The -1997 G/T and Sp1 Polymorphisms in the Collagen Type I alpha1 (COLIA1) Gene in Relation to Changes in Femoral Neck Bone Mineral Density and the Risk of Fracture in the Elderly: The Rotterdam Study

The COLIA1 Sp1 polymorphism has been associated with bone mineral density (BMD) and fracture. A promoter polymorphism, -1997 G/T, also has been associated with BMD. In this study, we examined whether these polymorphisms alone and in the form of haplotypes influence bone parameters and fracture risk in a large population-based cohort of elderly Caucasians. We determined the COLIA1 -1997 G/T (promoter) and Sp1 G/T (intron) polymorphisms in 6,280 individuals and inferred haplotypes. Femoral neck BMD and BMD change were compared across COLIA1 genotypes at baseline and follow-up (mean 6.5 years). We also investigated the relationship between the COLIA1 polymorphisms and incident nonvertebral fractures, which were recorded during a mean follow-up period of 7.4 years. Vertebral fractures were assessed by radiographs on 3,456 genotyped individuals. Femoral neck BMD measured at baseline was 3.8% lower in women carrying two copies of the T-Sp1 allele (P for trend = 0.03). No genotype dependent differences in BMD loss were observed. In women homozygous for the T allele of the Sp1 polymorphism, the risk of fragility fracture increased 2.3 times (95% confidence interval 1.4–3.9, P = 0.001). No such association was observed with the promoter polymorphism. In men, no association with either the Sp1 or the -1997 G/T promoter polymorphism was seen with BMD or fracture. High linkage disequilibrium (LD; D′ = 0.99, r2 = 0.03) exists between the two studied polymorphisms. We observed three haplotypes in our population: haplotype 1 (Gpromoter–Gintron) frequency (f) = 69%, haplotype 2 (Gpromoter–Tintron) f = 17.6%, and haplotype 3 (Tpromoter–Gintron) f = 13.4%. Haplotype 2 was associated with a 2.1-fold increased risk of fragility fracture in women (95% confidence interval 1.2–3.7, P = 0.001). We confirm that the COLIA1 Sp1 polymorphism influences BMD and the risk of fracture in postmenopausal Caucasian women. In contrast, we found no independent effect of the -1997 G/T promoter polymorphism on BMD or fracture.
Introduction
Osteoporosis is a multifactorial disease with both genetic and environmental determinants. It is characterized by a reduction in bone mineral density (BMD) and microarchitectural deterioration of bone tissue, which leads to an increased risk of fracture in later life [1, 2]. Being a predictor of bone fragility and susceptibility to fracture, BMD is used for the diagnosis of osteoporosis [3, 4]. The risk of fracture is dependent not only on BMD but also on geometry, architecture, material properties, and mass distribution [5].
The skeletal determinants of osteoporotic fracture risk such as BMD, bone loss, and bone geometry are all subject to strong genetic influences [2, 6, 7]. It has been estimated from twin studies that 60-80% of the variance in BMD is attributable to genetic factors [8, 9]. Several genes are thought to be involved in the pathogenesis of osteoporosis. Collagen type I alpha 1 (COLIA1) is one of the most prominent candidate genes, which has been consistently associated with osteoporosis in different populations [10–12]. COLIA1 encodes the alpha 1 chain of collagen type I, which is the most abundant structural protein in the bone matrix; rare mutations in this gene cause osteogenesis imperfecta, a mendelian disorder presenting with moderate to severe bone fragility [13, 14].
Previously, Grant et al. [15] identified a relatively common guanine to thymidine (G→T) polymorphism in the first intron of COLIA1. This polymorphism affects one of the binding sites of the transcription factor Sp1 and results in increased expression of collagen type I alpha 1 in bone matrix in T carriers [11] .We and others have shown that the T allele is associated with osteoporosis, lower BMD [10, 15], and increased fracture risk [10, 12, 16, 17]. Moreover, in a very large prospective meta-analysis of individual data, we observed that the Sp1 polymorphism in the COLIA1 gene is associated with reduced BMD and incident vertebral fractures independent of BMD [18]. In addition to the Sp1 polymorphism, Garcia-Giralt et al. [19] described two polymorphisms within the COLIA1 promoter region: -1997 G/T and -1663 indelT. The study showed in a small cohort of postmenopausal Spanish women that the T allele of the -1997 G/T polymorphism was significantly associated with a 7.5% decreased lumbar spine BMD and a 12% decreased femoral neck BMD. Furthermore, they analyzed compound genotypes including three polymorphic sites. However, this small study of the promoter polymorphisms in women investigated the relation in form of compound genotypes, was not extended to haplotypes of promoter and Sp1 polymorphisms, and most importantly, did not analyze fractures, the clinically most relevant end point of osteoporosis.
Therefore, we investigated the influence of both COLIA1 polymorphisms independently and in the form of haplotypes in relation to baseline femoral neck BMD, change in BMD with follow-up, and risk of vertebral and nonvertebral fractures in a large population-based cohort of elderly Caucasian men and women.
Materials and Methods
Study Population
This study was embedded in the Rotterdam Study, a population–based cohort study in which all residents of the Rotterdam suburb Ommoord aged 55 years and older were invited to take part. The design of the study has been described elsewhere [20]. Written informed consent was obtained from all participants, and the Medical Ethics Committee of the Erasmus Medical Center approved the study. Baseline data collection was conducted in January 1990 and June 1993, while two follow-up assessments were performed between July 1993 and January 1996 and from July 1996 until December 1999. A total of 7,983 subjects participated in the study (response rate 78%), and for the present study, we examined 6,280 individuals who were genotyped.
Study Design
This study was performed in three steps. In the first step, we performed a cross-sectional analysis where we examined the relation between the genotype and baseline BMD (n = 5,737). In the second step, we performed a longitudinal analysis to study change in BMD between baseline and the second follow-up (mean 6.5 ± standard deviation [SD] 0.6 years, n = 2,670). In the third step, we looked at the relation between COLIA1 polymorphisms and the risk of incident fracture. We studied incidence of nonvertebral fracture (mean follow-up 7.4 ± 3.3 years, n = 6,280) and vertebral fractures assessed by radiographs both at baseline (1990–1993) and at follow-up visit’ between 1997 and 1999, thoracolumbar radiographs of the spine were available for 3,469 individuals in a mean follow-up of 6.4 years.
Measurements
BMD (g/cm2) of the hip and L2-L4 of the lumbar spine were measured by dual-energy X-ray absorptiometry (DXA) using a Lunar DPX densitometry (DPX-L) (Lunar Radiation, Madison, WI) and reanalyzed with DPX-IQ software, under standard protocols. Methods, quality assurance, accuracy, and precision issues of the DXA measurements have been described previously [21]. The relative change of BMD from baseline was estimated as the difference in BMD between assessment periods divided by the BMD at baseline.
Height (cm) and weight (kg) were measured with a stadiometer at the initial examination, in standing position wearing indoor clothes without shoes. Body mass index (BMI) was computed as weight divided by height (kg/cm2).
Fracture Follow-up
Information on incident nonvertebral fractures was collected from baseline (1990–1993) until January 1, 2002 (mean follow-up ± SD 7.4 ± 3.3 years, n = 6,280).
Nonvertebral fracture events were retrieved from computerized records of the general practitioners (GPs) in the research area. Research physicians regularly followed participant information in GP records outside the research area and made an independent review, encoding all reported events. Subsequently, a medical expert in the field reviewed all coded events for final classification. We excluded fractures that were considered nonosteoporotic, i.e., caused by cancer and high trauma, including fractures of the hand, foot, skull, and face. Subsequently, we analyzed separately “fragility” fractures, which were defined as any fracture of the hip, pelvis, or proximal humerus that had occurred with minimal trauma at older age (mean >75 years).
Vertebral Fracture Assessment
Both at baseline and at first follow-up, between 1997 and 1999, thoracolumbar radiographs of the spine were obtained. The follow-up radiographs were available for 3,456 individuals, who survived an average of 6.4 (SD = 0.4) years after the baseline visit and who were still able to come to our research center. All follow-up radiographs were scored for the presence of vertebral fracture by the McCloskey/Kanis method, as described previously [22].
