Open Access

Genetic variation of the ABC transporter gene ABCC1 (Multidrug resistance protein 1 – MRP1) in the Polish population

  • Marcin Słomka1,
  • Marta Sobalska-Kwapis1,
  • Małgorzata Korycka-Machała2,
  • Grzegorz Bartosz3,
  • Jarosław Dziadek2 and
  • Dominik Strapagiel1Email author
BMC Genetics201516:114

https://doi.org/10.1186/s12863-015-0271-3

Received: 14 April 2015

Accepted: 11 September 2015

Published: 23 September 2015

Abstract

Background

Multidrug resistance-associated protein 1 (MRP1), encoded by the ABCC1 gene, is an ATP-binding cassette transporter mediating efflux of organic anions and xenobiotics; its overexpression leads to multidrug resistance. In this study, 30 exons (from 31 in total) of the ABCC1 gene as well as and their flanking intron sequences were screened for genetic variation, using the High Resolution Melting (HRM) method, for 190 healthy volunteers representing the Polish population. Polymorphism screening is an indispensable step in personalized patient therapy. An additional targeted SNP verification study for ten variants was performed to verify sensitivity of the scanning method.

Results

During scanning, 46 polymorphisms, including seven novel ones, were found: one in 3’ UTR, 21 in exons (11 of them non-synonymous) and 24 in introns, including one deletion variant. These results revealed some ethnic differences in frequency of several polymorphisms when compared to literature data for other populations. Based on linkage disequilibrium analysis, 4 haplotype blocks were determined for 9 detected polymorphisms and 12 haplotypes were defined. To capture the common haplotypes, haplotype-tagging single nucleotide polymorphisms were identified.

Conclusions

Targeted genotyping results correlated well with scanning results; thus, HRM is a suitable method to study genetic variation in this model. HRM is an efficient and sensitive method for scanning and genotyping polymorphic variants. Ethnic differences were found for frequency of some variants in the Polish population compared to others.

Thus, this study may be useful for pharmacogenetics of drugs affected by MRP1-mediated efflux.

Keywords

High resolution melting ABCC1 MRP Gene scanning Genotyping

Background

The human multidrug resistance-associated protein 1 (MRP1) is a member of the ATP-binding cassette (ABC) transporter superfamily and is encoded by the ABCC1 gene [1]. MRP1 was first described by Cole et al. [2] who cloned the overexpressed transporter from lung cancer cell line H69AR. The gene is mapped to chromosome 16p13.1. Its length is around 200.000 base pairs; the gene contains 31 exons encoding a protein of 1,531 amino acids, with molecular weight of 190 kDa [3, 4]. The protein has 3 hydrophobic transmembrane domains (TMDs), also called membrane-spanning domains (MSDs), named TMD0 (N-terminal), TMD1 (middle) and TMD2 (C-terminal). They contain 17 transmembrane helices (TM), 5 in TMD0 and 6 in both TMD1 and TMD2. The intracellular region of the protein contains 2 hydrophilic nucleotide binding domains (NBDs), NBD1 associated with TMD1 and NBD2 associated with TMD2 [5, 6]. They are both involved in binding and hydrolyzing ATP, which is indispensable for substrate transport. Each of NBDs contains highly conserved motifs: Walker A motif which binds the β-phosphate of ATP and Walker B motif that interacts with Mg2+ ions. There is also a third, 13 amino acid sequence motif, between Walker A and Walker B called the ABC signature [1, 7].

MRP1 is ubiquitously expressed in many human tissues and physiological barriers like blood–brain or blood-testis barriers. High levels of MRP1 expression were detected in lung, kidney, heart, testis, placenta, adrenal glands and skeletal muscles, while lower levels of expression were found in intestine, colon, brain, peripheral blood mononuclear cells and liver [1, 3, 8]. With the exception of brain cells, MRP1, in contrast to other ABC transporters, is expressed at the basolateral membrane of polarized epithelial cells where it plays a protective role against xenobiotics and their metabolites [3, 9].

Many drugs are good substrates for MRP1, so its overexpression leads to multidrug resistance (MDR), especially during cancer chemotherapy. This effect can be observed in many types of cancer cells including solid tumors (lung cancer, breast cancer, gastric and colon carcinomas, melanoma, prostate cancer, neuroblastoma) as well as in various types of leukemias [3, 9]. MRP1-overexpressing cells are capable of transporting a large group of substrates, including anticancer drugs like folate-based antimetabolites, anthracyclines, Vinca alkaloids, antiandrogens and even some inorganic anions such as arsenite and arsenate. MRP1 is also an active transporter of a broad range of endogenous compounds including glutathione (GSH), glucuronate and sulfate-conjugates of organic anions, such as inflammatory mediator leukotriene C4 or estradiol-17-β-D-glucuronide (E217βG). Therefore, MRP1 plays a crucial role in efflux of organic anions, anionic drugs, xenobiotic conjugates and additionally in the maintenance of redox balance by participation in cellular response to oxidative stress [3, 7, 911].

A large number of single nucleotide polymorphisms (SNPs) in ABCC1 gene were detected so far in studies of various populations, especially Asian and Caucasian. Saito et al. [12] reported 779 genetic variations in eight ABC genes in Japanese population, 95 among them concerning ABCC1. Subsequently, Fukushima-Uesaka et al. [13], screening ABCC1 in the same population, identified 86 genetic variations including 31 novel ones. Similar screening of Chinese population revealed 32 SNPs [14]. Leschziner et al. [15] screened five ABC transporter genes in Caucasian individuals and detected 221 variants, including 61 for ABCC1, among them 22 novel for this gene. They identified also large blocks of high linkage disequilibrium and low haplotype diversity across all the genes in the examined population. Comparative study including polymorphisms from four different populations: Chinese, Malay, Indian and Caucasian showed that apart from population-specific SNPs, only 6 of 71 detected SNPs altered amino acid sequence, so ABCC1 is considered to be a highly conserved gene [16]. Majority of previously reported genetic variations were found in intron sequences. However, there is evidence in literature that some SNPs change protein functioning and effectiveness of cancer chemotherapy. Such cases were reported for neuroblastoma [17], breast cancer [18], ovarian cancer [19] and other serious diseases like: chronic obstructive pulmonary disease (COPD) [20, 21] cystic fibrosis [22] and major depression [23].

