Skip to main content

Main menu

  • Home
  • Content
    • Published Ahead of Print
    • Current Issue
    • Podcasts
    • Subject Collections
    • Archives
    • Kidney Week Abstracts
    • Saved Searches
  • Authors
    • Submit a Manuscript
    • Author Resources
  • Trainees
    • Peer Review Program
    • Prize Competition
  • About CJASN
    • About CJASN
    • Editorial Team
    • CJASN Impact
    • CJASN Recognitions
  • More
    • Alerts
    • Advertising
    • Feedback
    • Reprint Information
    • Subscriptions
  • ASN Kidney News
  • Other
    • ASN Publications
    • JASN
    • Kidney360
    • Kidney News Online
    • American Society of Nephrology

User menu

  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
American Society of Nephrology
  • Other
    • ASN Publications
    • JASN
    • Kidney360
    • Kidney News Online
    • American Society of Nephrology
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Advertisement
American Society of Nephrology

Advanced Search

  • Home
  • Content
    • Published Ahead of Print
    • Current Issue
    • Podcasts
    • Subject Collections
    • Archives
    • Kidney Week Abstracts
    • Saved Searches
  • Authors
    • Submit a Manuscript
    • Author Resources
  • Trainees
    • Peer Review Program
    • Prize Competition
  • About CJASN
    • About CJASN
    • Editorial Team
    • CJASN Impact
    • CJASN Recognitions
  • More
    • Alerts
    • Advertising
    • Feedback
    • Reprint Information
    • Subscriptions
  • ASN Kidney News
  • Visit ASN on Facebook
  • Follow CJASN on Twitter
  • CJASN RSS
  • Community Forum
Hereditary Disease
You have accessRestricted Access

Evidence for Pathogenicity of Atypical Splice Mutations in Autosomal Dominant Polycystic Kidney Disease

Kiarong Wang, Xiao Zhao, Shelly Chan, Onur Cil, Ning He, Xuewen Song, Andrew D. Paterson and York Pei
CJASN February 2009, 4 (2) 442-449; DOI: https://doi.org/10.2215/CJN.00980208
Kiarong Wang
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiao Zhao
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shelly Chan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Onur Cil
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ning He
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xuewen Song
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew D. Paterson
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
York Pei
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data Supps
  • Info & Metrics
  • View PDF
Loading

Abstract

Background and objectives: Mutation-based molecular diagnostics of autosomal dominant polycystic kidney disease (ADPKD) is complicated by locus and allelic heterogeneity, large multi-exon gene structure and duplication in PKD1, and a high level of unclassified variants. Comprehensive screening of PKD1 and PKD2 by two recent studies have shown that atypical splice mutations account for 3.5% to 5% of ADPKD. We evaluated the role of bioinformatic prediction of atypical splice mutations and determined the pathogenicity of an atypical PKD2 splice variant from a multiplex ADPKD (TOR101) family.

Design, setting, participants, & measurements: Using PubMed, we identified 17 atypical PKD1 and PKD2 splice mutations. We found that bioinformatics analysis was often useful for evaluating the pathogenicity of these mutations, although RT-PCR is needed to provide the definitive proof.

Results: Sequencing of both PKD1 and PKD2 in an affected subject of TOR101 failed to identify a definite mutation, but revealed several UCVs, including an atypical PKD2 splice variant. Linkage analysis with microsatellite markers indicated that TOR101 was PKD2-linked and IVS8 + 5G→A was shown to cosegregate only with affected subjects. RT-PCR of leukocyte mRNA from an affected subject using primers from exons 7 and 9 revealed six splice variants that resulted from activation of different combinations of donor and acceptor cryptic splice sites, all terminating with premature stop codons.

Conclusions: The data provide strong evidence that IVS8 + 5G→A is a pathogenic mutation for PKD2. This case highlights the importance of functional analysis of UCVs.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder worldwide, affecting approximately one in 500 live births. It is characterized by focal development and progressive enlargement of renal cysts, leading to end-stage renal disease (ESRD) in late middle age. Typically, only a few renal cysts are detected in most affected subjects before 30 yr of age. However, by the fifth decade of life, hundreds to thousands of renal cysts are found in most patients. Overall, ADPKD accounts for 5% to 8% of end-stage renal disease (ESRD) in developed countries (1,2). Extrarenal complications of ADPKD are variable and include inguinal hernias, colonic diverticulae, valvular heart disease, and intracranial arterial aneurysms (1).

