, Yang YN, Fu ZY, Li XM, et al. Relationship between a novel polymorphism of the C5L2 gene and coronary artery disease. PLoS One. 2011;6:e20984.Google Scholar
  25. Xie X, Ma YT, Fu ZY, Yang YN, Ma X, Chen BD, et al. Association of polymorphisms of PTGS2 and CYP8A1 with myocardial infarction. Clin Chem Lab Med. 2009;47:347–52.Google Scholar
  26. Verdi JM, Bashirullah A, Goldhawk DE, Kubu CJ, Jamali M, Meakin SO, et al. Distinct human NUMB isoforms regulate differentiation vs. proliferation in the neuronal lineage. Proc Natl Acad Sci U S A. 1999;96:10472–6.Google Scholar
  27. Zwahlen C, Li SC, Kay LE, Pawson T, Forman-Kay JD. Multiple modes of peptide recognition by the PTB domain of the cell fate determinant Numb. EMBO J. 2000;19:1505–15.PubMed CentralPubMedView ArticleGoogle Scholar
  28. Li SC, Zwahlen C, Vincent SJ, McGlade CJ, Kay LE, Pawson T, et al. Structure of a Numb PTB domain-peptide complex suggests a basis for diverse binding specificity. Nat Struct Biol. 1998;5:1075–83.Google Scholar
  29. Li SC, Songyang Z, Vincent SJ, Zwahlen C, Wiley S, Cantley L, et al. High-affinity binding of the Drosophila Numb phosphotyrosine-binding domain to peptides containing a Gly-Pro-(p)Tyr motif. Proc Natl Acad Sci U S A. 1997;94:7204–9.Google Scholar
  30. Naganuma T, Nakayama T, Sato N, Fu Z, Yamaguchi M, Soma M, et al. Haplotype-based case-control study between human apurinic/apyrimidinic endonuclease 1/redox effector factor-1 gene and cerebral infarction. Clin Biochem. 2009;42:1493–9.PubMedView ArticleGoogle Scholar
  31. Li X, Ma YT, Xie X, Yang YN, Ma X, Zheng YY, et al. Association of Egr3 genetic polymorphisms and coronary artery disease in the Uygur and Han of China. Lipids Health Dis. 2014;13:84.Google Scholar
  32. Nakayama T, Soma M, Rahmutula D, Ozawa Y, Kanmatsuse K. Isolation of the 5′-flanking region of genes by thermal asymmetric interlaced polymerase chain reaction. Med Sci Monit. 2001;7:345–9.PubMedGoogle Scholar
  33. Sano M, Kuroi N, Nakayama T, Sato N, Izumi Y, Soma M, et al. The association study of calcitonin-receptor-like receptor gene in essential hypertension. Am J Hypertens. 2005;18:403–8.PubMedView ArticleGoogle Scholar
  34. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Invest. 2006;116:1793–801.PubMed CentralPubMedView ArticleGoogle Scholar
  35. Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat Rev Mol Cell Biol. 2008;9:367–77.PubMed CentralPubMedView ArticleGoogle Scholar
  36. Blüher M. Adipose tissue dysfunction in obesity. Exp Clin Endocrinol Diabetes. 2009;117:241–50.PubMedView ArticleGoogle Scholar

Copyright

© Abudoukelimu et al. 2015

Advertisement