Previously mentioned studies have shown importance of ABCC1 polymorphisms and its genetic variation. These data can have great relevance for pharmacogenetics in personalized patient therapy. Based on the data described above for several populations, there exist ethnic differences in the frequency of ABCC1 polymorphic variants. Some of them are so strongly linked to each other that they are always inherited together, forming haplotypes. Haplotype is a combination of polymorphic variants inherited on the same chromosome and sometimes has stronger clinical relevance for drug response or adverse events than a single polymorphism [24]. Moreover, there is a large haplotype diversity across different populations [25]. Therefore, prior comprehensive analysis of the whole gene is required in the specific population before application of genetic variation data in pharmacotherapy.

Our previous studies performed during the project TESTOPLEK in which we have examined genetic variation of multidrug transporter genes in the Polish population indicated that ABCC1 polymorphisms were present at significant level in a sample of Polish individuals. However, various SNPs in the ABCC1 gene differed significantly in their frequency in tested DNA samples [26]. This prompted us to verify the whole coding sequence of ABCC1 gene. We conducted high throughput analysis and screened 30 exons (from 31 in total) of ABCC1 gene for polymorphism variants in the Polish population (which is exclusively Caucasians). This is the first study of genetic variation of ABCC1 gene in the Polish population.

Methods

Materials and genomic DNA samples

Human genomic DNA samples were derived from anonymous Polish unrelated volunteers who declared to be healthy. Samples were randomly selected from the “normal Polish population” genetic collection at the Biobank Lab, Department of Molecular Biophysics, University of Lodz. Genetic material for this collection was sampled in 2011 – 2012 within the EU-funded TESTOPLEK project. All subjects gave their written informed consent to participate in the study. This study was approved by the relevant regional ethical committee (Research Bioethics Commission, University of Lodz - Decision no. 8/KBBN-UŁ/II/2014 and Statement of the Research Bioethics Commission, University of Lodz from 17th of June, 2010) and all procedures were performed in accordance with the Declaration of Helsinki.

Saliva was collected into Oragene OG-500 DNA collection/storage receptacles (DNA Genotek, Kanata, Canada) and genomic DNA was subsequently isolated by the MagNA Pure LC DNA Isolation Kit - Large Volume (Roche, Basel, Switzerland) with final concentration normalized to 200 pg/μl [27]. A total of 190 samples were enrolled in the scanning study and other 380 in genotyping.

Screening of ABCC1 by High Resolution Melting (HRM) Method

Investigation of ABCC1 genetic variation was conducted using High Resolution Melting method. The method is based on precise measurements of DNA melting profile. The dsDNA melting temperature depends on length and nucleotide composition of the PCR product. Even a single base variation generates a different melting curve, and many variants can be detected in this way. The list of primers used for HRM scanning of all the areas is presented in Table 1. The single reaction mixture (10 μl) was prepared using Janus® Automated Workstation (Perkin Elmer Inc., Waltham, USA) and composed of GoTaq® Colorless Master Mix (2×) (Promega, Madison, USA), LC Green Plus® dye (10x) (BioFire Defense Inc., Salt Lake City, USA), 0.5 μl of 10 μM primers mixture, 3 μl DNA, and filled up to the final volume with water. Reaction was performed on 384-micro well plates using CFX384™ real-time PCR system (Bio-Rad Laboratories Inc., Hercules, USA) on duplicate samples. The reaction conditions were as follows: initial denaturation at 95 °C for 3 min, 50 amplification cycles of denaturation at 95 °C for 30 s and annealing at specific temperature depending on the primers used, for 30 s. The plate was read after each cycle. Directly afterwards, melting curve was determined, the plate being incubated at 90 °C for 60 s, 40 °C for 60 s and from 65 °C to 95 °C with an increment of 0.2 °C for 10 s with plate reading. The obtained data were analysed with the Bio-Rad Precision Melt Analysis Software, version 1.2 (Bio-Rad Laboratories Inc., Hercules, USA). Based on HRM melting curve analyse, confirmation of genetic variation for every melting cluster was obtained by direct sequencing method for several samples selected from each cluster (or all samples from clusters with less than 4 samples). Study workflow design for all described methods is presented in Fig. 1.
Table 1

Pairs of primers used for HRM screening, sequencing and genotyping ABCC1

 

Exon

Number of scanned bp upstream from the exon (without primer)

Forward primer (5’ → 3’)

Reverse primer (5’ → 3’)

Number of scanned bp downstream from the exon (without primer)

Size (bp) including primers

Scanning

1

104*

CAGCGCTAGCGCCAGCAG

CCCGGTGCTTTCCCTCCC

49

234

2

31

GTCCTCTGGGGTGTGTCCT

TCTGAATGTAGCCTCGGTCA

 

201

  

GCTTTCAGAACACGGTCCTC

TTATCACCAACCACCACTCC

52

203

3

20

GGGCGGTCTGTTGTAGGATA

CCACTGAGCCCAGCAGAT

31

215

4

129

AAGCTGAGGCAGGAGAATCA

AAGGTAGCAAGCAGCTGAGG

 

163

  

AGCCTGGGTGACAAGAGTGA

TGGATCTCAGGATGGCTAGG

 

189

  

ATGCTCACTTTCTGGCTGGT

GGCAACGCATGACTTCTACA

72

170

5

27

CAGCCCCAGAATGTGATCTT

CACACACACGCACACACACT

19

212

6

20

TTTCCCTCTTCCTCCCAAAC

AGCTGAGCATGTTCATTCGTT

59

182

7

33

CCCTCCTCCTGTCATTGACTC

GAGTGGAAAGGAGGTGACCA

 

240

  

GGAACAAGTCGTGCCTGTTT

ATCTTGCCCAGAACCACAAA

104

184

8

74

TTCCCTGAAGGGTGACATTC

AGGGGTTCCACTCCTTCTGT

 

219

  

GGAGGCTTTGATCGTCAAGT

AAAGCCAAGGAGGGAAAATG

29

199

9

14

AGTCATTCCAGGCCCTCTCT

TGACGAAGCAGATGTGGAAG

 

165

  

TACACCGTGCTGCTGTTTGT

CAGGAGGGGATGTGGAAGT

33

175

10

27

TCTTCAGCTGCCACACTCAC

GCCACAGGAGGTAGAGAGCA

 

207

  