Mutations of two genes, PKD1 (MIM 601313) and PKD2 (MIM 173910), account for approximately 85% and 15% of all cases of ADPKD in linkage-characterized European populations (3,4). Although the clinical manifestations of PKD1 and PKD2 overlap completely, a strong locus effect on renal disease severity is evident with more severe renal disease in PKD1 than PKD2 (median age at ESRD: 54 yr versus 74, respectively) (5). PKD1 is a large gene consisting of 46 exons with an open reading frame of approximately 13 kb and is predicted to encode a protein of 4302 amino acids. Its entire 5′ region up to exon 33 has been duplicated six times proximally on chromosome 16p, and the presence of these highly homologous pseudogenes has made genetic analysis of PKD1 difficult (1,2). Recent availability of protocols for long-range and locus-specific amplification of PKD1 has enabled the complete mutation screening of this complex gene (6–9). In contrast, PKD2 is a single-copy gene consisting of 15 exons with an open reading frame of approximately 3 kb and is predicted to encode a protein of 968 amino acids (1,2).

The diagnosis of ADPKD is straightforward in affected subjects with a positive family history and enlarged kidneys with multiple cysts (6). Renal ultrasound is a useful method for this purpose, and age-dependant criteria based on cyst number have been derived for subjects born with 50% risk of PKD1 or PKD2 (6,10). However, ultrasound diagnosis of ADPKD in younger at-risk subjects with equivocal or negative findings and in subjects affected by PKD2 or de novo disease remains a challenge (6). For these reasons, molecular screening is a useful tool in the clinical setting. However, marked allelic heterogeneity is evident, with over 200 different PKD1 and over 50 different PKD2 mutations reported to date (2,6–9,11–13). The majority of these mutations are unique and scattered throughout both genes. Although the majority of these mutations are predicted to be protein truncating (frame-shift deletion/insertion, nonsense or canonical splice changes), a large number of unclassified variants (UCVs; in-frame deletions, mis-sense and atypical splice changes) has also been reported (7–9). Comprehensive screening of both PKD1 and PKD2 by two recent studies identified definitive and probable mutations in 42% to 63% and 26% to 37% of patients, respectively (8,9). These two studies also reported that atypical splice mutations account for approximately 3.5% to 5% of ADPKD (8,9). In the current study, we performed and evaluated the utility of bioinformatics analysis on 17 reported atypical PKD1 and PKD2 splice mutations. We also determine the pathogenicity of an atypical splice variant found in a family affected by PKD2 and highlight the importance of functional analysis of UCVs in molecular diagnostic testing.

Materials and Methods

Bioinformatic Analysis of Reported Atypical Splice Site Variants

We used PubMed (http://www.ncbi.nlm.nih.gov/sites/entrez?db=PubMed) to identify all of the atypical splice variants for PKD1 and PKD2 reported to date. We then performed bioinformatic analysis of the splice variants using Neural Network (http:/www/fruitfly.org/seq_tools/splice.html) and the maximum entropy (MAXENT) model (14).

Study Subjects

All available family members from TOR101 were clinically assessed for ADPKD. After receiving their informed consent, we reviewed their medical records and used renal ultrasonography to screen all at-risk subjects without a known diagnosis of ADPKD. We used the following criteria for the diagnosis of ADPKD: (1) the presence of at least three renal cysts (unilateral or bilateral) in an at-risk subject younger than 30 yr, (2) the presence of at least two cysts in each kidney in an at-risk subject age 30 to 59 yr, or (3) the presence of at least four cysts in each kidney in an at-risk subject age 60 yr or older (10). All study subjects provided a blood sample for serum creatinine and DNA genetic analysis. The Institutional Review Board of the University Health Network in Toronto approved all of the protocols used for this study.

Gene-Based Mutation Screening

Sequence analysis of PKD1 and PKD2 was performed in a clinically affected subject (II: 4) using a commercial diagnostic service (Athena Diagnostics, Worcester, MA; http://www.athenadiagnostics.com/content/test-catalog/find-test/service) (8,9). Briefly, genomic DNA was used for locus-specific long-range PCR amplification of eight segments encompassing the entire PKD1 duplicated region. The long-range PCR products served as templates for 43 nested PCRs, and the unique region of PKD1 and the entire PKD2 were amplified from genomic DNA in 28 additional PCRs. All 71 PCR products were bidirectionally sequenced, including the coding regions and exon–intron splice junctions of both genes (9).

Haplotype and Linkage Analysis

Genomic DNA was extracted from peripheral blood leukocytes using the FlexiDNA extraction Kit (Qiagen, Mississauga, ON, Canada). All available family members from TOR101 were genotyped with microsatellite markers at both PKD1 and PKD2 loci by means of a published protocol (15). The locations of the markers relative to PKD1 are as follows (the number between markers denotes intermarker distance in centimorgans): D16S521–2.0-HBAP1–2.0-KG8/PKD1-0.8-D16S2618 (16). KG8 is an intragenic marker located within the 3′ end of PKD1. The locations of the markers relative to PKD2 are as follows: D4S231–2.0-D4S1534–2.3-SPP1–0.2-PKD2-2.5-D4S423 (17). Genotyping was performed by [32P] α-deoxycytidine triphosphate-labeling of PCR products and was analyzed by PAGE and autoradiography. Haplotypes were constructed by hand and by using the program GENEHUNTER (v2.1_r5) (18). Two-point and multipoint linkages with “affected-only analysis” were performed with M-LINK from the FASTLINK package (v4.0) (19,20) and GENEHUNTER (v2.1_r5), respectively. An autosomal dominant model with a disease allele frequency of 0.001 and a phenocopy rate of 0.001 was assumed. Marker allele frequencies were obtained from married-in subjects and reconstruction of the genotypes of the founders.

