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(Circulation. 1999;100:693-699.)
© 1999 American Heart Association, Inc.

Clinical Investigation and Reports

Splice-Site Mutations in Atherosclerosis Candidate Genes

Relating Individual Information to Phenotype

Yskert von Kodolitsch, MD; Reed E. Pyeritz, MD, PhD; Peter K. Rogan, PhD

From the Department of Cardiology (Y.v.K.), University Hospital Eppendorf, Hamburg, Germany; Departments of Human Genetics, Medicine, and Pediatrics (R.E.P.), Allegheny General Hospital, Pittsburgh, Pa; and Section of Molecular Genetics and Molecular Medicine (P.K.R.), Children's Mercy Hospital, Kansas City, Mo.

Correspondence to Peter K. Rogan, PhD, Section of Molecular Genetics and Molecular Medicine, Children's Mercy Hospital and Clinics, 2401 Gillham Road, Kansas City, MO 64108. E-mail progan{at}cmh.edu

	Abstract

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Background—Nucleotide variants in several genes for lipid and methionine metabolism influence the risk of premature atherosclerosis. Ten percent of single nucleotide substitutions in these genes involve mRNA splice sites. The effects of some of these changes on splicing and on phenotypic severity are not inherently obvious.

Methods and Results—Using an information theory-based model, we measured the individual information content (R_i, in bits) of splice sites adjacent to 289 mutations (including 31 splice-site mutations) in the atherosclerosis candidate genes APOAII, APOB, APOCII, APOE, CBS, CETP, LCAT, LIPA, LDLR, and LPL. The predictions of information analysis were then corroborated by published mRNA analyses. The R_i values of mutant sites were consistent with either complete (n=17) or partial (n=8) inactivation of these sites. Seven mutations were predicted to activate cryptic splice sites. Predicted inactive mutant sites were associated with either "average" or "severe" dyslipidemia and commensurate reductions in protein levels or activity, whereas mutations expected to exhibit residual splicing had average or "mild" effects on lipid and protein expression.

Conclusions—Information analysis of splice-junction variants in atherosclerosis candidate genes distinguishes inactive from leaky splice sites and identifies activated cryptic sites. Predicted changes in splicing were related to phenotypic severity.

Key Words: atherosclerosis • genetics • lipids • risk factors • RNA

	Introduction

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Mendelian syndromes that carry a substantial risk for developing occlusive arterial disease frequently alter metabolic pathways involving cholesterol, triglycerides, and sulfurated amino acids. For instance, mutations in the genes coding for cystathione ß-synthase (CBS), the low density lipoprotein receptor (LDLR), the LDLR binding region of apolipoprotein B (APOB), apolipoprotein E (APOE), lecithin-cholesterol transferase (LCAT), lysosomal acid lipase A (LIPA), lipoprotein lipase (LPL), and APOCII cause occlusive arterial disease. A low risk of atherosclerosis is observed in APOB mutations outside the LDL binding region and in cholesteryl ester transfer protein (CETP) deficiency, whereas the impact of APOAII deficiency on atherosclerosis has not been established¹ (Table 1).

View this table: [in this window] [in a new window]   Table 1. Reference Parameters for Atherosclerosis Candidate Gene Mutations

Among the single nucleotide substitutions in these genes, 10% are located within splice junctions (Table 1). The donor GT and acceptor AG dinucleotides at the splice junctions are conserved; however, other sequences at these and other conserved positions are also permissible. Mutations within natural (termed "primary") splice junctions can either completely or partially inactivate these sites. Some nucleotide changes activate splice sites that are not normally recognized ("cryptic" or "secondary" sites). Other splice-site substitutions have no effect on mRNA splicing.^{2 3}

Information theory–based models that account for all of the nucleotide variations in splice sites have been used to predict the activities of natural and mutant sites and identify cryptic splice sites.² The information content (in bits) of a member of any sequence family describes the degree to which that member contributes to the conservation of the entire family.⁴ The effects of all nucleotide changes are detectable, given that information is cumulative over all positions in a splice site.²

In previous studies, lipid and protein levels or enzymatic activities of gene products were used to indirectly assess the effects of mutations that predispose a person to atherosclerosis. We used information theory–based models of mRNA splicing to relate the severity of splicing mutations to atherosclerosis phenotypes.