Genotyping
Genomic DNA was extracted from samples of peripheral venous blood according to standard procedures. Genomic DNA (1–2 ng) was dispensed into 384-well plates using a Sciclone ALH3000 pipetting robot (Caliper, Mountain View, CA). Genotypes were determined using the Taqman allelic discrimination assay. The Assay-by-Design service (http://www.appliedbio-systems.com) was used to set up a Taqman allelic discrimination assay for the COL1PR-1997 polymorphism (primers: Fw, GCCTCCGGAGGGTGTCA, Rv, AAGGAGAGCAATTCTTACAGGTGTCT; probes: FAM-CCTGAGGGATGGAA-MGB, VIC-CCTGAAGGATGGAAG-MGB. The polymerase chain reaction (PCR) mixture included 1–2 ng of genomic DNA in a 2 μL volume and the following reagents: FAM and VIC probes (200 nM), primers (0.9 μM), and 2× Taqman PCR master mix (ABgene, Epsom, UK). Reagents were dispensed in a 384-well plate using the Deerac Equator NS808 (Deerac Fluidics, Dublin, Ireland). PCR cycling was performed in 384-well PCR plates in an ABI 9700 system (Applied Biosystems, Foster City, CA) and consisted of initial denaturation for 15 minutes at 95°C, 40 cycles with denaturation of 15 seconds at 95°C, and annealing and extension for 60 seconds at 60°C. Results were analyzed by the ABI Taqman 7900HT using the sequence detection system 2.22 software (Applied Biosystems). To confirm the accuracy of genotyping results, 332 (5%) randomly selected samples were regenotyped with the same method. No inconsistencies were observed. For Sp1 (intron1) an assay was set up using Primer Express Software version 2.0 (Applied Biosystems, Foster city, CA). Forward and reverse primer sequences were 5′-GTTGTCTAGGTGCTGGAGGTT-3′ and 5′-GGCGAGGGAGGAGAGAAGG-3′. The PCR mixture included 5 ng of genomic DNA in a 4 μL volume and the following reagents: FAM-CCCGCCCACATTCCCTGG-MGB probes (250 nM), TET-CCCGCCCCCATTCCCTGG-MGB probe (500 nM), primers (300 nM), 2× Taqman PCR master mix (Applied Biosystems). PCR cycling was performed in 384-well PCR plates in the ABI 9700 PCR system and consisted of initial denaturation for 15 minutes at 95°C, 40 cycles with denaturation of 15 seconds at 95°C, and annealing and extension for 60 seconds at 60°C. Results were analyzed by the ABI Taqman 7900HT using the sequence detection system 2.1 software (Applied Biosystems). To confirm the accuracy of the genotyping results, 332 randomly selected samples were genotyped for a second time with the same method. All polymorphisms had an error rate lower than 1%.
Statistical Analysis
Hardy-Weinberg equilibrium of the COLIA1 polymorphism genotypes was tested using the GENEPOP package [23]. Linkage disequilibrium (LD) between each pair of alleles at both polymorphic loci was calculated as D′ and r2 [24].
We stratified all analyses by gender, considering peak bone mass, changes in BMD, and fractures, following age- and sex-specific patterns. Baseline BMD and BMD rate of change were compared across COLIA1 polymorphisms using univariate analysis of variance (ANOVA). Corrections were made for age and BMI. Trend analysis assuming an underlying additive genetic model was done for the presence of zero, one, or two copies of the associated allele, incorporating the genotype variable as a continuous term in a general linear regression model. For the analysis of nonvertebral fracture follow-up data, we computed the incidence rates of fracture among genotypes and used Cox’s proportional hazards model, adjusting for age and BMI to estimate risk of fracture. For vertebral fractures, odd ratios with 95% confidence intervals (95% CIs) were calculated using logistic regression models since no data on the exact time of occurrence could be determined.
We used HaploStats (available at http://www.cran.r-project.org/) to estimate the frequency of inferred haplotypes and investigate the association of haplotypes with BMD and the risk of fractures. We restricted the analysis to haplotypes with an inferred frequency of more than 0.02. The first haplotype, which was most frequent, was used as reference.
Significant P values were 0.05 or lower. Finally, model assumptions were verified and model residuals checked for goodness-of-fit. SPSS 11.0 (SPSS, Chicago, IL) was used for the analyses.
Results
Allele and genotype frequencies of the -1997 G/T and Sp1 polymorphisms were in Hardy-Weinberg equilibrium (P = 0.61 and P = 0.10, respectively). General characteristics of the study population at baseline and follow-up are shown in Table 1.
Table 1General characteristics of the study population at baseline and second follow-upWomenMenBaselineSecond follow-upBaselineSecond follow-upNumbern = 3,374n = 1,724n = 2,452n = 1,287Age (years)68.3 ± 8.272.7 ± 6.867.6 ± 7.772.2 ± 6.5Height (cm)161.1 ± 6.8160.6 ± 6.4174.6 ± 6.8174.0 ± 6.7Weight (kg)69.3 ± 11.470.3 ± 12.278.2 ± 10.879.5 ± 11.3BMI (kg/m2)26.7 ± 4.127.2 ± 4.425.6 ± 3.026.3 ± 3.2FN-BMD (g/cm2)a0.83 ± 0.140.80 ± 0.130.92 ± 0.140.90 ± 0.14Lumbar spine BMD (g/cm2)1.03 ± 0.18-1.16 ± 0.20-FN-BMD changen = 1,527n = 1,143FN-BMD change (relative % of baseline year)b−0.84 ± 1.09-Values are means ± SD. Anthropometric measurement based on 5,826 individuals at baseline and 3,011individuals at follow-upaBMD measurements based on 5,737 individual at baseline and 2,670 individual at second follow-upbFemoral neck (FN) BMD change was measured between second follow-up and baseline. Second follow-up measurements were performed on average 6.5 (SD = 0.6) years after baseline
Baseline BMD by COLIA1 Genotypes
In both genders, age, height, weight, and BMI did not differ significantly between genotypes for the -1997 G/T and Sp1 polymorphisms (data not shown). Table 2 shows the BMD values according to COLIA1 genotypes in men and women. Femoral neck BMD was 3.8% lower (mean difference = 0.03 g/cm2, P = 0.09) in women who were homozygous carriers of the Sp1 T allele compared to noncarriers, with evidence for an allele dose effect (P for trend = 0.03) (Table 2). No association was found between the -1997 promoter polymorphism and lumbar spine BMD or femoral neck BMD in men or women (Table 2). We did not observe any significant association between the Sp1 and -1997 promoter polymorphisms with changes in BMD during follow-up in men or women (Table 2).
Table 2BMD measurements by COLIA1 genotypes at baseline and follow-upPromoter -1997 G/TIntron 1 Sp1 G/TGGGTTTP*GGGTTTP*Menn = 1,663n = 494n = 37n = 1,484n = 655n = 55Femoral neck (g/cm2)0.92 ± 0.130.92 ± 0.140.90 ± 0.141.000.92 ± 0.140.92 ± 0.140.90 ± 0.120.45Lumbar spine (g/cm2)1.16 ± 0.191.17 ± 0.201.15 ± 0.220.961.17 ± 0.191.16 ± 0.201.16 ± 0.200.91Numbern = 668n = 233n = 16n = 606n = 293n = 18FN-BMD change (relative % of baseline year)−0.46 ± 0.91−0.36 ± 0.93−0.38 ± 0.600.62−0.39 ± 0.93−0.56 ± 0.87−0.30 ± 0.820.01Womenn = 2,157n = 710n = 53n = 1,971n = 858n = 91Femoral neck (g/cm2)0.83 ± 0.140.84 ± 0.130.85 ± 0.130. 220.83 ± 0.130.82 ± 0.140.80 ± 0.140.09**Lumbar spine (g/cm2)1.03 ± 0.181.04 ± 0.181.01 ± 0.180.691.04 ± 0.181.04 ± 0.191.01 ± 0.200.52Numbern = 820n = 298n = 24n = 773n = 340n = 29FN-BMD change (relative % of baseline year)−0.84 ± 1.11−0.83 ± 1.01−0.78 ± 1.030.85−0.85 ± 1.08−0.80 ± 1.04−0.74 ± 1.490.55Values are expressed as mean ± SD. Adjustments for age and BMI. Femoral neck (FN) BMD change was measured between second follow-up and baseline*P for ANOVA. **For trend linear regression: P = 0.03
Risk of Fracture by COLIA1 Genotypes
The relation between risk of fracture and COLIA1 Sp1 polymorphism is shown in Table 3. Women with two copies of the T allele of the Sp1 polymorphism had a 2.3 times higher risk of fragility fracture (95% CI 1.4-3.9, P = 0.001). Adjustment for femoral neck BMD did not essentially modify the association. A similar association was observed in men who were homozygous carriers of the Sp1 T allele, which was borderline significant (risk ratio = 2.3, 95% CI 0.9–5.8, P = 0.07). For the -1997 promoter polymorphism no association was found with any type of fracture in either men or women (Table 3).