Segregation Analysis of an Atypical PKD2 Splice Variant

PCR amplification of exon 8 of PKD2, including the mutated atypical splice site (IVS8 + 5G→A), was performed with genomic DNA with a forward and a reverse primer, 5′-CCCGCCGCCCCCGCCGTTATTATAATACAGTCACACCATTTTGTTT-3′ (the first 16 bases of this primer provide a 5′-GC clamp) and 5′-TGAGAAGCAGTGACAACTGTGA-3′, and HotStart TaqDNA polymerase (Qiagen) for 35 cycles at an annealing temperature of 60°C. Five microliters of PCR reaction was then used for denaturing HPLC (DHPLC) at 53.5°C using the WAVE system (Transgenomic Inc., Omaha, NE), and the elution profiles of the normal and mutant variants were used for segregation analysis in TOR101.

RT-PCR and Splice Variant Analysis

Total RNA from extracted from peripheral blood mononuclear cells using the Trizol method (Invitrogen, Carlsbad, CA). Reverse transcription was performed in a 20-μl volume with 2 μg of total RNA, 1 μl of oligo(dT)12–18, and SuperScript II RNase H-Reverse Transcriptase (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. One microliter of RT product from first-strand reaction was amplified in 25 μl of hot start PCR reaction, including 0.25 μM of each primer and HotStar Taq DNA polymerase (Qiagen, Mississauga, ON, Canada). The forward and reverse primers used were: 5′-CCCAACTTTGAGCATCTGG-3′ and 5′-CCAAAACTCGATTAGCTTCCTC-3′, respectively. The amplified fragment spans the last part of exon 7, the entire exon 8, and the beginning of exon 9. Cycling conditions were 94°C for 15 min, followed by 36 cycles of 94°C for 45 s, annealing temperature for 45 s, and 72°C for 1 min (touch-down annealing temperature from 63 to 60.5°C through the first six cycles).

Cloning of the RT-PCR products was performed with the TOPO TA Cloning Kit (Invitrogen) according to manufacturer's instructions. Four microliters of gel-extracted PCR products, excluding the lower wild-type band from II:4 (Figure 2A), were used for the ligation reaction, and the mixture was incubated at room temperature for 10 min. Then 2 μl of the TOPO cloning reaction mix were added to One Shot Chemically Competent E. coli (Invitrogen) and mixed gently. After incubation on ice for 10 min, the mixture was heat-shocked at 42°C for 30 s, and immediately transferred to ice. Two hundred fifty microliters of SOC medium was added into the vial containing the reaction mix which was shaken horizontally at 37°C at 200 rpm for 1 h. Fifty microliters of the transformation mix were spread on two prewarmed selective LB plates (containing 100 μg/ml ampicillin with 40 μl of 40mg/ml X-gal) and incubated at 37°C overnight. All white colonies were inoculated on a plate, numbered serially, and resuspended individually in 50 μl of water. After 5 min of heating at 95°C and centrifugation, 1 μl of supernatant from each colony was added into a final 10-μl PCR reaction using the same RT-PCR conditions, except the cycle number was reduced to 30. Thirty randomly chosen plasmids with cloned cDNA inserts were sequenced using the RT-PCR primers.

To determine the relative frequencies of the wild type (WT) and six splice variants (Table 2), purified PCR products from 97 randomly chosen white colonies were sized on a 2.5% agarose gel. To distinguish the WT allele (334 bp) from splice variant V (330 bp), all samples with PCR products of approximately 330 bp were restriction digested by Hinf I (New England Bio Lab, Ipswich, MA), which only cleaved splice variant V into 260 and 70 bp fragments, and resized on a 2.5% agarose gel. Similarly, to distinguish splice variants II and III (410 and 408 bp, respectively), all samples with PCR products of approximately 410 bp were restriction digested by Bsr I (New England Bio Lab) which only cleaved the splice variant III into approximately 370- and 40-bp fragments, and resized on a 2.5% agarose gel.

