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Association of Polymorphisms of IGF1R and Genes in the Transforming Growth Factor–β/Bone Morphogenetic Protein Pathway with Bacteremia in Sickle Cell Anemia

  1. Adeboye H. Adewoye1,
  2. Vikki G. Nolan1,
  3. Qianli Ma1,
  4. Clinton Baldwin2,3,
  5. Diego F. Wyszynski1,
  6. John J. Farrell1,
  7. Lindsay A. Farrer1, and
  8. Martin H. Steinberg1
  1. 1Departments of Medicine, Boston, Massachusetts
  2. 2Department of Pediatrics, Boston, Massachusetts
  3. 3Department of Center for Human Genetics, Boston University School of Medicine, Boston, Massachusetts
  1. Reprints or correspondence: Dr. Martin H. Steinberg, E248, Boston Medical Center, 88 E. Newton St., Boston, MA 02118 (mhsteinb{at}bu.edu).
 
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Abstract

Infection and bacteremia are common in sickle cell disease. We hypothesized that, consistent with evidence for the genetic modulation of other disease complications, the risk of developing bacteremia might also be genetically modulated. Accordingly, we studied the association of single nucleotide polymorphisms (SNPs) in candidate genes with the risk of bacteremia in sickle cell anemia. We found significant associations with SNPs in IGF1R and genes of the TGF-β/BMP pathway (BMP6, TGFBR3, BMPR1A, SMAD6 and SMAD3). We suggest that both IGF1R and the TGF-β/BMP pathway could play important roles in immune function in sickle cell anemia and their polymorphisms may help identify a “bacteremia-prone” phenotype.

Sickle cell anemia, caused by homozygosity for a mutation in the β hemoglobin gene (HBB; glu6val) has multiple complications due to sickle vasculopathy and hemolytic anemia. Among these complications is an increased risk of infection and bacteremia. Compared with the general African American population, the incidence of invasive Streptococcus pneumoniae infection is 20- to 100-fold higher in children with sickle cell anemia [1]. One major explanation for the increased incidence of infection is abnormal splenic function. By 10 years of age, >90% of patients with sickle cell anemia have splenic atrophy or laboratory evidence of functional asplenia contributing to an impairment in cellular and humoral immunity. However, other factors besides splenic function, such as polymorphisms of the human leukocyte antigen system and the haplotype of the β-globin gene cluster, may modulate the susceptibility to infection in sickle cell anemia. To study a possible genetic basis for this increased susceptibility to bacteremia in sickle cell anemia, we studied the association of single nucleotide polymorphisms (SNPs) in candidate genes that might determine an increased or decreased risk of infection for individuals with this phenotype.

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Methods

Database. The Cooperative Study of Sickle Cell Disease enrolled 4082 patients from 23 clinical centers in the United States who were observed longitudinally for an average of 5.2 years [2]. Blood samples were obtained for the detection of α thalassemia and the β-like globin gene cluster haplotype, and DNA from these samples was deposited in a National Institutes of Health–controlled repository and used for SNP genotyping. We limited our study to patients with sickle cell anemia, with or without coincident α thalassemia (heterozygosity or homozygosity for gene deletion [HBA1 and HBA2] α thalassemia), thereby yielding 1473 patients with available demographic and clinical data and DNA samples. The use of these data was approved by the Institutional Review Board of Boston University/Boston Medical Center (Boston, MA).

Case patients and control subjects. A case patient was defined as any patient enrolled in the Cooperative Study of Sickle Cell Disease who entered a clinic, emergency department, or hospital at a study institution for which a positive blood culture result not associated with a known source of infection (such as osteomyelitis, septic arthritis, pneumonia, or meningitis) was found. Control subjects had no history of bacteremia and no incident bacteremic events.