TCTGTGGACGCTCAGAGGTT

CTGCCCACACGTAGAAGTCC

38

157

11

21

TCTTCTGTCTGGTGAGTGATGAA

TGGACTAAAATCCTCATGGAGAG

49

209

12

41

GTTGAGTGATGGGCTGATCC

GGCAGACTTCTTCAGCACCT

 

217

  

GCATTCAAGGACAAGGTGCT

CCTGGGCAACATAGTGACCT

75

206

13

62

GTCTCCAGGGCCTGTCACT

ACCGGAGGATGTTGAACAAG

 

184

  

CCAGACAGCCTTCGTGTCTT

GCTTTCGTGGCCTAGAACC

42

149

14

49

TTAACCTTGGTTGGTTTTGC

CAAGTAGGGAGGACCCAGTG

51

228

15

41

GTTCTGTGCATGTGGAGTCG

GGCTCTCTCCACACCATCAC

22

179

16

73

TTCTCTACTTGGGGTAAATTGAGG

CTGAGAGCAGGGACGACTTT

 

177

  

ATCCCCGAAGGTGCTTTG

GCTTTTCCTCAGACCACCAG

37

171

17

12

CTGTCTCACCTCGTTCTCCA

CTGACCTTCTCGCCAATCTC

 

214

  

CTCCCAGACCTGGAAATCCT

CAGCCCACTGAGATTGTGAG

60

134

18

20

ATTTCCCAGGAAACCCACTC

GGCCAATCACATTTTCAAAGA

 

185

  

CTTCGATGATCCCCTCTCAG

CCTAAAGGGGACACGTTCTG

55

166

19

26

CTCACACATGTGCACTCACG

TCTGTGCTGGCATAGGTACG

 

204

  

GCCAAGCTAGGCAGTCTCAC

GGCAAGTAGCTCATGCTGTG

 

99

  

TCTGAGATGGGCTCCTACCA

CAACCTCAAAGAGGCCAATG

56

182

20

48

TTGTTGCCCTTGGTTTTAGC

CTCCTCTTGTCCTCCCACTG

44

223

21

20

GCATCTGTACGGTTGACACC

TGGGCGCTCTATAAACTCCA

32

228

22

25

GGTGCGTGCATGTGCTAAG

ATGGGGTCATCAGTCCAGAG

 

184

  

GGCTTCCAACTATTGGCTCA

CAAAACGCTGAGGACTCTAAGG

72

204

23

21

CCCTCTCTGCATTGTGGAGT

GTCCACTGTGTCCAGCTCCT

 

214

  

ATGAGCTTCTTTGAGCGGAC

GGCTTGTCCCGAACACTAAG

14

244

24

22

GAGGGAACCTTCATCAACTCC

TCTGGTTCTCGTCCACCTTC

 

212

  

CCCATTTCAACGAGACCTTG

GGAAAAGATCGAGGCAAAGA

55

208

25

26

AGAGCTGACTCCATGCCTGT

GGCTCCCCCATTAACAGACT

32

225

26

24

CGCCCGCTTACTCTAGAAAT

CCAAAGACCAAGAGGTCCAG

18

184

27

45

GGGGAGTCACAGCTTTACCA

GGGAATGGGTGAGGGAAT

18

248

28

6

ACACCTGGGCCCTTCTGTC

TGATCTCTCCTTCGGCAGAC

 

113

  

CTGGGCTTATTTCGGATCAA

CAGTGCAATCATAGGGCTTG

40

174

29

55

AGTCCTTAGGTCGCCTCCAT

CCTTCTGCACATTCATGGTC

 

227

  

CCCACCTGAAGGACTTCGT

CAAACACCCCTACCGAGATG

47

143

30

74

GTGTCTCCTTTCGCTTCTCC

ATGGTGGACTGGATGAGGTC

 

217

  

CCTGCTGAGGAAGACGAAGA

AGGCAAACTCCCAAAGCCTA

25

204

31

15

CCCTTCCCCTCATGTCTGTA

ATAGGCCCTGCAGTTCTGAC

21**

185

Sequencing

1

 

-

-

 

-

2

 

TCTCGAACTCCCAGCCTAAA

TAAGCGGCAGAGCAAAGATT

 

749

 

3

 

GAGCATGGTGACCAGACAAA

CTCCTGACCTCAGGTGATCC

 

638

4

 

GTGGTGAAACCCCGTCTTTA

CCTTGGAGCAACACAGACAA

 

604

5

 

CCCGAGTAGCTGGGATTACA

ACCATGCCAAGTGAGAAACC

 

606

6

 

GGGTTGTTGTGGGGATAGAA

AAGCAAAAGCAGTCCAGCAT

 

622

7

 

CTGCATGACCCAACAGAATG

CAAACTCCCGACCAAGTGAT

 

651

8

 

CTTTGACTCTGCCTTCCCTG

TAGGGGAGCCTAGTGGGTTT

 

691

9

 

AGCAATTAGTGCACAACCCC

AAGACATCTACACGCCCACC

 

613

10

 

ACAGCCTGGAAGCGTAGAAA

AACTGCCAGGGAGCTCTACA

 

664

11

 

GAGCGTGGACCTGCTTATTC

CACCGGCCTACAAGGTCTAA

 

608

12

 

GATGTTGAGTGATGGGCTGA

CCTGGGCAACATAGTGACCT

 

363

13

 

ATACTGCCCCAGGTTTTTCC

AAAAAGGATAGGAATGGCGG

 

611

14

 

CTGGGGAAACCCTTGAAAGT

CCAAGGGAAAGAAATGCAAG

 

263

15

 

CCCAAGATGATCGTCCAGAT

TACGCAAGCCAAGAATCTCC

 

632

16

 

TCTCCTTTCAGACCTTGCGT

CCTGCCTTCTAGGACAGCAC

 

676

17

 

ACTCTTTGGAAGCAGGGGTT

AGTGAGACCTGAGCCACACC

 

693

18

 

CTCAAGCCATCTTCCCACTC

GAAGCCAGCCCTGTGACTTA

 

717

19

 

CATGTCCCACCTTCAGACCT

CCAGCTTAACTCCGTGCTTC

 

748

20

 

CTGCACAGTTGCAAAGCACT

GCCAAGAGACCTGAGCAAAG

 

620

21

 