Splice Site Prediction

Exonic splicing enhancers (ESEs) are common cis-regulatory elements that act as bindings sites for Ser/Arg-rich proteins (SR proteins), a family of conserved splicing factors that participate in multiple steps of the splicing pathway (21). We used ESEfinder (http://exon.cshl.edu/ESE), which predicts ESE binding sites based on the consensus motifs of four SR proteins (SF2/ASF, SC35, SRp40 and SRp55), to assess whether IVS8 + 5G→A might affect an ESE binding site (21). Three predictive software packages were used to calculate the strengths of the 5′ and 3′ cryptic splice sites of our cloned RT-PCR products: (1) SpliceSiteFinder (http://violin.genet.sickkids.on.ca/∼ali/splicesitefinder.html) uses the Shapiro and Senapathy consensus matrix, which reflects the degree of conservation at each position of the consensus 5′ss motif (22); (2) the Maximum Dependency Decomposition (MDD) model uses a decision-tree approach that emphasizes the strongest dependencies in the early branches of the tree (23); and (3) MAXENT, which can monitor the dependencies between different positions by using a maximum-entropy distribution consistent with lower order marginal constraints (24–26). The latter two software tools are available from the Burge lab at MIT (http://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html) (24,25).

Results

From the published literature, we identified 11 atypical splice variants for PKD1 and 3 for PKD2 (7–9,11–13), and performed bioinformatic analysis on them using Neural Network and MAXENT (Table 1). Although seven of 11 PKD1 and one of three PKD2 atypical splice variants are definitive (Class A) mutations confirmed by RT-PCR, our bioinformatics analysis failed to identify four of them (IVS31 + 25del19; IVS43 + 14del20; IVS43 + 17del18; IVS25–16G>A) as pathogenic. Three of these latter mutations are 18- to 20-bp deletion of a small intron (75 to 87 bp) resulting in a short intron that may be suboptimal for normal splicing (13). However, one or both of the above algorithms predicted aberrant splicing in the remaining four mutations. For example, Neural Network predicted the activation of a cryptic splice site 8 bp upstream from the authentic 3′ splice site by the PKD1 mutation IVS15–10C>A as a result of a decreased 3′ authentic splice site score (from 0.95 [WT] to 0.79 [mutant]) and increased 3′ cryptic splice site score (from < 0.1 [WT] to 0.98 [mutant]). This prediction was also supported by bioinfomatic analysis using MAXENT (Table 1). Similarly, both Neural Network and MAXENT predicted aberrant splicing for the atypical PKD1 splice variants IVS24 + 5G>C and IVS17 + 4del4, and atypical PKD2 splice variant IVS8 + 5G>A. However, without RT-PCR confirmation, these mutations were classified as probably or likely pathogenic, and their clinical utility remains to be defined (8,9). In the case of the atypical PKD1 splice variant IVS24 + 28G>T, the changes of mutant 5′ authentic and cryptic splice site scores from MAXENT (5.97→7.2), but not Neural Network (0.91→0.63), suggest that this variant may be pathogenic. Last, both algorithms predicted the atypical PKD1 splice variants IVS26 + 76C>A, IVS20–16C>G, IVS37–4C>T, and IVS10–4A>G, and atypical PKD2 splice variant IVS6–4T>C as neutral polymorphisms, although none of the predictions have been confirmed by RT-PCR (Table 1).

View this table:
  • View inline
  • View popup
Table 1.

Bioinformatic analysis of PKD1 and PKD2 atypical splice variants

Subject III:9 from TOR101 was referred to us for evaluation as a potential living kidney donor to her affected aunt (II:4) (Figure 1B). She was at risk for ADPKD but had a negative renal ultrasound at age 25 yr. To assess her risk for ADPKD, we sought to identify the pathogenic mutation in her aunt by sequencing of both PKD1 and PKD2 using a commercial service (Athena Diagnostics, Worcester, MA). Three mis-sense and one intronic variants were identified, but none was reported to be definitively pathogenic (Table 2). Of note, the atypical PKD2 splice mutation (IVS8 + 5G→A) (Figure 1A) we identified in TOR101 has been independently reported as a probable pathogenic mutation by two recent studies (8,9). To further delineate the molecular genetic defect of TOR101, we genotyped eight affected subjects from this family using microsatellite markers at both PKD1 and PKD2. Affected-only linkage analysis indicated that TOR101 was PKD2 linked (parametric multipoint lod score: 2.06 for PKD2 and −2.54 for PKD1) and the PKD2 haplotype 1 to 2–1 to 2 cosegregated with all of the affected subjects (Figure 1B). Consistent with PKD2 linkage, none of the older affected family members (II:2, eGFR of 45 ml/min at age 70 yr; II:4, eGFR of 20 ml/min at age 69 yr; and II:6, eGFR of 52 ml/min at age 66 yr) from TOR101 had ESRD at their last follow-up. DHPLC of genomic exon 8 PCR amplicons showed that IVS8 + 5G→A displayed a distinct elution profile compared with control and cosegregated with all of the affected subjects in TOR101 (Figure 1C).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

(A) Sequence tracing of a heterozygous PKD2 mis-sense splice site variant (IVS8 + 5G→A) identified in II:4. (B) Pedigree structure and haplotype analysis. The PKD2 haplotype (D4S231-D4S1534-SPP1-D4S423) 1 to 2–1 to 2 segregated with all of the affected subjects. (C) DHPLC showing a distinct elution profile for IVS8 + 5G→A in II:4, which cosegregated with all of the affected subjects. Squares and circles denote male and female, respectively. The cross symbol denotes the study subject; the solid filled symbol, the affected subject.