	Methods

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Selection of Mutations
More than 200 genes have been proposed as influencing the risk of developing atherosclerosis.¹ Of the genes known to alter lipid and methionine metabolism, the Human Gene Mutation Database⁵ reveals 10 genes that have splicing mutations (n=31). Missense mutations (n=258) were also analyzed to examine whether these changes might have collateral effects on splicing.⁶

Computation of Individual Information
The individual information contents (R_i) of natural and mutant splice sites and coding sequence variants in atherosclerosis candidate genes were compared.⁴ The computer programs Scan, MakeWalker, and Lister were used to compute R_i values and display results.⁷

The information contents of potential splice sites were determined for sequences up to 150 nucleotides upstream and downstream of each nucleotide substitution.^{2 8} R_i values could not be directly assessed for the LDLR and CBS genes^{2 7} because the terminal nucleotides of the corresponding splice junctions (positions -16 to -25 for acceptor sites, and +6 for donor sites) were unavailable. To model incomplete sites, the possible range of R_i values was defined by computing R_i for the best and worst sequence combinations. These ranges were computed for the missing sequence data from the corresponding coordinates of the donor and acceptor weight matrix elements that produced the maximal and minimal R_i values. Interpretation of variants was possible only if the R_i intervals of the natural and corresponding mutant sites did not overlap. Inactivating mutations were distinguishable from those that resulted in leaky splicing if the R_i interval of the mutant site was consistently either less or greater than 2.4 bits, respectively. Six of 171 variants with incomplete acceptor splice sites could not be interpreted because the R_i intervals of the variant and corresponding natural sequences overlapped.

Criteria for Grading Phenotypic Severity
For each splicing mutation, reference distributions of protein expression, plasma lipids, or plasma homocystine were derived from individuals with other mutations in the corresponding gene from previously published reports (data not shown) (Table 1). Individuals with mutations in the same gene were also distinguished according to genotype because the diagnosis, in some instances, depended on the mode of inheritance. Protein levels and enzymatic and/or binding activity were measured by the same methods for each splice-site mutation and the corresponding reference population; these values are given as the reported range of the percentage of normal protein levels. The mean and 95% confidence intervals (±2SD) of lipid values for each reference population were either derived from published reports or computed from values in published patient cohorts.

Stringent criteria for dyslipidemic phenotypes were defined from reference mean values based on populations of individuals with mutations in the same gene. Each lipid parameter (total plasma triglycerides, cholesterol, and LDL and HDL cholesterol) with values within ±2SD of the respective reference population was defined as "average." Individuals with significantly increased lipid levels (>2SD above the mean in the affected reference population) were defined as having "severe" dyslipidemia (eg, Table 2, mutations 1, 13, 16, 22, and 31 [mutation numbers throughout the article are those found in Table 2]). Those carrying mutations that decreased lipid levels >2SD below the average in the reference population (eg, mutation 21) were also designated as severe. Dyslipidemia was defined as "mild" in patients with mutations producing HDL-cholesterol levels >2SD above the average for the reference population (mutations 8 and 20).

View this table: [in this window] [in a new window]   Table 2. Information Analysis of Splice-Site Variants

View this table: [in this window] [in a new window]   Table 2A. Continued

Protein levels and/or activities were similarly graded relative to reference values for populations with mutations in the same gene. Protein levels and/or activities were defined as average if they were within the reported range of the percentage of normal for each respective reference population. Reduction of protein levels and/or activities below or above this reported range were graded as severe or mild, respectively.

Clinical severity of atherosclerosis was assessed using age-related expression of angina pectoris or myocardial infarction. The ages of onset of coronary symptoms for 2 or more unrelated patients with identical LDLR (mutation 3) and APOE (mutation 20) mutations were related to the corresponding age distributions for reference populations with type II (LDLR)⁴⁴ or type III (APOE)⁴⁵ familial hyperlipoproteinemia. Clinical signs of atherosclerosis were graded as average for ages of onset within 2SD of the mean age for the reference populations; mild or severe atherosclerosis occurred with average ages of onset above or below the 2SD bound, respectively.

Phenotypic analyses were limited to patients with fasting plasma lipid and homocysteine levels. Because outlier phenotypes were derived using stringent statistical criteria, some patients originally described as severely or mildly affected were categorized as having average phenotypes in the present study (mutations 3 and 7 [reference 15]). Mutations in compound heterozygotes (n=5) were not analyzed because the phenotypic effects of each allele could not be separated.

Criteria for Predicting Splice-Site Function
The effects of nucleotide substitutions were predicted from R_i values on the basis of the following, previously validated criteria² :

1. Substitutions resulting in R_i<2.4 bits would completely inactivate primary splice sites and result in severely affected phenotypes.
   
2. Substitutions with decreased R_i values that were 2.4 bits would reduce splicing, thereby producing a milder phenotype. The residual amount of correctly spliced mRNA at leaky sites was computed as the minimum fold change in binding affinity, 2^Ri (R_i is the difference between the R_i value of the natural site and that of the variant site). The result was expressed as the maximum percent of normal mRNA.^{2 5}
   
3. Substitutions would activate cryptic splice sites with R_i values exceeding that of the adjacent natural site. Observations of exon skipping or cryptic splicing were used to validate R_i-based predictions whenever these data were available.²
   
4. Nucleotide changes that did not significantly alter R_i would have no effect on splicing.⁷ The significance of the change in R_i (R_i) was assessed with a paired t-test using a cutoff at 5% significance.²
   