Laboratory. The laboratory methods used to define the hemoglobin phenotype and the haplotypes of the α- and β-like globin gene clusters were previously described [3]. Validated SNPs with population frequency information and heterozygosity values of >.1 in the candidate genes were first selected from public databases (http://www.ncbi.nlm.nih.gov/), and SNP analysis was done by mass spectrometry using the Sequenom mass spectrometry SNP genotyping system (Sequenom), as reported elsewhere [4]. Our candidate genes were selected because of their potential roles in inflammation, reperfusion injury, nitric oxide chemistry, oxidant biology, and other elements of the pathophysiology of sickle cell disease. After we found significant associations among SNPs in insulin growth factor receptor 1 (IGF1R) and of the TGF-β/BMP pathway with many of the subphenotypes of sickle cell disease, a second phase of genotyping was done to further explore these genes. Follow-up SNPs were selected using SNPBROWSER software, version 3.0 (Applied Biosystems). Each haplotype tagging SNP that was selected met the assay design requirements for genotyping using the ABI SNPLEX genotyping system (Applera Group—Applied Biosystems Group). SNPLEX reactions were analyzed on an Applied Biosystems 3730 DNA Sequencer. A summary of all SNPs and genes studied are available in spreadsheet form and can be downloaded at http://www.bu.edu/sicklecell/downloads/Publications_SupplementaryInformation/TGFB_SNPs.xls.

For quality control purposes, ∼3% of the DNA samples were regenotyped, and Hardy-Weinberg equilibrium was assessed for each SNP. Hardy-Weinberg equilibrium was determined prior to analysis and, because all members of our study population have sickle cell anemia, was done for quality-control purposes rather than to determine whether the genotypes met Mendelian expectation.

Data acquisition, monitoring, and quality control. An extensive data file containing medical history and laboratory findings was obtained from the Cooperative Study of Sickle Cell Disease study database. A tracking database was developed to record data set characteristics, including file name, date, record count, and contents of each data file. Identities were verified against those in the central sample tracking database, and data formatting was checked for compatibility with the cumulative genotype data set. Data were forwarded to a structured query language (SQL) server database that contained the genotype data set that was used in this study.

Statistical analysis. Genotypic counts were compared between patients with sickle cell anemia who had bacteremia and patients with sickle cell anemia without bacteremia using multiple logistic regression adjusted for leukocyte count and age. These parameters where chosen because they were differentially distributed among case patients and control subjects (table 1) and/or based on the current understanding of their influence in the morbidity and mortality of sickle cell anemia. For each SNP, OR and 95% CI were calculated by assigning either 1 of the homozygote genotype classes or a pooled group of heterozygotes and 1 homozygote class as the referent. In our initial screening, we considered a SNP to have an association with a phenotype when the P value was .01 or if 1 SNPs in the same gene were considered to be significant at the .05 level. A second phase of genotyping was done to study additional SNPs in these genes and in other genes related to or located near them. Individual associations between the additional SNPs and bacteremia were ascertained using the same analytic method described above.

Figure 1
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Figure 1

Linkage disequilibrium plot of the 9 single nucleotide polymorphisms in BMP6 for all subjects. White, D' <1 and logarithm of the odds (LOD) <2; gray, D' <1 and LOD 2; black, D' = 1 and LOD 2.

Figure 2
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Figure 2

Linkage disequilibrium plot of the 27 single nucleotide polymorphisms in IGF1R for all subjects. White, D' <1 and LOD <2; gray, D' <1 and LOD 2; black, D' = 1 and LOD 2.

Table 1
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Table 1

Clinical and demographic characteristics of case patients with bacteremia and control subjects.

Haplotype analysis. Pairwise linkage disequilibrium between SNPs, expressed as D' and r2, was evaluated and illustrated using the software Haploview, version 3.2 (http://www.broad.mit.edu/mpg/haploview/). For candidate genes with dense SNP coverage, haplotype associations were explored with score tests that account for linkage phase ambiguity. The score tests, derived from generalized linear models, are used for global tests of association and as haplotype-specific tests. A 2-step strategy was applied to explore haplotype associations: first, a sliding window method of 2-SNPs, 3-SNPs, and 4-SNPs was used to scan the regions that yielded significant association; then, on the basis of the results from the sliding window method, associations with specific haplotypes were analyzed. The haplo.stats program, version 1.2.1, which implements the algorithm of Schaid et al. [5], was used to perform these analyses. SNPs with low minor allele frequency (<.05) and significant deviation from the Hardy-Weinberg equilibrium expectation (P < .01) were dropped from the haplotype analysis. The minimum frequency for a haplotype to be analyzed was set at 0.5%.