View this table:
  • View inline
  • View popup
Table 2.

Directing sequencing of PKD1 and PKD2 identified four unclassified variants in an affected subject (II:4) from TOR101

RT-PCR using total RNA from peripheral blood mononuclear cells and primers from exons 7 and 9 revealed multiple higher molecular extra bands in the affected subject II:4 compared with one distinct band in the normal control (Figure 2A). Gel purified RT-PCR products from II:4 were subcloned and miniprepped, and 30 randomly chosen plasmids with cloned cDNA inserts were sequenced. In addition to the WT allele, six splice variants were identified, all terminating with a premature stop codon (Figure 2B and Table 3). Sequence analysis of the six splice variants revealed activation of three 5′ cryptic splice sites (A, B, C) and two 3′ cryptic splice sites (B′, C′) along with the authentic 5′ (D) and 3′ (A′) splice sites in different combinations (Figure 2C). To evaluate the relative frequencies of these splice variants, we performed PCR in 97 randomly selected white colonies using the same RT-PCR primers from exons 7 and 9. Purified PCR products (some also restriction digested with Hinf I or Bsr I; see Material and Methods) were analyzed according to their size by 2.5% agarose gel electrophoresis. Fifty-seven clones (approximately 58%) yielded a WT allele, and the remaining 40 clones yielded six splice variants in varying frequencies (Table 3).

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

(A) RT-PCR from peripheral blood mononuclear cells using primers from exon 7 and 9. The normal control subject (lane 1) had one distinct band, whereas the affected subject (II:4) has multiple higher molecular weight extra bands (lane 2). Lane 3 is a negative control without DNA. (B) Diagrammatic representation of the different splice variants identified from II:4, which were produced through activation of different combinations of donor and acceptor cryptic splice sites. (C) Representation of the authentic (D and A′) and activated cryptic (B, C, A, C′ and B′) splice sites along the genomic DNA.

View this table:
  • View inline
  • View popup
Table 3.

Effects of splice variants on their encoded protein products

Using ESEfinder (17), we identified three SRp55 motifs with significant scores (> 2.676) overlapping the authentic IVS8 splice site (data not shown). The IVS8 + 5G→A transition, in turn, is predicted to result in the loss of one of these motifs, and could therefore diminish the affinity of SRp55 for the authentic 5′ splice site in favor of other cryptic splice sites. We also used three algorithms to score the relative strengths of the splice sites used in the cloned RT-PCR variants (Table 4). In general, there was good agreement between these algorithms, with the authentic 5′ and 3′ (D(IVS8 + 5G) and A′) splice sites being the most preferred, followed by A, B, C, and D(IVS8 + 5A) in descending order among the 5′ cryptic splice sites, and B′ and C′ in descending order among the 3′ cryptic splice sites. However, the Shapiro and Senapathy algorithm predicted that the mutant variant D(IVS8 + 5A) would be more preferred than the 5′ cryptic splice sites B and C, which is opposite from the results predicted by the MDD and MAXENT algorithms. In addition, although both the MDD and MAXENT algorithms predict a stronger preference for the 5′ cryptic splice site A than B, the observed frequencies of usage of these splice sites suggests the converse.

View this table:
  • View inline
  • View popup
Table 4.

Predicted strength of authentic and cryptic splice sites and frequency of their use

Discussion

Sequence-based mutation screening for PKD1 and PKD2 is now available and provides a novel means for improved diagnosis in ADPKD (6–8). This approach is particularly useful in the clinical evaluation of younger at-risk subjects with equivocal imaging results and in patients with PKD2 or de novo ADPKD. Conversely, gene-based molecular diagnostics may also be used for disease exclusion in the evaluation of younger subjects at risk of ADPKD for living-related kidney donation (6). Comprehensive screening of both PKD1 and PKD2 by two large recent studies identified definitive and probable mutations in 42% to 63% and 26 to 37% of cases, respectively (8,9). The assignment of pathogenicity for the former class of mutations (e.g., protein-truncating) is straightforward. In contrast, the assignment of pathogenicity in the latter class of mutations (e.g., nonconserved coding sequence mis-sense variants, in-frame deletions, and atypical splice changes) from a high level of UCVs remains challenging in the absence of a functional assay, and the clinical utility of this latter class of mutations needs to be further defined. Among the UCVs, splicing defects may be difficult to detect given the recent realization that certain silent coding sequence mis-sense mutations can disrupt premRNA processing, with dramatic effects on the structure of the gene product (21). In addition, aberrant splicing can also occur, with mis-sense changes affecting 5′ and 3′ splice sites other than the invariant GT and AG dinucleotides, respectively (27–29). Because most mutation screening is performed using genomic DNA as templates, atypical splicing mutations are likely underestimated.