Table 3Risk of fractures by COLIA1 genotypesCOLIA1 genotypesTypes of fractureEvent (%)Risk ratio (95% CI)GGGTTTGG (reference)GT vs. referenceTT vs. referenceMenPromoter -1997 G/TNonvertebral147/1952 (7.5)49/592 (8.3)1/46 (2.2)1.0 (reference)3.18 (0.45–22.74)3.44 (0.47–29.94)Fragility72/1952 (3.7)14/592 (2.4)1/46 (1.8)1.0 (reference)1.46 (0.20–10.55)0.94 (0.12.7.14)Vertebral100/1056 (9.5)35/340 (10.3)3/28 (10.7)1.0 (reference)1.07 (0.71–1.61)1.23 (0.36–4.18)Intron 1 Sp1 G/TNonvertebral91/1254 (7.3)39/574 (6.8)4/44 (9.1)1.0 (reference)0.97 (0.71–1.33)1.40 (0.65–3.00)Fragility36/1254 (2.9)14/574 (2.4)2/44 (4.5)1.0 (reference)0.94 (0.58–1.51)2.34 (0.94–5.85)Vertebral68/719 (9.5)31/350 (8.9)2/22 (9.1)1.0 (reference)0.83 (0.55–1.24)1.59 (0.60–4.22)WomenPromoter -1997 G/TNonvertebral528/2721 (19.4)182/879 (20.7)8/63 (12.7)1.0 (reference)1.53 (0.76–3.09)1.66 (0.82–3.36)Fragility216/2721 (7.9)73/879 (8.3)4/63 (6.3)1.0 (reference)1.13 (0.42–3.05)1.19 (0.44–3.26)Vertebral159/1320 (12.0)52/445 (11.7)5/35 (14.3)1.0 (reference)0.98 (0.70–1.37)1.31 (0.49–3.45)Intron 1 Sp1 G/TNonvertebral279/1626 (17.2)121/710 (17.0)17/76 (22.4)1.0 (reference)1.05 (0.90–1.23)1.34 (0.92–1.23)Fragility98/1626 (6.0)42/710 (5.9)10/76 (13.2)1.0 (reference)1.01 (0.78–1.31)2.33 (1.39–3.87) Vertebral98/891 (11.0)51/401 (12.7)3/34 (8.8)1.0 (reference)1.21 (0.88–1.65)1.37 (0.56–3.35)Adjustments for age and BMI
Haplotype Analysis
High LD exists between the -1997 G/T and Sp1 polymorphisms, as assessed by D′ measure (D′ = 0.99, r2 = 0.03). We observed in our population three -1997 G/T, Sp1 G/T haplotype alleles (Fig. 1): haplotype 1 (Gpromoter–GIntron), 69.0%; haplotype 2 (Gpromoter–TIntron), 17.6%; and haplotype 3 (Tpromoter–GIntron), 13.4%. Haplotype 4 (Tpromoter–TIntron) was not present (Fig. 1). The strong LD between the -1997 G/T and Sp1 G/T polymorphism and the virtual nonexistence of haplotype 4 suggests there is absence of ancestral recombination in the region. We observed a borderline significant association (P = 0.06) between haplotype 2 (Gpromoter–TIntron) and femoral neck BMD in women. Women who carried haplotype 2 (Gpromoter–TIntron) had lower (−0.01 mg/cm2) femoral neck BMD compared with those who carried haplotype 1 (Gpromoter–GIntron).
Fig. 1Schematic representation of the COLIA1 gene with the structural-1997 G/T polymorphism in the promoter region and G/T Sp1 polymorphism at binding site, with observed haplotype frequencies in the Rotterdam Study
The relation between risk of fracture and COLIA1 haplotypes is shown in Table 4. Women with haplotype 2 (Gpromoter–TIntron) had a 2.1 times higher relative risk of fragility fracture (P = 0.03); in men the increase in risk was 2.0 (P = 0.31). These results were essentially unchanged after adjustment for femoral neck BMD. For haplotype 3 (Tpromoter–GIntron) we found no association with any type of fracture in either men or women.
Table 4Risk of fracture by COLIA1 haplotypesTypes of fractureMen, OR (95% CI)Women, OR (95% CI)Haplotype 1Haplotype 2Haplotype 3Haplotype 1Haplotype 2Haplotype 3Nonvertebral1 (reference)1.29 (0.55–3.02)0.00 (0.00-∞)1 (reference)1.31 (0.83–2.07)0.61 (0.29–1.29)Fragility1 (reference)2.01 (0.71–5.67)0.00 (0.00-∞)1 (reference)2.12 (1.23–3.66)0.80 (0.29–2.23)Vertebral1 (reference)1.64 (0.62–4.31)1.19 (0.35–4.00)1 (reference)1.23 (0.51–2.95)1.31 (0.50–3.42)Adjustments for age and BMI. Haplotype 1, (Gpromoter–GIntron); haplotype 2, (Gpromoter–TIntron); haplotype 3, (Tpromoter–GIntron)
Discussion
In this large population-based study, we found that the Sp1 polymorphism influences the risk of fragility fracture in elderly women, with a similar yet not significant effect in men. Similarly, women homozygous for the T allele had 3.8% lower BMD at baseline. The -1997 G/T polymorphism showed no independent effect on fracture risk or BMD levels in both genders. The haplotype analysis showed an association with BMD and fracture in women, which appeared to be driven by the effect of the Sp1 polymorphism.
A study in Spain [19] showed that the -1997 G/T polymorphism located in the promoter region of the COLIA1 gene was associated with BMD in postmenopausal women of Spanish origin. In addition, analysis of compound genotypes of the three studied polymorphisms (-1997 G/T, Sp1, and -1663 indelT) suggested that the lowest value for BMD corresponded to GG homozygous at -1997 and heterozygous at the other two loci. Furthermore, in another report, the same group observed a possible functional mechanism for the -1997 G/T polymorphism [25]. Our population-based study suggests there is no independent effect of the -1997 polymorphism on BMD and the risk of fractures.
Recently, Stewart et al. [26] examined the three polymorphisms of the COLIA1 gene in forms of haplotypes in postmenopausal women. In contrast with our study, they observed an association between reduced BMD values and the promoter –1997G/T polymorphism. Since the promoter -1997G/T polymorphism is in strong LD with the Sp1 polymorphism, the observed association of haplotype 2 is driven by the SP1 polymorphism.
We showed that the association between fragility fractures and Sp1 polymorphism is significant only in women. We also found that the association between fragility fracture and the Sp1 polymorphism was independent of femoral neck BMD. A possible explanation for an increased risk of fracture is the different number of fractures between men and women. There are a higher number of fractures in women compared to men (13.2% in women and 4.5% in men). This suggests that other underlying biological mechanisms beyond BMD levels, such as the role of microarchitecture and composition of mineral crystals in bone tissue, might explain the increased fracture risk [11, 27, 28]. Biomechanical testing of bone samples from heterozygous individuals with the GT genotype showed reduced bone strength compared to the homozygous GG genotype and a slight reduction in mineralization of bone [11]. Presence of the T allele in the COLIA1 Sp1 binding site leads to an abnormal relative level of COLIA1/COLIA2, which may reduce bone quality and quantity [11]. Accordingly, we assume that a weaker network of abnormal collagen cross-linking may generate a three-dimensional unstable condition that may be responsible for its relatively greater risk of fragility fracture in elderly women homozygous for the Sp1 T allele. Similarly, it is likely that the Sp1 polymorphism drives these associations since evidence of functionality of this polymorphism has been reported previously.
In the Genetic Markers for Osteoporosis (GENOMOS) Study, which is the largest study examining the Sp1 polymorphism (n = 20,786) in relation to osteoporosis, an association between the Sp1 polymorphism and a 1.3 times incident risk of vertebral fractures was also observed. An effect of the -1997 promoter polymorphism due to power limitations cannot be fully excluded and should be subject to study in a larger population like that of GENOMOS.