CCGTCTCTTATGCCATGGTT

GGTTCAAATCCCAGTTCTGC

 

461

22

 

GCATGTGCTAAGCTGCCTTAT

TGTAAAATGGGCACACTGGA

 

485

23

 

CTCAAGTGATCCACCCACCT

TGCTTCAAAAGCACCACACT

 

617

24

 

GAGGTTAGCACTTTGCAGCC

ATGATTCATCTGTCCTCCGC

 

791

25

 

AAGGCAAGCTTCAGATTCCA

AAAGCATTCCCCTCGTACCT

 

674

26

 

AAATGCCACGTGACTCTTCC

CCTGGTGAGGTATCCAGCTC

 

242

27

 

AGGGGACAGAGGGACACAG

AAATCTGTGGGGCTCATTTG

 

618

28

 

TCATGATGGGAAACTCACCA

GAACGATGAAGTAGGGCCAA

 

669

29

 

AGACAGGGTGTTGCCATGTT

TCAACTGAATGGAGCTGGTG

 

689

30

 

GGTGTGAGCTGCTGTTTTGA

AAGAGAGGATCCACCCACTG

 

556

31

 

CCATGATTGATGTGGGGTAG

AAGCACCAGGAAACCACTTG

 

640

Genotyping

SNP

Forward primer (5’ → 3’)

Reverse primer (5’ → 3’)

 

Size (bp) including primers

 

c.128G > C

 

TCAGAACACGGTCCTCG

ACAGGCCCAGAGGTAAAA

 

49

c.616-7942A > G

 

AGCAACAGGGCAAACAAATC

GACGGAGCCTAAATGTCCAG

 

76

c.816G > A

 

CTTGTGTTCCAGGCAGC

GCAGGATCCTTGGAGGAGTA

 

51

c.825 T > C

c.1057G > A

 

GCTCCTTTGCAGGTTGCTCATC

GGGGCCTTCGTGTCATTCA

 

48

c.1062 T > C

 

GGTTGCTCATCAAGTTCGTGA

GTCTGGGGCCTTCGTGTC

 

41

c.1218 + 8A > G

 

CATGAGGATCAAGACCGCTG

GTGGAAGTCGGGCCACAT

 

84

c.1299G > T

 

CCAGAAAATCCTCCACGGT

GTACGTGGCCAAGTCCATGA

 

80

c.1684 T > C

 

GTTCTGCTTGCAGGAGGCC

TTCAGGGGAAACCGGAGGAT

 

125

c.1704C > T

c.2012G > T

 

TCACCTTCTCCATCCCCG

CACTTTGTCCATCTCAGCCA

 

104

c.2168G > A

 

CAGGCCTGGATTCAGAATGA

ATGGTTCCTCCAGCTGACAT

 

67

c.2581G > A

 

CAGGAGCTGCTGGCTCG

TCTGTGCTGGCATAGGTACG

 

62

c.2965G > A

 

TCCTTTTCATGTGTAACCATGTG

CAATAGTTGGAAGCCAGCG

 

46

c.3140G > C

 

CTACTCCATGGCCGTGTCC

AGGATGCTGTGCAGCAGGT

 

75

c.3436G > A

 

GAGGTTCTACGTGGCTTCC

GAATAGACCGGGGAGCG

 

69

c.4002G > A

 

GACAAGTCCGGATGCCAG

CGAAATAAGCCCAGGGTC

 

112

c.4009A > G

 

AGCTGGGAAGTCGTCCCT

TGATCTCTCCTTCGGCAG

 

65

104* - upstream from the START codon. 21** - downstream from the STOP codon. Bold variants means these ones which genetic variation was detected during genotyping method

Fig. 1

Study flow chart

HRM genotyping

Independently of the scanning study, we performed an additional genotyping study. We focused on highly polymorphic regions of the ABCC1 gene existing in databases, often examined in recent publications. We also tested some interesting variants observed in literature that may have clinical significance. The selection provided us with 17 SNPs to check. To identify common genetic variation of selected SNPs in the ABCC1 gene, we used the HRM analysis method according to conditions described above. Sequences of primers used for genotyping are described in Table 1. Based on HRM melting curve analysis, confirmation of genetic variation for every melting cluster was obtained by direct sequencing method for several samples selected from each cluster (or all samples from clusters less than 3 samples). We used the data obtained from genotyping to compare them with HRM scanning results (comparison of minor allele frequencies - MAFs) and thereby to validate the accuracy of the screening method.

DNA sequencing

For sequencing, selected samples were prepared by PCR. Reaction mixture filled with water to a final volume of 50 μl per sample was composed using GoTaq® Green Master Mix (2×) (Promega, Madison, USA), 5 μl of 10 μM primers mix and 5 μl DNA. Reaction conditions were as follows: an initial denaturation at 95 °C for 3 min, 45 amplification cycles of denaturation at 95 °C for 30 s, annealing at specific temperature depending on the primers used for 30 s and extension at 72 °C for 45 s, followed by final extension at 72 °C for 5 min (list of primers for exon sequencing is shown in Table 1). Afterwards, PCR products were purified using NucleoSpin® Gel and PCR Clean-up kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany). DNA concentration and size of PCR product was determined by agarose electrophoresis of 2 μl of each sample. Based on the intensity of bands the samples were diluted from 5 to 50 times and applied for sequencing reaction using BigDye Terminator V3.1 (Applied Biosystems, USA) according to the manufacturer's protocol. The PCR-sequencing product was purified using BigDye X-Terminator kit following the manufacturer's protocol (Applied Biosystems, USA). Further, 30 μl of each purified sample was applied to 96 wells of titration plates and analyzed in 3500 Genetic Analyzer (Applied Biosystems, USA).

Polymorphism detection

The ABCC1 genomic DNA sequence (NG_028268.1) was obtained from GenBank (http://www.ncbi.nlm.nih.gov) and used as a reference sequence during analysis of sequencing results by CodonCode Aligner software (CodonCode Corporation, Centerville, USA). Sequencing results of selected samples were compared with respective HRM clusters. Based on this, genotype for each melting cluster was established and genetic variation for each sample was verified.