The atypical PKD2 splice variant IVS8 + 5G→A we described in TOR101 has been recently reported by Rossetti et al. and was predicted to be possibly pathogenic on the basis of conservation of the genomic sequence (8). By linkage, segregation, and RT-PCR studies we have provided further and stronger evidence for the pathogenicity of this mutation. Indeed, similar atypical splice mutations have been documented in other diseases. For example, IVS51 + 5G→A in CDH23 caused Usher syndrome type 1D through inframe skipping of exon 51 (27). In addition, several atypical splice mutations, confirmed by RT-PCR, have been reported for PKD1 and PKD2 (see Table 1) (8,9,11–13). PremRNA splicing is a complex cellular process in which the removal of introns is performed by the complex interactions of the splicosome, cis-regulatory splice enhancers and silencers, and transregulatory factors (28,29). Using ESEfinder, we found that the IVS8 + 5G→A transition might result in the loss of one of three SRp55 motifs and could therefore diminish the affinity of SRp55 for the authentic 5′ splice site in favor of other cryptic splice sites. We also assessed the relative strengths of all of the donor and acceptor splice sites used in the normal and splice variants. The donor sites were scored based on their affinity for the U1 snRNA 5′ terminus using a 9 bp motif from position −3 to + 6 relative to the GT sequence (21,30). We found that the IVS8 + 5G→A transition appeared to have a dramatic effect in reducing the strength of the authentic 5′ splice site (Table 4), which allows for other cryptic sites (e.g., A and B) with relatively high strengths to compete for splicing. This may help explain why the G nucleotide is highly conserved at the position IVS8 + 5 in approximately 80% of the 5′ splice sites examined across five different species (24). Although the splice site scores predicted by the three algorithms used in this study were generally concordant, they did not always predict the observed frequencies of splice site usage, suggesting that these models only partially account for complex splicing process.

In summary, we identified 17 atypical PKD1 and PKD2 splice mutations from the published literature. We found that bioinformatic analysis can be useful for evaluating the pathogenicity of these mutations although RT-PCR is needed to provide the definitive proof. In a multiplex family with ADPKD, we identified an atypical PKD2 splice mutation IVS8 + 5G→A, which co-segregated with the affected members of this PKD2-linked family. Bioinformatic analysis further showed that this mutation is predicted to disrupt an evolutionary conserved binding motif for an exonic splice enhancer. RT-PCR showed that the mutation resulted in six splice variants through activation of different donor and acceptor cryptic splice sites with all mutant gene products predicted to terminate with premature stop codons. Taken together, our data provide definitive evidence that IVS8 + 5G→A is a pathogenic mutation for PKD2 and highlights the importance of functional analysis of UCVs in molecular diagnostic testing.

Disclosures

None.

Acknowledgments

We are indebted to all of the participating members of the study family (TOR101). This work was supported by a grant from the Kidney Foundation of Canada (to Y.P.).

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • K.W. and X.Z. contributed equally to this study.

  • Received February 27, 2008.
  • Accepted October 21, 2008.
  • Copyright © 2009 by the American Society of Nephrology