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Results

Demographic characteristics and hematological data. Relevant clinical data for the patients with sickle cell anemia included in this study are shown in table 1. We did not observe significant differences between the 145 case patients with bacteremia and the 1248 control subjects in sex, fetal hemoglobin concentration, distribution of β-globin gene cluster haplotypes, or the presence of coincident α thalassemia. Patients with bacteremia had slightly lower hemoglobin concentrations and higher leukocyte counts. Subjects who received antibiotic prophylaxis (P = .0006) or received Haemophilus influenzae vaccination (P < .0247) were more likely to develop bacteremia due to an organism not covered by antibiotic prophylaxis or the H. influenzae vaccine.

Seventeen different organisms were isolated from patients with sickle cell anemia and bacteremia: S. pneumoniae (41% of patients), Escherichia coli (21%), H. influenzae (7.6%), Staphylococcus aureus (5.5%), Streptococcus viridans (4.8%), Salmonella enterica serovar Typhi (4.1%), and Klebsiella species(4.1%). The remaining 12% of organisms isolated from patients with sickle cell anemia and bacteremia included Enterobacteriacae, group A streptococci, Haemophilus parainfluenzae, Staphylococcus epidermidis, Neisseria meningiditis, Salmonella species, and Serratia species. Insufficient numbers of case patients did not allow us to examine the association of SNPs with a single infecting organism.

Initial SNP genotyping. In the first phase of genotyping, we studied >200 SNPs in ∼50 candidate genes for an association with bacteremia. The SNPs that met our criteria for an association with bacteremia are shown in table 2. Among the SNPs in known genes were 4 in BMP6 and another TGFBR3. These genes have been associated with other phenotypes of sickle cell disease, such as stroke and osteonecrosis [6].

Table 2
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Table 2

Single single nucleotide polymorphism (SNP) genotype association results.

Second-phase SNP screening. Next, we genotyped 86 SNPs in 20 genes of the TGF-β/BMP pathway and 27 SNPs in IGF1R. Two SNPs in BMPR1A (rs6586039 and hCV1663921), 1 SNP each in SMAD6 and SMAD3 (rs5014202 and rs10518707, respectively), and 4 SNPs in IGF1R (rs1319868, rs1567811, rs8041224, and rs2872060) revealed evidence for an association with bacteremia (table 2).

Haplotype analysis. After we noted significant findings in BMP6 and IGF1R, haplotype analysis for each gene was performed (for linkage disequilibrium plot, see http://www.bu.edu/sicklecell/downloads/Publications_SupplementaryInformation/LD_Plot_BMP6.jpg) and http://www.bu.edu/sicklecell/downloads/Publications_SupplementaryInformation/LD_plot_IGF1R.jpg) A region in BMP6 with 2 SNPs in strong linkage disequilibrium, rs408505 and rs449853, yielded the most statistically significant association using the sliding window method. The global statistic for this region reached a P value of .0031. Comparing the haplotype containing C-C with the most common haplotype (reference C-T) yielded an OR of 0.5 (95% CI, 0.4–0.8) and a specific P value of .0003 (figure 1).

Two significant 2-SNP haplotypes were identified in IGF1R; the haplotype consisting of SNPs rs6598542 and rs8041224 was the most significant, with a global P value of .0409. Comparing the haplotype containing A-T with the most common haplotype (reference A-C) yielded an OR of 0.5 (95% CI, 0.3–0.8) and a specific P value of .029. The second haplotype (rs1319868 and rs11247361) was marginally significant, with a global P value of .0474, and had a specific P value of .009 (OR, 1.6; 95% CI, 1.2–2.3) when comparing the haplotype containing T-C with the reference (G-C) (figure 2).