Availability of supporting data

For each of the polymorphisms detected in this study in ABCC1 gene, the following parameters were assigned: dbSNP IDs (rs numbers) (http://www.ncbi.nlm.nih.gov/SNP/), nucleotide position within or relative to the coding sequence based on the NM_004996.3, and amino acid position in the protein for SNPs in the coding sequence based on the reference sequence NP_004987.2. These data were obtained from GenBank (http://www.ncbi.nlm.nih.gov) and were used in this paper as the nomenclature of variants.

The obtained data as supporting materials are presented in details in Additional files 1, 2, 3 and 4.

Prediction of impact of SNPs upon protein functionality

Using PolyPhen-2 v2.2.2r398 tool (http://genetics.bwh.harvard.edu/pph2/), we performed automatic prediction of effects of missense SNPs and amino acid substitutions for protein functionality and structure. PolyPhen-2 uses damaging alleles datasets, like HumDiv or HumVar, to calculate naïve Bayes posterior probability and to classify a mutation deleterious effect as benign, possibly damaging or probably damaging. This ternary classification depends on thresholds of false positive rate (FPR) denoting the chance that mutation would be falsely qualified as deleterious. For HumDiv-trained model, the thresholds are 5 % and 10 % FPR while for HumVar-trained model they are 10 % and 20 % FPR, respectively [28].

Linkage disequilibrium and haplotype blocks analysis

The observed genotype distribution was determined for all the detected polymorphisms by performing the Hardy-Weinberg equilibrium (HWE) exact test assuming consistency for P-value higher than 0.001. Linkage disequilibrium (LD) and haplotype block analysis was performed by Haploview 4.2 software (http://www.broad.mit.edu/mpg/haploview/). The LD analysis was made using |D’| and r2 parameters for each variation pair. |D’| coefficient was preferentially used to model recombination rates in examined population, and r2 parameter to model association power [29, 30].

Studying haplotypes in the human genome requires definition of the location of haplotype blocks, which are regions with little evidence for historical recombination and within which only a few common haplotypes are observed [31]. To determine haplotype blocks in ABCC1 for the Polish population, we used the method described previously by Gabriel et al. [31] based on confidence bounds on D’. For pairs of SNPs being in strong linkage, 95 % upper confidence bound D’ was higher than 0.98 and the lower bound was above 0.7 while for pairs of SNPs with strong evidences of historical recombination upper bound on D’ is lower than 0.9. According to this model, polymorphisms with MAF [22] lower than 0.05 were excluded during the creation of blocks and 16 polymorphic variants with higher MAF were included. Haplotypes with frequency lower than 1 % were grouped as “others”.

Results

ABCC1 genetic variation detected in this study

We screened coding regions and exon/intron boundaries as the most important gene areas (Additional file 1). To assure optimal detection level, the maximal length of tested PCR product was never longer than 250 and 125 base pairs for scanning and genotyping, respectively. For scanning, most of exons had to be divided into separate scanned areas, which provided 51 fragments for analysis. We performed complete polymorphism screening in 30 exons in total. We were unable to scan exon 1 because of problems with amplification during PCR, caused by extremely GC-rich sequence region.

In the scanning study on 190 Polish volunteers, we found 46 different SNPs in the scanned areas, and seven of them being novel and not reported yet (Table 2). 21 SNPs were located in exons and 11 of them change amino acid sequence as non-synonymous variants, including one novel polymorphic variant detected in this study: c.596C > T (p.Ser199Leu) with MAF = 0.003. Among all detected non-synonymous variants, only one, c.2012G > T (p.Gly671Val), occurred as a homozygote with estimated MAF = 0.077. The rest of SNPs found homozygously in exons were synonymous variants which resided at: c.825 T > C (p.Val275=), c.1062 T > C (p.Asn354=), c.1684 T > C (p.Leu562=), c.1704C > T (p.Tyr568=) and c.4002G > A (p.Ser1334=) with MAF values equal to 0.309, 0.332, 0.130. 0.088 and 0.277, respectively. Apart from polymorphisms in coding regions, 23 SNPs were located in introns and a last one (c.*3C > G) in the 3’ UTR. Furthermore, one intron deletion variant c.2461-39_2461-38delAT was found during the scanning. Two polymorphic variants located in introns, c.2871 + 26C > T and c.3079 + 10G > A, occurred only as non-reference homozygotes in all subjects.
Table 2

Summary of ABCC1 variants detected during scanning by HRM

Exon scanned by HRM

dbSNP ID

Variant position NM_004996.3:

Intron/amino acid residue NP_004987.2:

Observed genotypesa, b (n)