References

  1. ↵
    Igarashi P, Somlo S: Genetics and pathogenesis of polycystic kidney disease. J Am Soc Nephrol13 :2384– 2398,2002
    OpenUrlFREE Full Text
  2. ↵
    Ong A, Harris P: Molecular pathogenesis of ADPKD: The polycystin complex gets complex. Kidney Int67 :1234– 1237,2005
    OpenUrlCrossRefPubMed
  3. ↵
    Ravine D, Walker RG, Gibson RN, Forrest SM, Richards RI, Friend K, Sheffield LJ, Kincaid-Smith P, Danks DM: Phenotype and genotype heterogeneity in autosomal dominant polycystic kidney disease. Lancet340 :1330– 1333,1992
    OpenUrlCrossRefPubMed
  4. ↵
    Peters DJ, Sandkuijl LA: Genetic heterogeneity of polycystic kidney disease in Europe. Contrib Nephrol97 :128– 139,1992
    OpenUrlCrossRefPubMed
  5. ↵
    Hateboer N, v Dijk MA, Bogdanova N, Coto E, Saggar-Malik AK, San Millan JL, Torra R, Breuning M, Ravine D: Comparison of phenotypes of polycystic kidney disease types 1 and 2. European PKD1-PKD2 Study Group. Lancet353 :103– 107,1999
    OpenUrlCrossRefPubMed
  6. ↵
    Pei Y: Diagnostic approach in autosomal dominant polycystic kidney disease. Clin J Am Soc Nephrol1 :1108– 1114,2006
    OpenUrlAbstract/FREE Full Text
  7. ↵
    Rossetti S, Chauveau D, Walker D, Saggar-Malik A, Winearls C, Torres V, Harris P: A complete mutation screen of the ADPKD genes by DHPLC. Kidney Int61 :1588– 1599,2002
    OpenUrlCrossRefPubMed
  8. ↵
    Rossetti S, Consugar M, Chapman A, Torres V, Guay-Woodford L, Grantham J, Bennett W, Meyers C, Walker D, Bae KT, Qin Zhang, Thompson P, Miller P, Harris P, the CRISP Consortium: Comprehensive molecular diagnostics in autosomal dominant polycystic kidney disease. J Am Soc Nephrol18 :2143– 2160,2007
    OpenUrlAbstract/FREE Full Text
  9. ↵
    Garcia-Gonzalez M, Jones J, Allen S, Palatucci C, Batish S, Seltzer W, Lan Z, Allen E, Qian F, Lens X, Pei Y, Germino G, Watnick T: Evaluating the clinical utility of a molecular genetic test for polycystic kidney disease. Molecular Genetics Metabolism92 :160– 167,2007
    OpenUrlCrossRef
  10. ↵
    Pei Y, Obaji J, Dupuis A, Paterson AD, Magistroni R, Dicks E, Parfrey P, Cramer B, Coto E, Torra R, San Millán JL, Gibson R, Breuning M, Peters D, Ravine D: Unified ultrasonographic diagnostic criteria for autosomal dominant polycystic kidney disease. J Am Soc Nephrol (In press).
  11. ↵
    Rossetti S, Strmecki L, Gamble V, Burton S, Sneddon V, Peral B, Roy S, Bakkaloglu A, Komel R, Winearls C, Harris P: Mutation analysis of the entire PKD1 gene: Genetic and diagnostic implications. Am J Hum Genet68 :46– 63,2001
    OpenUrlCrossRefPubMed
  12. ↵
    Peral B, Gamble V, Strong C, Ong A, Sloane-Stanley J, Zerres K, Winearls C, Harris P: Identification of mutations in the duplicated region of the polycystic kidney disease 1 gene (PKD1) by a novel approach. Am J Hum Genet60 :1399– 1410,1997
    OpenUrlPubMed
  13. ↵
    Peral B, Gamble V, San Millan JL, Strong C, Sloane-Stanley J, Moreno F, Harris P: Splicing mutations of the polycystic kidney disease 1 gene induced by intronic deletion. Hum Mol Genet4 :569– 574,1995
    OpenUrlCrossRefPubMed
  14. ↵
    Roca X, Sachidanandam R, Krainer AR: Intrinsic difference between authentic and cryptic 5≪ splice sites. Nucleic Acids Res31 :6321– 6333,2003
    OpenUrlCrossRefPubMed
  15. ↵
    Pei Y, Paterson AD, Wang KR, He N, Hefferton D, Watnick T, Germino GG, Parfrey P, Somlo S, St. George-Hyslop P: Bilineal disease and trans-heterozygotes in autosomal dominant polycystic kidney disease. Am J Hum Genet68 :355– 363,2001
    OpenUrlCrossRefPubMed
  16. ↵
    Germino GG, Weinstat-Saslow D, Hammelbauer H, Gillespie G, Somlo S, Wirth B, Barton N, Harris KL, Frischauf AM, Reeders ST: The gene for autosomal dominant polycystic disease lies in a 750-kb CpG-rich region. Genomics13 :144– 151,1992
    OpenUrlCrossRefPubMed
  17. ↵
    San Millán JL, Viribay M, Peral B, Martinez I, Weissenbach J, Moreno F: Refining the localization of the PKD2 locus on chromosome 4q by linkage analysis in Spanish families with autosomal dominant polycystic kidney disease type 2. Am J Hum Genet56 :248– 253,1995
    OpenUrlPubMed
  18. ↵
    Kruglyak L, Daly M, Reeve-Daly M, Lander E: Parametric and nonparametric linkage analysis: A unified multipoint approach. Am J Hum Genet58 :1347– 1363,1996
    OpenUrlPubMed
  19. ↵
    Lathrop M, Lalouel JM: Easy Calculations of LOD scores and genetic risks on small computers. Am J Hum Genet36 :460– 465,1984
    OpenUrlPubMed
  20. ↵
    Schäffer A, Gupta S, Shriram K, Cottingham Jr. RW: Avoiding recomputation in linkage analysis. Hum Hered44 :225– 237,1994
    OpenUrlCrossRefPubMed
  21. ↵
    Cartegni L, Wang J, Zhu Z, Zhang MQ, Krainer AR: ESEfinder: A web research to identify exonic splicing enhancers. Nucleic Acids Res31 :3568– 3571,2003
    OpenUrlCrossRefPubMed
  22. ↵
    Shapiro MP, Senapathy P: RNA splice junctions of different classes of eukaryotes sequence statistics and functional implications in gene expression. Nucleic Acids Res15 :7155– 7174,1987
    OpenUrlCrossRefPubMed
  23. ↵
    Burke C, Karlin S: Prediction of complete gene structures in human genomic DNA. J Mol Biol268 :78– 94,1997
    OpenUrlCrossRefPubMed
  24. ↵
    Reese MG, Eeckman FH, Kul D, Haussler D: Improved splice site detection in gene. J Comput Biol4 :311– 323,1997
    OpenUrlCrossRefPubMed
  25. ↵
    Yeo G, Burge CB: Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol11 :377– 394,2004
    OpenUrlCrossRefPubMed
  26. ↵
    Carmel I, Tal S, Vig I, Ast G: Comparative analysis detects dependencies among the 5′ splice-site positions. RNA10 :828– 840,2004
    OpenUrlAbstract/FREE Full Text
  27. ↵
    Bolz H von Brederlow B, et al: Mutation of CDH23, encoding a new member of the cadherin gene family, causes Usher syndrome type 1D. Nat Genet27 :108– 112,2001
    OpenUrlCrossRefPubMed
  28. ↵
    Krawczak M, Reiss J, Cooper DN: The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: Causes and consequences. Hum Genet92 :41– 54,1992
    OpenUrl
  29. ↵
    Wang GS, Cooper T: Splicing in disease: Disruption of the splice code and the decoding machinery. Nature Rev Genet8 :749– 761,2007
    OpenUrlCrossRefPubMed
  30. ↵
    Roca X, Olson A, Rao A, Enerly E, Kristensen V, Borresen-Dale AL, Andresen B, Krainer A, Sachidanandam R: Features of 5′-splice-site efficiency derived from disease-causing mutations and comparative genomics. Genome Res18 :77– 87,2008
    OpenUrlAbstract/FREE Full Text
PreviousNext
Back to top