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Discussion

Sickle cell anemia, a prototypical monogenic Mendelian disorder, has a notably heterogeneous phenotype [7]. We hypothesized that, consistent with evidence for the genetic modulation of other sickle cell disease complications such as stroke, osteonecrosis, priapism, and leg ulceration [8], the likelihood of developing bacteremia could be associated with polymorphisms in some candidate genes. Previous studies have suggested that genetic heterogeneity influences the susceptibility to infection in the general population [9]. In sickle cell anemia, the incidence of infection may also be modulated by polymorphisms of the human leukocyte antigen system [10], the mannose-binding lectin receptor [11], the Fc receptor [12], and the haplotype of the β-globin like gene cluster [13].

The TGF-β superfamily includes TGF-β, bone morphogenetic proteins, and various activins. The members of this superfamily are critical to the regulation and transcription of proteins that modulate hematopoiesis, endothelial function, cell growth, WBC differentiation, and angiogenesis [14]. Variants of genes in this superfamily have been associated with pulmonary hypertension, cancer, osteoporosis, and rheumatic heart disease [15, 16]. Preliminary studies suggest that this pathway has been associated with survival in sickle cell anemia [17]. Members of the TGF-β pathway facilitate neutrophil differentiation and inhibit their proliferation and activation while BMP6 inhibits B cell maturation [18, 19]. Additionally, increased susceptibility to gram-negative and gram-positive organisms and impaired WBC function have been demonstrated in a murine model, with elevated circulating IgG-bound TGF-β [20–22], possibly through the modulation of immune cells via the Fc receptor [18, 21, 23]. We identified a SNP in SMAD3 to be significantly associated with bacteremia. Smad3-deficient mice have been observed to be susceptible to infection because of impaired mucosal and T cell function, suggesting a key role for TGF-β in these processes [24]. Impaired B cell maturation with defective opsonization and neutrophil chemotaxis, in combination with absent splenic function in most adults with sickle cell anemia, may explain the association of SNPs in the TGF-β pathway with bacteremia in some individuals.

IGF1R is ubiquitously expressed and regulates angiogenesis, apoptosis, and recruitment and differentiation of T and B cells. Additionally, it interacts with other signaling pathways, such as MAPK (ERK) pathways, through recruitment of the adapter proteins IRS-1, Gab and Crk, the PI3 kinase pathway, and members of the SAPK/JNK—all of which interact with members of the TGF-β pathway [25–31]. Dysfunctional immune response, lymphocyte recruitment and proliferation, and abnormal TI-2 responses in MZ B cells have been described in an igf1r-deficient mouse model [32, 33]. This mechanism may also explain, in part, the poor response of patients with type 2 diabetes mellitus to pneumoccocal vaccination and an increased risk of infection in these patients [33]. Variation in IGF1R might modulate the risk of infection by contributing to abnormal signaling in the TGF-β pathway and impairing the response of T and B lymphocytes to bacteria.

Modulation of the susceptibility of a patient with sickle cell anemia to infection and the host response to infection, as with other complications of sickle cell disease (like stroke), is likely to involve interactions of many genes and environmental factors. It is unlikely that a single genetic polymorphism alone will have a major effect. Therefore, the OR for the association of a SNP with this phenotype may be modest. Applying a Bonferroni correction or controlling the false discovery rate would be an excessively stringent method of excluding false-positive results and would require impractically large patient and control groups to find a positive result. Our studies are a first exploratory step in understanding the genetic modulation of the susceptibility to bacteremia in sickle cell anemia. Genome-wide association studies, along with sophisticated modeling techniques, may be needed to completely define the web of genetic associations with this phenotype [34].

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Acknowledgments

Financial support. National Heart, Lung, and Blood Institute (HL R01 68970 [to M.H.S.] and T32 HL007501 [to V.G.N.]).

Potential conflicts of interest. All authors: no conflicts.

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Footnotes

  • Presented in part: 47th Annual Meeting of the American Society of Hematology, Atlanta, Georgia, 12 December 2005 (abstract 3170); Blood 2005; 106:884a, and at the 29th Annual Meeting of the National Sickle Cell Disease Program, Memphis, Tennessee, 11 April 2006 (abstract 43).

  • Received May 10, 2006.
  • Accepted May 18, 2006.
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  1. Clin Infect Dis. (2006) 43 (5): 593-598. doi: 10.1086/506356
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