Our present study has some limitations. Survival bias may play a role if individuals who were lost to follow-up were associated to genotype. Considering this selection bias, a possible relationship of the COLIA1 polymorphisms with changes in BMD cannot be fully excluded.
In conclusion, we observed an increased risk of fragility fractures in women carriers of the COLIA1 Sp1 T allele. In contrast, the -1997 G/T polymorphism by itself appears to have no influence on fracture or BMD in postmenopausal women, though the role of power limitations cannot be excluded.

colia1
bone
bone mineral density
fracture
osteoporosis


Consensus structural models for the amino terminal domain of the retrovirus restriction gene Fv1 and the Murine Leukaemia Virus capsid proteins

Background The mouse Fv1 (friend virus) susceptibility gene inhibits the development of the murine leukaemia virus (MLV) by interacting with its capsid (CA) protein. As no structures are available for these proteins we have constructed molecular models based on distant sequence similarity to other retroviral capsid proteins.
Background
The Fv1 gene is one of a series of mouse genes that control the susceptibility of mice to murine leukaemia virus (MLV) [1-3]. The gene acts in the cell to restrict virus replication [4] through a mechanism that is still uncertain. Genetic studies suggest that the target for the Fv1 gene product is the capsid protein (CA) of MLV [5,6] and it is thought to interact with CA after entry of the virus into the cell but before integration and formation of the provirus.
When cloned and sequenced [7], the Fv1 gene was found to have sequence similarity to endogenous retroviruses of the HERV-L and MuERV-L families [7,8]. Based on its position within the Gag gene of these endogenous elements, it appears that Fv1 encodes a capsid-like protein. This structural assignment of the Fv1 gene is consistent with its function as it can be postulated that the gene product might act as a dominant negative mutation and interfere with the MLV capsid function [9]. Sequence alignments have been made between Fv1 and other retroviral capsid proteins [8] but besides one region of clear similarity, called the Major Homology Region (MHR), there is otherwise little that is conserved across the full family of retroviral (and related lenti-virus) CA sequences.
There are now several known structures for retroviral capsid proteins in the Protein Databank (PDB). While some of these are only fragmentary, a selection can be extracted that gives a reasonable phylogenetic spread across the retroviruses, with examples from three out of six genera of orthoretroviruses. In all the known structures, the CA protein has an all-α type structure consisting of two domains: a larger N-terminal and smaller C-terminal domain with a short extended linker-region between them. As this linker enters the C-terminal domain it incorporates the MHR. There is considerable variation in the orientation of the domains and in the conformation of the loop-regions between α-helices, particularly in the N-terminal domain.
In this work, we have exploited these multiple structures to construct consensus molecular models using threading methods both for the Fv1 gene product (FV1) and its target protein, the MVL CA. As threading takes known and predicted structure into account, it should provide better alignments for the regions that lie outside the MHR. However, as these methods are still far from perfect, we have constructed a model based on each known structure and the degree to which these agree has been used to assess the confidence of different parts of the model. As the threading method we have used has some 'free' parameters (such as the gap-penalty) we have introduced a novel modelling strategy in which the parameters are varied to give maximum agreement among the resulting models.
Results and Discussion
The sequence alignments compiled on the proteins of known structure using the Ψ-BLAST/QUEST search strategy (Methods Sectn.) were used in the modelling of both the MLV CA and FV1 sequences. These varied from 4 to 7 sequences (Table 1). Not unexpectedly, the alignments are to some extent similar, in particular each contains the sequence of the HIV-1 CA structure [1e6jP]. (QUEST is biased to retain sequences of known structure). Greatest overlap exists between the sequence sets of the two human viruses, with the HTLV-I sequences being a subset of the HIV-1 sequences (Table 0(d) and Table 0(c)). While it would be possible to extend and realign these sub-families based on structure comparisons, they were left unaltered so as to be equivalent to the MLV and FV1 alignments described below. This allows control modelling tests to be directly comparable to those performed for sequences of unknown structure.
Table 1
Template sequences selected for alignment. The Ψ-BLAST/QUEST search strategy (Methods Sectn.) when started with the probe sequence of the PDB structure indicated by "SEED" selected the sequences indicated in each subtable: (a) EIAV [1eia], (b) RSV [1d1dA], (c) HIV-1 [1e6jP] and (d) HTLV-I [1qrjA]. The sequences are identified by their gene identification (gi) number (first column) and their local source databank identifier. The sequence fragment (automatically extracted by QUEST) is given as a range of residue numbers.
(a) EIAV
(b) RSV
SEED
1eia
12084543
pdb-1E6J
0–210
6358699
gb-AAF07324
131–342
8072301
gb-AAF71968
0–155
6815746
gb-AAF28696
0–173
6649692
gb-AAF21520
5–224
12084543
pdb-1E6J
3–210
120850
sp-P18041
97–362
27803398
gb-AAO21890
120–281
294961
gb-AAA74706
116–381
5106563
gb-AAD39752
81–346
SEED
1d1dA
(c) HIV-1
(d) HTLV-I
6358699
gb-AAF07324
129–340
SEED
1qrjA
22037894
gb-AAM90230
148–359
12084543
pdb-1E6J
0–210
SEED
1e6jP
22037894
gb-AAM90230
144–370
532325
gb-AAA99545
50–224
9886907
gb-AAG01643
0–222
9886907
gb-AAG01643
0–211
MLV Modelling
The databank search using the MLV sequence as a probe provided a useful collection of six sequences (Table 1(a)) which, with the MLV probe sequence itself, were passed through the PsiPred secondary structure prediction protocol (Methods Sectn.). When the predicted secondary structures were viewed on the aligned sequences, several distinct α-helices were apparent, consistent with the protein having an all-α type structure similar to the other capsid proteins of known structure (Figure 1).
The MLV alignment with predicted secondary structure was matched in the MST program with the four capsid proteins of known structure along with their associated aligned sequences. This was done for each parameter combination specified in Methods Sectn. and the quality of the agreement among the four resulting models quantified using both the Fu and Fw measures defined in Methods Sectn. There was reasonable overall agreement between the two measures of the parameter combinations (Figure 2) and with otherwise no basis on which to prefer one measure over the other, the combined measure (Fv) was used to select the parameters on which the final models were calculated. These were S = 7, G = 30.
Figure 1
MLV sequence alignment with the sequence of 1qrjA and related sequences. The alignment is displayed twice in different colouring schemes. In the top two panels, (a) the amino half is coloured firstly by predicted secondary structure (red = α, green = β) and then below using a different colour for each amino acid type (Taylor, 1997; black = gap). The known helices (N1–N5) marked as red lines (minor helices are orange) between the two blocks. In the lower two panels (b) the carboxy half of the alignment is shown in a similar way. The sequence identifiers are given below the alignment in the same order as they are aligned. The mid-line divides the 1qrjA sub-family from the MLV sub-family.
Figure 2
MLV model agreement. The degree of similarity among the four MLV CA models is plotted for different parameter combinations of MST. Combinations resulting in better agreement have colours towards the red end of the spectrum. (a) Using the unweighted RMSd measure (Fu) and (b) Using the weighted RMSd measure (Fw). (See Methods Sectn. for details.) (c) Combined score (Fu·Fu). The MST parameters varied were X-axis: S = 0→9 (in steps ofone) and Y-axis: G = 10→90 (in steps of 10). (See Methods Sectn. for details.) The best combined score is at: S = 7, G = 30.
N-terminal domain
The multiple structure alignment of the four models showed good agreement. In the N-terminal domain there were two extensive regions over which all models were aligned in register, covering helices N2-N3 and N5 (including the preceding short helix in the loop region). Two alternate registers were observed for helices N1 and N4, with relative shifts of 3 and 4 residues, respectively. The models based on 1qrjA (HTLV-I) and 1d1dA (RSV) were in complete agreement and the summed Fw score indicated that 1d1dA provided the best consensus model.
Given that the alignment of the capsid protein sequences is ambiguous, the superposition of the models on the structures from which they were derived provides a better way to assess whether there is any significant sequence similarity that could be used as a basis on which any one model might be preferred over the others. The PSId values were: 5.6, 18.5, 10.5, 13.0 for 1d1dA, 1qrjA, 1eia and 1e6jP, respectively. (No differences were observed whether using the standard version or the sequence-biased version of SAP).