In this issue

Clinical Journal of the American Society of Nephrology
Vol. 4, Issue 2
February 2009
  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
View Selected Citations (0)
Print
Download PDF
Sign up for Alerts
Email Article
Thank you for your help in sharing the high-quality science in CJASN.
Enter multiple addresses on separate lines or separate them with commas.
Evidence for Pathogenicity of Atypical Splice Mutations in Autosomal Dominant Polycystic Kidney Disease
(Your Name) has sent you a message from American Society of Nephrology
(Your Name) thought you would like to see the American Society of Nephrology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Evidence for Pathogenicity of Atypical Splice Mutations in Autosomal Dominant Polycystic Kidney Disease
Kiarong Wang, Xiao Zhao, Shelly Chan, Onur Cil, Ning He, Xuewen Song, Andrew D. Paterson, York Pei
CJASN Feb 2009, 4 (2) 442-449; DOI: 10.2215/CJN.00980208

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Request Permissions
Share
Evidence for Pathogenicity of Atypical Splice Mutations in Autosomal Dominant Polycystic Kidney Disease
Kiarong Wang, Xiao Zhao, Shelly Chan, Onur Cil, Ning He, Xuewen Song, Andrew D. Paterson, York Pei
CJASN Feb 2009, 4 (2) 442-449; DOI: 10.2215/CJN.00980208
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like

Jump to section

  • Article
    • Abstract
    • Materials and Methods
    • Results
    • Discussion
    • Disclosures
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data Supps
  • Info & Metrics
  • View PDF

More in this TOC Section

  • Familial C3 Glomerulopathy Associated with CFHR5 Mutations: Clinical Characteristics of 91 Patients in 16 Pedigrees
  • Clinical Utility of Genetic Testing in Children and Adults with Steroid-Resistant Nephrotic Syndrome
  • Recurrent Deep Intronic Mutations in the SLC12A3 Gene Responsible for Gitelman's Syndrome
Show more Hereditary Disease

Cited By...

  • Polycystic Kidney Disease without an Apparent Family History
  • Google Scholar

Similar Articles

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Articles

  • Current Issue
  • Early Access
  • Subject Collections
  • Article Archive
  • ASN Meeting Abstracts

Information for Authors

  • Submit a Manuscript
  • Trainee of the Year
  • Author Resources
  • ASN Journal Policies
  • Reuse/Reprint Policy

About

  • CJASN
  • ASN
  • ASN Journals
  • ASN Kidney News

Journal Information

  • About CJASN
  • CJASN Email Alerts
  • CJASN Key Impact Information
  • CJASN Podcasts
  • CJASN RSS Feeds
  • Editorial Board

More Information

  • Advertise
  • ASN Podcasts
  • ASN Publications
  • Become an ASN Member
  • Feedback
  • Follow on Twitter
  • Password/Email Address Changes
  • Subscribe to ASN Journals

© 2022 American Society of Nephrology

Print ISSN - 1555-9041 Online ISSN - 1555-905X

Powered by HighWire