HWE exact test P-valuec

MAFd

R/R

R/V

V/V

2

rs8187843

c.225 + 26G > A

Intron

164

25

0

1

(A) 0.066

4

rs587783373*

c.352-79G > A

Intron

185

1

0

1

(A) 0.003

4

rs4148337

c.352-66 T > C

Intron

15

80

91

0.727

(T) 0.296

5

rs483352860*

c.596C > T

p.Ser199Leu

186

1

0

1

(T) 0.003

6

rs8187846

c.677 + 17C > T

Intron

188

1

0

1

(T) 0.003

7

rs483352864*

c.809 + 16C > T

Intron

188

1

0

1

(T) 0.003

7

rs45609533

c.809 + 31G > T

Intron

183

5

0

1

(T) 0.013

7

rs903880

c.809 + 54C > A

Intron

112

65

11

0.684

(A) 0.231

7

rs246232

c.809 + 64C > G

Intron

84

90

14

0.174

(G) 0.314

8

rs546943313

c.810-73C > T

Intron

187

1

0

1

(T) 0.003

8

rs200194736

c.814C > T

p.Pro272Ser

187

1

0

1

(T) 0.003

8

rs2230669

c.816G > A

p.Pro272=

172

16

0

1

(A) 0.043

8

rs246221

c.825 T > C

p.Val275=

84

92

12

0.059

(C) 0.309

8

rs587783372*

c.855G > A

p.Pro285=

187

1

0

1

(A) 0.003

9

rs35587

c.1062 T > C

p.Asn354=

78

91

16

0.185

(C) 0.332

9

rs35588

c.1218 + 8A > G

Intron

82

91

16

0.245

(G) 0.327

9

rs483352877*

c.1218 + 9C > T

Intron

188

1

0

1

(T) 0.003

10

rs60782127

c.1299G > T

p.Arg433Ser

186

2

0

1

(T) 0.005

12

rs17265551

c.1677 + 56C > T

Intron

162

27

0

0.604

(T) 0.072

13

rs35604

c.1678-37G > A

Intron

2

45

142

0.745

(G) 0.130

13

rs483352863*

c.1678-34G > A

Intron

188

1

0

1

(A) 0.003

13

rs35605

c.1684 T > C

p.Leu562=

2

45

142

0.745

(T) 0.130

13

rs8187858

c.1704C > T

p.Tyr568=

157

31

1

1

(T) 0.088

14

rs112282109

c.1898G > A

p.Arg633Gln

187

1

0

1

(A) 0.003

16

rs8187863

c.2001C > T

p.Ser667=

187

1

0

1

(T) 0.003

16

rs45511401

c.2012G > T

p.Gly671Val

161

25

2

0.296

(T) 0.077

17

rs4148356

c.2168G > A

p.Arg723Gln

181

9

0

1

(A) 0.024

19

rs45607032

c.2461-39_2461-38delAT

Intron

179

9

0

1

(delAT) 0.024

19

rs2074087

c.2461-30C > G

Intron

0

44

144

0.083

(C) 0.117

19

rs45492500

c.2461-27G > A

Intron

172

14

2

0.056

(A) 0.048

21

rs11075296

c.2871 + 26C > T

Intron

0

0

189

1

-

22

rs768191257

c.2876A > G

p.Lys959Arg

187

1

0

1

(G) 0.003

22

rs3851716

c.3079 + 10G > A

Intron

0

0

188

1

-

22

rs34794353

c.3079 + 24C > T

Intron

187

1

0

1

(T) 0.003

22

rs3887893

c.3079 + 62 T > C

Intron

67

96

25

0.358

(C) 0.388

23

rs191017838

c.3171G > A

p.Leu1057=

187

2

0

1

(A) 0.005

23

rs199773531

c.3196C > T

p.Arg1066Trp

188

1

0

1

(T) 0.003

25

rs41278168

c.3591-5C > T

Intron

187

1

0

1

(T) 0.003

27

rs200922662

c.3886C > T

p.Arg1296Trp

187

1

0

1

(T) 0.003

27

rs201533167

c.3901C > T

p.Arg1301Cys

187

1

0

1

(T) 0.003

28

rs2230671

c.4002G > A

p.Ser1334=

102

68

18

0.202

(A) 0.277

28

rs188980645

c.4093G > A

p.Asp1365Asn

187

1

0

1

(A) 0.003

29

rs212087

c.4126-45G > A

Intron

62

85

42

0.239

(A) 0.447

30

rs212088

c.4487 + 18G > A

Intron

136

47

5

0.775

(A) 0.148

31

rs587783374*

c.4551G > A

p.Gln1517=

186

1

0

1

(A) 0.003

31

rs373453875

c.*3C > G

3’ UTR

186

1

0

1

(G) 0.003

*Novel polymorphic variants detected in this study. aNumber of genotypes detected during this study, R – reference allele, V – variant allele. bTotal number of examined samples was 190, however during scanning single samples were excluded because of accidental problems with amplification during PCR and hence, total number of genotypes is not equal precisely 190. cP-value is consistent with Hardy-Weinberg equilibrium if P > 0.001. dMinor allele shown in brackets with its frequency. Bold variants signifies the ones which were validated by genotyping results

We used the data from genotyping study to validate our scanning results and compare accuracy of HRM methods (Additional file 2). For genotyping, we tested a subset of 17 SNPs, selected according to the literature or detected during scanning. Seven of them have not been found during scanning in examined samples and also did not show any genetic variation by genotyping method. Genotype validation by sequencing was performed for all of examined SNPs (Table 3). We calculated MAFs to enable us to assess the accuracy of scanning method compared to genotyping. Results obtained for eight of SNPs were quite similar and the differences were not statistically significant (Table 3). We found statistically significant difference in MAF values obtained in the both our studies for only two loci – c.1062 T > C (p.Asn354+) and c.2012G > T (p.Gly671Val).
Table 3

Summary of ABCC1 selected SNPs genotyping by HRM and comparing them with scanning results

dbSNP ID

Variant residue NM_004996.3:

Intron/amino acid residue NP_004987.2:

Observed genotypesa (n)

HWE exact test P-valueb

MAF c (genotyping)

MAFc (scanning)

Chi-square test P-valued

R/R

R/V

V/V

rs41395947

c.128G > C

p.Cys43Se

380

0

0

1

--

--

--

rs2230669 

c.816G > A

p.Pro272=

362

18

0

1

(A) 0.024

(A) 0.043

0.079

rs246221

c.825 T > C

p.Val275

197

160

23

0.243

(C) 0.271

(C) 0.309

0.187

rs8187852

c.1057G > A

p.Val353Met

379

0

0

1

--

--

--

rs35587

c.1062 T > C

p.Asn354=

204

142

33

0.247

(C) 0.274

(C) 0.332

0.044

rs35588

c.1218 + 8A > G

Intron

190

160

30

0.709

(G) 0.289

(G) 0.325

0.214

rs60782127

c.1299G > T

p.Arg433Ser

373

6

0

1

(T) 0.008

(T) 0.005

0.623

rs35605

c.1684 T > C

p.Leu562=

13

105

262

0.588

(T) 0.172

(T) 0.130

0.063

rs8187858

c.1704C > T

p.Tyr568=

325

55

0

0.242

(T) 0.072

(T) 0.087

0.374

rs45511401

c.2012G > T

p.Gly671Val

346

28

3

0.007

(T) 0.045

(T) 0.077

0.038

rs4148356

c.2168G > A

p.Arg723Gln

360

19

0

1

(A) 0.025

(A) 0.024

0.888

rs45517537

c.2581G > A

p.Ala861Thr

380

0

0

1

--

--

--

rs35529209

c.2965G > A

p.Ala989Thr

378

0

0

1

--

--

--

rs13337489

c.3140G > C

p.Cys1047Ser

380

0

0

1

--

--

--

rs28706727

c.3436G > A

p.Val1146Ile

380

0

0

1

--

--

--

rs2230671

c.4002G > A

p.Ser1334=

204

140

32

0.296

(A) 0.271

(A) 0.277

0.850

rs28364006

c.4009A > G

p.Thr1337Ala

380

0

0

1

--

--

--

aNumber of genotypes detected during this study, R – reference allele, V – variant allele. bP-value is consistent with Hardy-Weinberg equilibrium if P > 0.001. cMinor allele shown in brackets with its frequency. dP-value of Chi-square test with Yates’ correction, no significant difference between MAFs if P > 0.05