Although the 1d1dA model was the best consensus representative, it has only 5.6% sequence similarity with MLV and the higher sequence similarity of the 1qrjA based model with its template (18.5%) was considered sufficient to justify its adoption as the preferred model over the 1d1dA model. Structurally, both were very similar (1.4 Å wRMSd) with the only significant structural difference being a slight reorientation of the helical region in the loop preceeding the final helix. This structure for the 1qrjA based model is shown along with its comparison with the 1d1dA model in Figure 3.
Figure 3
Consensus model for the MLV N-domain. (a) The model for the MLV CA N-domain based on 1qrjA shown as a α-carbon trace with predicted α-helices coloured red (some fragments of predicted β-structure are coloured green). The molecule is in approximately the same orientation as in Figure 8(a) and residues identified by Stevens et al. (2003) are marked as small spheres. (b) The models based on 1qrjA and 1d1dA are shown superposed and coloured from blue (amino) to red (carboxy). Feint dashed lines connect identical residues. The wRMSd = 1.4 (uRMSd = 5.8) over 123 residues.
It can be seen from Figure 3(a) that there is good location of the predicted and model helices with deviations occurring only at the ends of some helices and into the loop regions at the 'top' of the molecule. The ends of helices N4 and N5 and the loops at this end of the molecule also encompass the mutations identified as affecting the sensitivity of the virus to Fv1 [29].
C-terminal domain
With its relatively unambiguous MHR, all the models of the C-terminal domain were in complete agreement over the first half of the domain. The more C-terminal half, however, was less consistent due to a combination of its generally less conserved nature combined with uncertainty in the location of the terminus in some of the sequences.
As the C-terminal domain has been shown to be less important in determining the property of virus susceptibility, further effort was not expended to try and refine the alignment at the carboxy terminus of the molecule, especially in the more difficult alignment of the FV1 sequence described below.
FV1 Modelling
The databank search using the FV1 sequence as a probe provided a less useful collection of only two distinct sequences (Table 1(b)). Although other sequences were found these were rejected by QUEST as being too similar to those retained.
The FV1 alignment with predicted secondary structure was matched to the four capsid proteins of known structure as described above for each MST parameter combination and agreement among the four models monitored using the F scores (Methods Sectn.). These were 50.3 and 131.874 for Fu and Fw while the corresponding score obtained for the MLV models were 78.7 and 188.0, respectively. The greater variation among the FV1 models was undoubtedly due to uncertainty in the placement of the large 'unstructured' region. Nevertheless, there was reasonable overall agreement between the two measures and both found maximal model agreement when S = 9, G = 20 (Figure 4).
Figure 4
FV1 model agreement. The degree of similarity among the four FV1 CA models is plotted for different parameter combinations of MST. (a) Using the unweighted RMSd measure (Fu) and (b) Using the weighted RMSd measure (Fw). (See Methods Sectn. for details.) (c) Combined score (Fu·Fu). The MST parameters varied were X-axis: S = 0→9 (in steps of one) and Y-axis: G = 10→90 (in steps of 10). (See Methods Sectn. for details.) The best combined score is at: S = 9, G = 20.
When the predicted secondary structures were viewed on the aligned sequences, although several distinct α-helices were apparent, these had a less direct correspondence with those expected for a capsid protein (Figure 5). In particular, there is little predicted α-structure for helix N4 and the alignment around N5 is ambiguous. If the secondary structure prediction were to be believed, this might indicate the introduction of a large insertion, or given that there were less sequences on which to base the predictions, it is possible that the predictions are not as accurate as those obtained for the MLV sequences.
Figure 5
FV1 sequence alignment with the sequence of 1e6jP and related sequences. The alignment is displayed twice in different colouring schemes. In the top two panels, the amino half is coloured by predicted secondary structure (red = α, green = β) and amino acid type (Taylor, 1997). See the legend to Figure 2 for further details. with the known helices (N1–N5) marked as red lines between (minor helices are orange). The carboxy half of the alignment is displayed in a similar way in the lower panels. The sequence identifiers are given below the alignment in the same order as they are aligned. The mid-line divides the 1e6jP sub-family from the FV1 sub-family.
N-terminal domain
The multiple structure alignment of the four models showed unexpectedly good agreement. As in the MLV models, two regions were aligned in register over all the models. These included most of helix N4 and the following loop region then, after only one shift, the full register was regained for helix N5. The sum-of-F scores for each model indicated that 1eia provided the best consensus, followed by 1e6jP (to which it was most similar). The PSId values were: 9.3, 7.8, 6.6, 15.9 for 1d1dA, 1qrjA, 1eia and 1e6jP, respectively. As the 1e6jP model aligns better on its target by almost 10 PSId, this was considered sufficient to justify its adoption as the preferred model over the 1eia based model. Both models are similar in the core region (1.3 Å wRMSd) but with the significant differences in the extensive loop regions at the 'top' of the molecules. This structure for the 1e6jA based model is shown along with its comparison with the 1eia model in Figure 6.
Figure 6
Consensus model for the FV1 N-terminal domain. (a) The model for the FV1 N-domain based on 1e6jP shown as a α-carbon trace with predicted α-helices coloured red (some fragments of predicted β-structure are coloured green). The molecule is in approximately the same orientation as in Figure 8(a). (b) The models based on 1e6jP and 1eia are shown superposed and coloured from blue (amino) to red (carboxy). Feint dashed lines connect identical residues. The wRMSd = 1.3 (uRMSd = 5.4) over 124 residues. Note: the modelling program rescales the secondary structure prediction values so that there is the same proportion of predicted structure as measured secondary structure on the model.
It can be seen from Figure 5 and Figure 6(a) that there is reasonable location of the predicted and model helices except for helix N4 which is almost completely unpredicted. Otherwise deviations occurred at the ends of helices and in the loop regions at the 'top' of the molecule.
Table 2
Target sequences selected for alignment. The Ψ-BLAST/QUEST search strategy (Methods Sectn.) when started with the two target sequences indicated by "SEED", selected the sequences indicated in each subtable: (a) MVL and (b) FV1. The sequences are identified as in Table 1. (* the C-terminal domain of this sequence appears to be replaced by an oncogene.)
(a) MLV
SEED
AAD55051
215–432
gi-120892
sp-P03330
207–423
gi-2393894
gb-AAC58239
206–434
gi-419481
pir-A46312
199–423
gi-323873
gb-AAA43041
203–418*
gi-7548235
gb-AAA4306
206–422
gi-5726238
gb-AAD48375
156–352
(b) FV1
gi-7521942
T29096
gi-23485357
gb-EAA20381.1
gi-3913713
sp-P70213
FV1 MOUSE
Control Models
To help assess the accuracy of the models constructed above, the protocols described in the Methods sections were applied to each of the sequences of known structure. For each protein, this results in four models based on the other structures (and its own structure). Each of these models, was then compared to the known structure using the modified SAP program and the RMSd/PSId measures reported in Table 3.
Table 3
Control model similarity. The similarity of the capsid protein domains are tabulated as: uRMSd/PSId, both as calculated by the SAP program. The proteins are identified as in Table 4. The column "params" gives the MST parameter values S, G at which the four models in each row had maximum agreement as measured by the combined agreement score Fv (Methods Sect n.).
str \ seq
D1D
QRJ
E6J
EIA
params
D1D
0.60/78.9
4.14/90.0
2.05/56.7
2.41/38.3
9,10
QRJ
5.58/100.
0.32/100.
3.58/88.4
5.51/100.
3,10
E6J
1.88/95.2
1.70/84.7
0.31/100.
1.39/100.
7,10
EIA
2.00/93.3
2.00/100.
1.28/100.
0.35/100.
8,20
With few exceptions, the wRMSd/PSId values indicated generally good models and by visual inspection, all had the correct fold. The models based on their own structures were almost identical to their templates with the exception of 1d1dA. When the uWRMSd values of the models against the known structure are plotted against number of residues ranked by local score (Figure 7) it can be seen that for 1d1dA the error rises sharply after 85 positions. Visual inspection revealed that this was due to a misaligned segment in the large loop region. The majority of models exhibited a gradual increase in wRMSd with increased error mounting as the loop regions were introduced. Two models that ran markedly below this trend were those based on 1eia and 1e6jP (22% identity), the two most similar proteins. A few models were also markedly worse. Two of these were derived from the 1qrj alignment based models which had a distinctly lower weight on the structure component (S weight) in the MST alignment.