	Results

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Prediction and Validation of Splicing Defects
We predicted either complete loss of primary splice site function (mutations 1 to 5, 9 to 16, 18, 19, 21, and 22) or leaky splicing (mutations 6 to 8, 17, 20, 23, and 24) on the basis of reductions in R_i values of the corresponding mutant sites. We predicted that mutations 18 to 24 would activate cryptic splice sites. mRNA analyses corroborated these predictions for primary splice sites (mutations 1 to 8 and 18 to 20) and activated cryptic splice sites (mutations 1, 18 to 20, and 32). The R_i value of a polymorphic intron variant within an acceptor site was unchanged (mutation 26), which was expected on the basis of previous studies of polymorphic splice sites.^{2 7} Of the 258 missense mutations analyzed, we only predicted 2 (0.8%) would decrease R_i values and affect splicing at adjacent donor sites (mutations 23 and 24). Five missense substitutions produced insignificant changes in the R_i values in adjacent splice sites (mutations 27 to 31); the remainder had no detectable effect on splicing. Despite the fact that the splice-site sequences for the LDLR and CBS genes were incomplete, the R_i analyses were interpretable for 165 of the 171 missense changes adjacent to these sites. Splicing mutations at the presumed branchpoint sequence reduced (mutation 32) or completely abolished (mutation 25) the use of the corresponding acceptor site but did not alter the information content. This was not surprising because this signal is not detectable by information analysis³ or by other methods. However, one of these branchpoint mutations was also predicted to activate cryptic splicing (mutation 32).

Relationship of R_i to Phenotype
To determine whether disease phenotype was related to predicted splice-site use, the degrees of dyslipidemia, protein expression, and clinical severity were graded and compared with the R_i values of mutant splice sites. Severe (mutations 1, 13, 16, 21, and 22) and average (mutations 2 to 4, 9 to 12, 14, 18, and 19) dyslipidemia were present in individuals with primary splice-site mutations with R_i values <2.4 bits, but dyslipidemia was not present in individuals with mutant sites 2.4 bits. Conversely, mutant sites with R_i values 2.4 bits were found in patients with mild (mutations 8 and 20) or average (mutations 7, 23, and 24) dyslipidemia but not in those with severe dyslipidemia. Average (mutations 2, 9, 11, 16, 19, 21, and 22), severe (mutation 1), and mild (mutation 18, which is borderline average) decreases in protein levels or enzymatic activity were found in individuals harboring mutant splice sites <2.4 bits (Table 3). Mutations that resulted in sites with R_i2.4 bits, however, exhibited only mild (mutations 8 and 20) or average (mutation 7) reductions in protein expression. On the basis of age at onset of coronary symptoms, the severity of clinical atherosclerosis for one patient who had R_i<2.4 bits was graded as average (mutation 3); it was graded mild for another individual carrying a mutation with R_i>2.4 bits (mutation 20). Corresponding clinical data were not available for the other mutations.

View this table: [in this window] [in a new window]   Table 3. Relationship Between Information Content of Splice-Site Mutations and Phenotype

	Discussion

Top Abstract Introduction Methods Results Discussion References

 
The decrease in information content at mutant splice sites in atherosclerosis candidate genes is related to diminished splice-site use. Predicted reductions in the proportions of normal mRNA were confirmed for 11 splice-site mutations. The decrease in R_i also tended to be related to the degree of reduced protein expression and to phenotypic severity (Table 3). Missense mutations can, in some cases, simultaneously affect both translation and splicing, and the splicing effects can be detected by information analysis.^{4 7} We found 2 missense changes within codons adjacent to splice junctions (mutations 23 and 24) that were predicted to impair splicing. By contrast, 5 other similarly located missense mutations slightly increased (mutations 27 to 29) or decreased (mutations 30 and 31) R_i values without affecting splicing. Incomplete genomic sequences for splice sites in the LDLR and CBS genes were analyzed by comparing the ranges of possible R_i values for the corresponding natural and variant sites. The results were interpretable for all except 6 mutations in which the R_i intervals of natural and variant acceptor sites overlapped. Thus, the 5' and 3' terminal intronic sequences (positions -16 to -25 of acceptor and +6 of donor sites) can sometimes make a significant contribution to the overall information content of a splice site. The decrease in R_i values for splicing mutations was related to the severity of dyslipidemia (Table 3). Splicing mutations that resulted in milder dyslipidemia were predicted to be leaky (2.4 bits), whereas severely affected individuals carried mutations that presumably inactivated splice sites and abolished protein expression (<2.4 bits). The phenotypes at the outlying lipid values were concordant with these predictions, although corresponding R_i values for mutations in individuals with average dyslipidemia were not predictive. More refined models, incorporating lipid phenotypes as quantitative traits, will require analysis of additional patients and mutations.

	Acknowledgments

 
We thank Drs Thomas D. Schneider and Christoph A. Nienaber for their suggestions. Support was provided by grants from the Public Health Service (CA74683), the American Cancer Society (DHP-132), Merck Genome Research Foundation (to P.K.R.), and Deutsche Forschungsgemeinschaft (KO 1828/1 and KO 1828/1-2; to Y.v.K).

Received December 31, 1998; revision received May 10, 1999; accepted May 25, 1999.

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