All the amino acids altered by non-synonymous variants were located in cytoplasmic domains of the protein. Variants c.596C > T (p.Ser199Leu) and c.814C > T (p.Pro272Ser) were located in the third intracellular loop (between TM5 and TM6), variant c.1299G > T (p.Arg433Ser) in the fourth intracellular loop, variant c.3196C > T (p.Arg1066Trp) in the seventh intracellular loop (between TM7 and TM8). Additional four variants located in the loop containing NBD1 alter amino acids sequence: c.1898G > A (p.Arg633Gln) and c.2012G > T (p.Gly671Val) are located 44 and 6 amino acids upstream of the Walker A motif, respectively, while c.2168G > A (p.Arg723Gln) and c.2876A > G (p.Lys959Arg) are located 37 amino acids downstream of this motif, respectively. Similarly, three variants changing amino acids in the loop containing NBD2 were detected: c.3886C > T (p.Arg1296Trp) and c.3901C > T (p.Arg1301Cys) are located 30 and 25 amino acids upstream of the Walker A motif, respectively, while c.4093G > A (p.Asp1365Asn) is located 30 amino acids downstream of the motif. Detected variant c.4002G > A (p.Ser1334=) occurred in the last position of Walker A motif in NBD2, however it does not change the amino acid sequence.

Analysis of all the non-synonymous variants detected in this study by the PolyPhen-2 tool (data in Additional file 3) showed for HumDiv-trained model that five of them: c.814C > T (p.Pro272Ser), c.1898G > A (p.Arg633Gln), c.2168G > A (p.Arg723Gln), c.2876A > G (p.Lys959Arg), the novel one c.4093G > A (p.Asp1365Asn), probably have benign influence on the functioning of the protein. For one polymorphism, c.3196C > T (p.Arg1066Trp) a possibly damaging effect on protein activity was expected. The novel variant c.596C > T (p.Ser199Leu) was estimated as a probably damaging substitution, likewise as four others: c.1299G > T (Arg433Ser), c.2012G > T (p.Gly671Val), c.3886C > T (p.Arg1296Trp) and c.3901C > T (p.Arg1301Cys). On the other hand, analysis for HumVar-trained model indicated that three polymorphisms: c.1299G > T (Arg433Ser), c.2012G > T (p.Gly671Val), c.3901C > T (p.Arg1301Cys), lead to probably damaging substitutions and two others, c.596C > T (p.Ser199Leu) and c.3886C > T (p.Arg1296Cys), are possibly damaging variants.

Linkage disequilibrium analysis

Based on full genotype sets of 44 polymorphic variants confirmed by Hardy-Weinberg equilibrium exact test (Table 2), linkage disequilibrium analysis using r2 and |D’| statistics was performed (Additional file 4). Accordingly to the r2 parameter, perfect linkage (r2 = 1) was detected between the new variant c.809 + 16C > T and c.3196C > T, between c.1678-37G > A and c.1684 T > C, between c.2168G > A and c.2461-39_2461-38delAT. Almost perfect linkage was observed between c.1062 T > C and c.1218 + 8A > G (r2 = 0.928), between c.825 T > C (p.Val275=) and c.1062 T > C (p.Asn354=) (r2 = 0.822) and between c.825 T > C (p.Val275=) and c.1218 + 8A > G (r2 = 0.894). Apart from perfect linkage between variants c.1678-37G > A and c.1684 T > C, they were both strongly linked (r2 = 0.705) with c.2461-30C > G. Other linkages (r2 = 0.624 and r2 = 0.658, respectively) were found between c.225 + 26G > A and c.816G > A, and between c.809 + 54C > A and c.809 + 64C > G.

For the |D’| values, perfect linkage (|D’| = 1) was observed for the great majority of variants pairs (760 for 946). Strong linkage (1 > |D’| ≥ 0.8) was detected for other 17 pairs. Small areas of low |D’| values, and hence weak or lacking of linkage, were defined between polymorphisms from group: c.225 + 26G > A, c.352-66 T > C, c.809 + 31G > T, c.809 + 54C > A, c.809 + 64C > G, c.816G > A, c.825 T > C, c.1062 T > C, c.1218 + 8A > G, c.1677 + 56C > T, and those from group: c.1678-37G > A, c.1684 T > C, c.1704C > T, c.2012G > T, c.2168G > A, c.2461-39_2461-38delAT, c.2461-30C > G, c.2461-27G > A, c.3079 + 62 T > C, c.4002G > A, c.4126-45G > A, c.4487 + 18G > A.

Haplotype block analysis

Based on the model of Gabriel et al. [31] a total of 12 common haplotypes were identified and their frequencies were defined in four blocks (Table 4). In block 1, variants c.809 + 54C > A and c.809 + 64C > G were included, and the most frequent haplotype in this block was that containing major alleles (frequency: 0.686), the next most frequent one was the haplotype with both minor alleles (frequency: 0.231). Block 2 spanned SNPs c.825 T > C (p.Val275=), c.1062 T > C (p.Asn354=) and c.1218 + 8A > G at a distance of 1 kb, with the most frequent haplotype containing all major alleles (frequency: 0.659), subsequently the haplotype with all minor alleles (frequency: 0.300), two other ones had low frequency (set alleles TCG: 0.021 and set alleles TCA: 0.011). Block 3 had two haplotypes composed of SNPs c.1678-37G > A and c.1684 T > C (p.Leu562=). The first haplotype was the set with major alleles (frequency: 0.870) and the second one the set with minor alleles (frequency: 0.130). Block 4 was created of SNPs c.4126-45G > A and c.4487 + 18G > A at a distance of around 2 kb. The most frequent haplotype was that of alleles combination AG (frequency: 0.447), the next ones with major alleles (frequency: 0.405) and allele combination GA (frequency: 0.148). The frequency of recombination was also calculated between blocks as a value of multiallelic D’ coefficient, and equaled to 0.65 between block 1 and 2, 0.55 between block 2 and 3 and 0.27 between block 3 and 4. HaploView predicted 51 possible connections of haplotypes for recombination between blocks higher than 1 %. However, recombination between blocks higher than 10 % occurred only between the most frequent haplotype from block 1 and the most frequent one from block 2, between three most frequent haplotypes from block 2 and the most frequent one from block 3, and between the most frequent one from block 3 and three haplotypes from block 4. Apart from c.1684 T > C (p.Leu562=), the remaining SNPs in blocks were tagged in silico as haplotype tag SNPs (htSNPs).
Table 4