Figure 7
Control model uRMSd values. Each model was compared with its known structure and the local residue matches calculated by SAP were ranked. The uRMSd was calculated for increasingly larger sets of ranked residues and plotted against set size. This means that the best fitted residues in each comparison are found to the left of the plot with increasing divergence towards the right. Four models are plotted for each of four CA proteins of known structure: 1d1dA = red, 1qrjA = green, 1eia = purple, 1e6jP = blue. The best models are those of sequences built on their own structure. Three of these remain low throughout their length while one has poor loops and rises towards the right. Above these are two models for the most similar sequences of 1e6jP and 1eia built on each others structure (22% identity). Most models lie in the mid range with a few accumulating early errors due to shifts in the core regions.
Conclusions
Based on relative degrees of sequence similarity among the control models (Table 3(b)) and the MLV and FV1 sequences (MLV:1qrj = 18.5%, FV1:1e6jP = 15.9%), it would be expected that the models constructed for the MLV sequence would fall in the mid-range of the spread in quality observed in Figure 7 – typically, a good core model (3 Å RMS over 80 residues) with increasing divergence in the more variable loop regions. This is where the majority of the control models lie which were all constructed from sequence similarities that are generally lower than either of the above relationships used to model the MLV or FV1 sequences.
While a similar confidence might be hoped for the FV1 model, given its overall lower sequence similarity to the proteins of known structure and the less consistent nature of its secondary structure prediction, it is more likely that it will be of lesser quality – corresponding more to the poorer models constructed among the control proteins (Figure 7). Typically, this would include shifts in the core helices (giving the characteristic immediate rise in the traces in Figure 7) with further shifts in the loop regions. Despite this, as with all of the control models, it is likely that the core fold of the protein should remain unaltered.
This study has shown that reasonable models can be constructed for both the FV1 and its target MLV protein based on other retroviral capsid proteins. Although this result was suggested by the existence of the MHR in both sequences, the fluid nature of retroviral genomes does not necessarily constrain the preceding domain to remain constant in structure or even remain at all. Despite only weak sequence similarities in this region, the addition of multiple sequences with predicted secondary structure has allowed plausible models to be constructed.
These models can now be used in the interpretation of experimental studies on the mode of action of retroviral susceptibility. As will be reported in more detail elsewhere [29], a series of amino acids in CA affecting the CA – FV1 interaction have been identified in the loops at the 'top' of the N-terminal domain (Figure 3). Based on the model, they suggest a potential FV1 binding domain in the MLV CA. Experiments are currently under way to test this prediction by crystallographic studies.
For many years, the Fv1 gene has been the only known intracellular non-immune natural defense against a retroviral infection. Recently, two additional genes, Ref1 and Lv1, with antiviral activity have been described in human cells [30,31]. Phenotypically, they resemble Fv1 [32] but the genes themselves remain to be characterised. Understanding the mechanism of Fv1 action will provide insights into how natural defences to retroviral infection might be deployed against HIV.
Methods and Data
Data
Sequence Data
All sequences were extracted from (and searches were made over) the non-redundant protein sequence databank (NRDB) at the National Centre for Biotechnology Information (NCBI) as it was found on 28th of January 2003.
The sequence of the MLV used was the gag protein AAD55051 (GI:5881091) [10] and a region was extracted from residues 215–432, corresponding to the CA protein.
The sequence of the Fv1 gene [7] was taken from FV1_MOUSE (GI:3913713). The region corresponding to the CA was identified as residues 100/120–340/360 where the inner numbers represent the probable core of the protein. This range corresponds to the region of highest similarity to the MuERV-L sequence [8]. The leading 100 residues of the polyprotein may correspond with a relic matrix protein. Perhaps because of this, there is no obvious protease cleavage motif [11] to give any indication of the true terminus. However, in other situations this has not always been an accurate guide [12].
Structural Data
The structures of capsid proteins were extracted from the PDB [13] with the aid of the FSSP structure comparison database [14]. Of the six structures in the FSSP alignment, only four extended over the full length of the two structural domains. There were as follows, with their PDB code (and chain delimiter, if any) shown in brackets: Rous Sarcoma Virus (RSV) [1d1dA] [15], Human T-cell Leukemia Virus (HTLV-I) [1qrjA] [16,17], Equine Infectious Anemia Virus (EIAV) p26 [1eia] [18] and Human Immunodeficiency Virus (HIV-I) p24 [1e6jP] [19].
The common core of the N-terminal domains of these proteins (in the numbering of the PDB structure) was defined as: 1d1dA 15–148, 1qrjA 16–129, 1eia 16–145 and 1e6jP 16–146. These fragments will be distinguished below as: 1d1dAn, 1qrjAn, 1eia-n and 1e6jPn and each terminates 8 or 9 residues before the conserved glutamine of the MHR. The N-terminal domain can be described as having five α-helices (N1...N5) with a long 'disordered', partly helical, loop between helices N4 and N5. For ease of reference below, this region will be called the 'top' of the molecule and its representation in the Figures will preserve this orientation.
The C-terminal domains were defined as: 1d1dA 152–224, 1qrjA 132–204, 1eia 149–220, 1e6jP 149–220 and were distinguished by the suffix "c". The common core of this domain consists of an extended strand leading into the MHR region followed by four helices designated C1...C4.
Despite their different sizes, both the N and the C domains have the same fold, perhaps suggesting an ancient gene duplication. This is most obvious in the HIV structure [1e6jP] where the domains can be superposed with 4.6 (2.0) unweighted (weighted) RMSd over 68 residues.
The similarity of sequence and structure over these domains was calculated using the SAP structure comparison program [20] for each pair of like domains (Table 4). The N-terminal domains have clearly related structures (3–6 Å RMSd) but have no significant sequence similarity (over 20%) except for the EIAV and HIV-1 structures. The smaller C-terminal domain (which contains the MHR) has greater overall sequence and structural similarity compared to the N-terminal domain (mostly over 20% with 2–3 Å RMSd) and over 40% for the EIAV/HIV-1 pair.
Table 4
Capsid protein similarity. The similarity of the capsid protein domains are tabulated as: (a) RMSd (unweighted) and (b) PSId, both as calculated by the SAP program. Upper-right triangle: over the common core of the C-terminal domain and Lower-left triangle: over the common core of the N-terminal domain. The proteins are identified as: D1D = RSV [1d1dA], QRJ = HTLV-I [1qrjA], E6J = HIV-I [1e6jP], EIA = EIAV [1eia ].
(a) structure
(b) sequence
N \ C
D1D
QRJ
E6J
EIA
N \ C
D1D
QRJ
E6J
EIA
D1D
2.88
2.64
2.46
D1D
21.9
22.2
14.7
QRJ
3.83
2.11
2.39
QRJ
12.5
30.6
26.4
E6J
6.21
4.76
1.40
E6J
8.6
10.8
41.7
EIA
5.75
4.31
3.22
EIA
11.6
12.5
22.0
Sequence Databank Searches
Initially, each probe sequence was compared against the sequence databank using the Ψ-BLAST program [21] with a significance level set at 0.001 and 5 cycles of iteration. When the probe is a retroviral sequence, the number of hits found by Ψ-BLAST can be large (typically over 1000). These were reduced to manageable numbers by the use of the search program QUEST which is similar to Ψ-BLAST but incorporates a multiple sequence alignment stage in its iterations to exclude redundant sequences as well as excluding poorly related or incomplete sequences [22]. The alignments produced by QUEST typically contain between 6–12 sequences (including the probe sequences), none of which have more than 60% sequence identity (PSId) with each other.
The sequences retained by QUEST are selected on the basis of associated biological information, with those including useful annotation and structural data being given preference over those with no annotation or keywords such as "hypothetical". The filters are part of the MULTAL sequence alignment program [23] which are fully described in Ref. [24].
Secondary Structure Prediction
The multiple alignments resulting from the Ψ-BLAST/QUEST search protocol were passed to the program PsiPred , Version 2.3) [25]. This program normally performs its own databank searches using Ψ-BLAST to build-up an alignment. Given the problems described above that arise when searching with retroviral sequences, the PsiPred program was used locally to search only a database consisting of the sequences that had already been selected by QUEST.
Each sequence in the alignment was taken in turn and used as a probe against this small local database. As the Ψ-BLAST parameters used by PsiPred were more restrictive than those used in the full search (only 3 cycles) and there are fewer sequences in the databank, each sequence may only find those to which it is more closely related. This introduces some variation into the predictions which provides a useful indication of the confidence of each predicted secondary structure element (SSE).
Multiple Sequence Threading
The alignment gathered on the probe sequence was then aligned with a protein structure using the multiple sequence threading MST program [26]. This program uses multiple sequence and structural information to automatically construct an α-carbon molecular model for the probe sequence with some limited remodelling in regions of insertion and deletion.
Template Sequence Alignments
The MST program can incorporate multiple aligned sequences along with both the probe sequence and the template structure. The latter were gathered in an identical manner to the probe sequence using the Ψ-BLAST/QUEST search protocol described above. Each search against the NRDB was started with the sequence of the protein of known structure and the resulting multiple alignments examined 'by-eye' in the light of the known secondary structures. If any large insert had been made in a secondary structure element (SSE) then it was assessed whether the gap could be shifted outside the SSE without significant loss of residue matches. Similarly, if a large insert (more than 6 residues) was made by any sequence other than the probe sequence (of known structure) then the insert was reduced to six residues by removing the positions with most gaps.
Parameter Choice
The MST program has parameters that allow different weights to be attached to the matching contribution of the sequences, their secondary structures, residue exposure and the residue packing in the resulting model. There is also a gap-penalty. The best values for these weights depends on the number and degree of relatedness among both the probe and the template sequences [26]. Rather than vary all these parameters individually, the weights on the structural components (secondary structure, exposure and packing) were 'ganged' together into a single parameter reflecting the contribution of structural terms relative to the sequence matching component. This gave two parameters: S (for structure) and G (the gap-penalty). Previously, the structural parameters had all been scaled into the same range so a value of S = 3 corresponds to a value of 3 for each individual weight. Although the gap-penalty is correlated with S, it cannot be linked in the same way without the risk of missing good alignments.
In the current application, there was more than one available template structure and advantage was taken of this by constructing models based on all available templates and choosing the MST parameters such that the agreement among the models was greatest. The parameters were varied over the ranges: S = 0→9 (in steps of one) and G = 10→90 (in steps of 10).
Measuring Model Agreement
Whatever the parameters for MST, all the models constructed from the same probe have an identical sequence. These might therefore be compared using the α-carbon RMSd based on a one-to-one (100 PSId) sequence equivalence. However, using this simple measure, a 'trivial' shift in space in which, say, an α-helix shifts by one turn relative to another α-helix might result in a large RMSd between what are, topologically, similar models. It is better to allow a local relative shift in sequence of four residues to restore the spatial equivalence at the expense of residue identity.
To implement this trade-off between RMSd and PSId, the models constructed for each parameter combination were compared against each other using the program SAP [20]. This program calculates both a weighted (Rw) and unweghted (Ru) RMSd for the two structures being compared and reports the percentage sequence identity of the alignment. The weighted RMSd down-weights regions of weak similarity which are mainly loop regions that can have large relative displacements. Despite its origins [27], in its current implementation the SAP program  does not include a sequence matching component and this was restored (for sequence identity only) by doubling the local residue pair score for identical residue types and otherwise halving all other residue match scores in the initial score matrix.
A score reflecting match quality (f) was calculated as: f = M/(1+R), where M is the PSId measured over the positions aligned by SAP and R is one of the RMSd measures. Identical structures would score 100. For a set of N models, a sum was calculated over the (N2 - N)/2 pair combinations giving an overall measure of agreement (F) among the set. For a set of four models that align perfectly (100 PSId) with 2 Å RMSd, the overall score obtained would be 200. This score was calculated for both the wRMSd and uRMSd values (giving Fw and Fu, respectively) and a combined score (Fv) as the product of Fw and Fu.
While this procedure provides a general method for choosing parameter values, in the current application to a multi-domain protein it was not meaningful to calculate the RMSd over the full atomic model (because of relative domain movements). Instead, the agreement was calculated over the more distantly related N-terminal domain.
Selecting a consensus model
Although any model in the set could be taken as a representative, it is best to try and select one that, by some criteria, can be considered to be the most representative. To do this, we compared each pair of models usuing the structure comparison protocol described in the previous section. This provides a pairwise alignment based on structure, and even though each model has an identical sequence, the structural alignment may not match-up identical residues. The pairwise alignment were then combined into a multiple structure alignment [28] and as the models all have an identical sequence, their relative shifts can be seen easily. Rather than use a pure structure or sequence based measure of similarity between the proteins, the score F was devised in the previous section (Methods Sectn.) that combines both a sequence and a structural component. This was used to find the model with the greatest sum-of-scores to the others.
An alternative selection test was also considered of selecting the model that had greatest sequence similarity when superposed with the template structure from which it was derived. As most of the sequence similarities considered below lie in the 'twilight-zone', the latter option was only used when one model was clearly better than the others. For this, we choose the criterion that it had to be 10 PSId points clear of its 'rivals'.
Abbreviations
Fv1/FV1, gene/gene-product of Friend Virus susceptibility locus-1;
MLV, Murine Leukaemia Virus;
CA, CApsid protein;
HERV-L, Human Endogenous RetroVirus (L family);
MuERV-L, Murine Endogenous RetroVirus (L family);
MHR, Major Homology Region;
NCBI, National Centre for Biotechnology Information;
NRDB, Non-Redundant DataBank;
MST, Multiple Sequence Threading (program);
SSE, Secondary Structure Element;
PSId, Percent Sequence Identity;
PDB, Protein DataBank;
RMSd, Root-Mean Square deviation;
wRMSd, weighted Root-Mean Square deviation;
uRMSd, unweighted Root-Mean Square deviation;
Figure 8
Capsid protein domains. The structures of the common core of the capsid proteins are shown using the RSV protein [1d1dA] as a representative. (a) The N-terminal domain, coloured blue→red from amino→carboxy termini with the five major helices represented by thickened lines. (b) The C-terminal domain, represented as in part (a) but with the MHR region marked in white.

fv1
mlv
virus capsid protein models threading


Embolization with the Amplatzer Vascular Plug in TIPS Patients

Vessel embolization can be a valuable adjunct procedure in transjugular intrahepatic portosystemic shunt (TIPS). During the creation of a TIPS, embolization of portal vein collaterals supplying esophageal varices may lower the risk of secondary rebleeding. And after creation of a TIPS, closure of the TIPS itself may be indicated if the resulting hepatic encephalopathy severely impairs mental functioning. The Amplatzer Vascular Plug (AVP; AGA Medical, Golden Valley, MN) is well suited for embolization of large-diameter vessels and has been employed in a variety of vascular lesions including congenital arteriovenous shunts. Here we describe the use of the AVP in the context of TIPS to embolize portal vein collaterals (n = 8) or to occlude the TIPS (n = 2).
Portal vein collaterals supplying esophageal varices may give high risk for secondary rebleeding. Those collaterals might require embolization to decrease this risk, despite transjugular intrahepatic portosystemic shunt (TIPS) placement. Embolization of the collaterals may be particularly difficult when these vessels are large. Moreover, after creation of a TIPS, closure of the TIPS itself may be indicated when hepatic encephalopathy severely impairs mental functioning. The Amplatzer Vascular Plug (AVP; AGA Medical, Golden Valley, MN) is a device well suited to embolize large vessels and has been employed in a variety of vascular lesions such as pulmonary arteriovenous malformations and congenital shunts [1, 2].
This report describes the use of the AVP in the context of TIPS to embolize portal vein collaterals (n = 8) or to occlude the TIPS (n = 2).
Description of Procedures
Embolization of Portal Vein Collaterals
Eight patients were referred for TIPS, seven because of multiple recurrent bleeding episodes from esophageal varices (n = 5), gastric fundus varices (n = 1), or both (n = 1) that could not be managed by endoscopic therapy. One other patient (n = 1) was in hypovolemic shock because of recurrent massive bleeding from rectal hemorrhoids. Three TIPS procedures were elective and five were done as emergency procedures for uncontrolled bleeding after initial hemodynamic stabilization had been achieved using blood transfusions, endoscopic treatment, intravenous octreotide infusion, and/or placement of a Sengstaken double balloon tube, alone or in combination. TIPS procedures were carried out under general anesthesia.
As an adjunct procedure, we obliterated the portal vein collaterals supplying the bleeding varices using AVP embolization (Figs. 1 and 2). This was done either before or after creation of the TIPS based on operator preference and clinical circumstances. Embolization early in the procedure with early control of the potential bleeding site was considered relevant in emergency procedures in hemodynamically unstable patients. Also, at least theoretically, AVP embolization may be done more easily prior to TIPS whenever the collateral target vein is still widened because of portal hypertension and easily opacified because of preferential flow. After the creation of a TIPS both these phenomena may have been altered.
Fig. 1Portal venous angiogram in a 53-year-old patient referred for TIPS creation because of recurrent variceal bleeding not controlled by medication plus multiple treatments with endoscopic band ligation and sclerotherapy. The varices are supplied by a single coronary veinFig. 2The same patient as in Fig. 1 immediately following TIPS creation and AVP embolization of the coronary vein (a; thin arrow). The unsubtracted image (b) shows the radiopaque sclerosing agent from prior endoscopic sclerotherapy (fat arrow) in the esophageal varices
Whenever AVP embolization preceded TIPS placement, the guiding catheter for AVP delivery was introduced transhepatically into the right portal vein. At our institution the portal vein is routinely catheterized prior to TIPS with a 3- or 4-Fr catheter to visualize the portal vein as a fluoroscopic target for transjugular transhepatic puncture. For transhepatic AVP placement this catheter was exchanged for a 6- to 8-Fr hepatic introducer sheath. After AVP embolization and initial creation of the hepatic-to-portal vein shunt tract for TIPS, the hepatic introducer sheath was withdrawn in multiple small successive steps over the course of the remainder of the procedure, so that a solid sealing clot could form in the hepatic parenchymal tract. No additional embolization of the tract was performed. Whenever vein embolization was performed after the creation of a TIPS, AVP placement was done via the transjugular route.
In all patients, the large-diameter coronary/gastric vein supplying extensive esophageal/gastric varices was embolized using a single AVP plug 10, 12, or 14 mm in diameter. For placement, a 6- to 8-Fr guiding catheter was first negotiated into the proximal part of the coronary vein. Then the AVP device was advanced through the guiding catheter and initially deployed by retracting the guiding catheter but without detaching the device. A control injection was made, and if needed, the AVP was retrieved into the guiding catheter and repositioned. When in a satisfactory position, the AVP was detached by anticlockwise rotation of the AVP guide wire to unscrew it from the AVP device proper. Within minutes of deployment, the collateral flow through the coronary vein had completely stopped. TIPS creation was done by placement of e-PTFE-covered stent-grafts (Viatorr; Gore, Flagstaff, AZ) 8–10 mm in diameter and 6 ± 2 cm long, resulting in a postprocedural portosystemic pressure difference of 5–10 mm Hg. All patients were hemodynamically stable immediately after the procedure.
During follow-up there were no recorded episodes of rebleeding with a follow-up of 1 year maximum. The patient who was treated for bleeding rectal hemorrhoids died after 3 days because of combined liver and respiratory failure. Another patient who received emergency TIPS while in hypovolemic shock due to massive unstoppable bleeding from esophageal varices died 46 days after the procedure because of liver and kidney failure complicated by ileus.
TIPS Occlusion
A 47-year-old male was referred for closure of a TIPS that had been created 3 years earlier for alcoholic Child A liver cirrhosis with recurrent episodes of esophagus variceal bleeding. He now presented with problems of amnesia, difficulties concentrating, and a general, overwhelming feeling of tiredness. Spectral EEG findings were consistent with grade 2 hepatic encephalopathy. The symptoms had not improved using conservative treatment. At angiography, a patent TIPS was seen, with a portosystemic pressure difference of 10 mm Hg. Closure of the TIPS with a 14-mm AVP device (Fig. 3) resulted in a dramatic improvement in cognitive functioning and normalization of the spectral EEG. Two years after closure of the TIPS, the patient is still free of variceal bleeding. Recently, however, he developed esophageal varices grades I–II, which are currently treated with band ligation. This will be repeated every 2 weeks until complete obliteration.
Fig. 3AVP device for TIPS occlusion in a patient with life-incapacitating hepatic encephalopathy. The 14-mm-diameter AVP is positioned through a guiding catheter in the central portion of the 10-mm-diameter e-PTFE stent-graft (a). By retraction of the guide wire, the unconstrained AVP sets in the intended place (b). At this point, the AVP can still be retracted into the guiding catheter if in an unsatisfactory position. The AVP is fully deployed by unscrewing the guide wire from the AVP device proper (c)
Another patient, a 65-year-old male, had been referred for TIPS placement for refractory ascites following liver transplantation, which required weekly paracentesis. In the days following placement of a 10-mm-diameter e-PTFE-covered stent-graft TIPS, liver failure developed, necessitating its closure. Closure of the TIPS was technically successfully achieved using a 16-mm-diameter AVP but did not substantially improve the clinical status. Because of liver failure and refractory ascites, the patient underwent a second liver transplantation 2 days after TIPS occlusion.
Discussion
In this report we have described the use of AVP embolization in the context of TIPS. For closure of collateral veins supplying bleeding varices, we consider the AVP a useful device in a defined subset of patients. The AVP’s relative advantages are its efficiency to occlude large-diameter vessels in a single-step procedure and its reliability of placement. Unlike standard embolization coils, the AVP can be positioned precisely, its position can be checked with contrast injections before actual detachment, and it can be repositioned if required. Migration of the AVP during or after deployment is unlikely. Disadvantages of the AVP include the higher cost of the device itself and of the additionally required guiding catheter. Furthermore, the 6- to 8-Fr guiding catheter markedly increases the bulk of the introducer system relative to the 4- to 5-Fr catheter alternatively used for coil embolization. In our practice we are inclined to use the AVP whenever there is a single, large-diameter collateral vein which would otherwise require the insertions of multiple embolization coils and/or whenever accurate placement of the embolic device close to the portal vein may be critical.
Embolization of portal vein collaterals to esophageal varices may be indicated even after successful portal venous decompression by creation of a TIPS. Especially in the life-threatening emergency situation of acute esophageal bleeding in hemodynamically unstable patients, we feel more confident providing additional control of the bleeding site by hemodynamically separating the weak-walled varices from the high-flow and potentially high-pressure portal venous bed. TIPS has proven efficient in the secondary prevention of variceal bleeding but this protection is not absolute. The rebleeding rate of TIPS alone (without embolization) has been reported as between 9% and 40.6%, with a median of 18.4% [3]. These values apply to bare-stented TIPS and initial experience suggests that the rebleeding rate is lower with e-PTFE-covered stented TIPS [4]. Whether or not performing embolization of the portal vein collaterals as an adjunct procedure provides additional protection against rebleeding is unknown. Under the assumption that it does, we routinely combine TIPS with embolization of all venous collaterals supplying the esophageal varices when creating a TIPS in the context of variceal bleeding.
Another potential use for the AVP device is for closure of the TIPS itself, in circumstances where hepatic encephalopathy and/or liver failure because of the shunt may prove to be life-incapacitating. Here, the effectiveness of the AVP in occluding large-diameter high-flow vessels, its resistance to migration, and its ease of placement are particularly helpful.
A potential concern when occluding a TIPS is the acute increase in portal venous pressure, which can lead to recurrence of variceal bleeding and ascites and, also, to hemodynamic changes with increased cardiac loading [5].
As mentioned, we routinely place a transhepatic catheter into the portal vein as a target for puncturing the portal vein during TIPS creation. In our experience, this increases the control over the exact entrance point into the portal vein and also reduces the number of puncture attempts while creating the TIPS tract. We have found that the reduced procedure time for the TIPS procedure proper more than makes up for the extra time it takes to insert this initial transhepatic catheter into the portal vein. We have encountered no complications because of this approach in more than 100 TIPS procedures.
To our knowledge, this is the second reported use of the AVP in the portal venous system. In a recent case report, Kessler and Trerotola described a patient with gastric varices and TIPS in whom they used the AVP device for transhepatic obliteration of the portal vein collateral supplying the varices [6], a patient similar to one of our patients with flow-mediated gastric varices. We would like to emphasize that the AVP may be considered also for embolization of the more common collateral veins to esophageal varices and, if need be, the TIPS stent itself.