Haplotype block analysis according to Gabriel et al. [31]

 

Block 1

Recombination between blocksd

Block 2

Recombination between blocksd

Block 3

Recombination between blocksd

Block 4

Variant positiona,b

c.809 + 54C >A

c.809 + 64C>G

Haplotype frequency

c.825 T>C p.Val275=

c.1062 T>C p.Asn354=

c.1218 + 8A>G

Haplotype frequency

c.1678-37G > A

c.1684 T > C p.Leu562=

Haplotype frequency

c.4126-45G > A

c.4487 + 18G N A

Haplotype frequency

Haplotypesc

C

C

0.686

0.65

T

T

A

0.659

0.45

A

C

0.870

0.27

A

G

0.447

A

G

0.231

C

C

G

0.300

G

T

0.130

G

G

0.405

C

G

0.082

T

C

G

0.021

 

G

A

0.148

others

0.001

T

C

A

0.011

 
 

others

0.009

aPosition according to reference sequences for coding nucleotides NM_004996.3 and amino acid residues NP_004987.2. bBold variants signify haplotype tag SNPs (htSNPs). cMajor alleles in white boxes, minor alleles in shaded boxes; all the haplotypes with their frequencies in population; haplotypes below 1 % grouped as “others”. dLevel of recombination between blocks as a value of multiallelic D’

Discussion

In this paper we highlighted the fact that the High Resolution Melting method is a powerful technique both for targeted genotyping of selected SNPs and for scanning of long parts of gene sequences. The obtained results for targeted genotyping and scanning of ABCC1 gene regions did not show statistically significant differences in MAFs values for most SNPs. Marginal statistical significance (0.05 > P > 0.01) of difference between MAF values for scanning and targeted genotyping methods for two SNPs c.1062 T > C (p.Asn354=) and c.2012G > T (p.Gly671Val) is probably caused by sample size effect (there was no overlap between groups of individuals selected for both stages of the study). Seven SNPs from a subset of 17 individually genotyped ones were not detected during the scanning and did not show any genetic variation during genotyping either. Although these variants were previously indicated to may have clinical significance and were therefore included in our study, they were only identified during in vitro studies or in non Caucasian populations (Exome Aggregation Consortium (ExAC), Cambridge, MA (http://exac.broadinstitute.org), date accessed: Aug 2015). Thus, it was expected not to find them in our study for 380 genotyped samples, where technical or methodological problems with detection should not have caused any discrepancy.

During the scanning study we identified 46 polymorphic variants by screening 30 exons of the ABCC1 gene with adjacent intronic fragments in 190 unrelated Polish individuals. A large fraction of variants represented rare or even completely novel (not previously published) ones. We were unable to compare them with the ones from the only other publication on ABCC1 variant distribution in Caucasians because of unavailability of the raw data from that study [15].

Comparing our genetic variation results obtained using the scanning method with data for Asian populations, 18 variants were detected in the Polish population which were present also in the Japanese one (n = 153) [13], while 13 variants from the Polish population were also detected in the Chinese one (n = 27) [14]. Some of these variants exhibited significant differences in MAF values, confirmed by chi-square test for alleles (with Yates’ correction if any allele quantity was below 5) and showed as their P-values, assumed significant if <0.05. Higher MAF for the Polish population was detected for variant c.809 + 54C > A (MAF = 0.231, compared to 0.059 for Japanese (P < 0.001) and 0.093 for Chinese (P = 0.020) populations), synonymous SNP c.4002G > A (MAF = 0.277, compared to 0.196 for Japanese (P = 0.014) and only 0.111 for Chinese (P = 0.009) populations) and for variant c.4126-45G > A between Polish (MAF = 0.447) and Japanese (MAF = 0.304) populations (P < 0.001). On the other hand, lower MAF for Polish population was noticed for variants: c.809 + 64C > A (0.313 for Polish, compared 0.418 (P = 0.004) for Japanese and 0.481 (P = 0.014) for Chinese populations), c.1678-37G > A and synonymous SNP c.1684 T > C (for both SNPs, MAF = 0.130 for Polish, 0.245 (P < 0.001) for Japanese but not significant for Chinese 0.204 (P = 0.141)), non-synonymous SNP c.2168G > A and deletion variant c.2461-39_2461-38delAT (for both variants, MAF = 0.024 for Polish, 0.065 (P = 0.007) for Japanese and not significant for Chinese 0.065 (P = 0.108)), c.2461-30C > G (for Polish MAF = 0.117, not significant for Chinese 0.167 (P = 0.299) but for Japanese 0.245 (P < 0.001)) and c.4487 + 18G > A between Polish (MAF = 0.148) and Japanese (MAF = 0.291, P < 0.001) populations. These results suggest that although a wide group of common variants for different populations have had similar MAF, there are also other polymorphisms which MAF values were significantly different. Since differences between Asian populations were consistently smaller than between them and the Polish population, we can conclude that ethnic ancestry is probable underlying cause of these variations. Similar conclusions were described previously during research of four polymorphisms in ABCC1 gene in Caucasians: c.816G > A, c.825 T > C, c.1684 T > C, c.4002G > A [32]. All these results correspond to those obtained by Wang et al. [16] who studied 60 SNPs and 10 deletion variants for four populations. During our ABCC1 scanning, we detected 25 common variants and obtained similar results for Polish population as those authors for Caucasian representatives. A slight difference in MAF was observed for few polymorphisms: the highest were seen for c.809 + 64C > G (MAF = 0.313 for Polish and 0.200 (P = 0.043) for Caucasians) and for c.825 T > C (p.Val275=) (MAF = 0.309 for Polish population and 0.139 (P = 0.003) for Caucasians), while the difference for c.4002G > A (MAF = 0.277 and 0.375 (P = 0.093) for Polish and Caucasians, respectively) was not significant. Furthermore, their results confirmed our previous observations concerning ethnic differences in occurrence of the same polymorphic variants between Caucasian and Asian populations. These differences relate mainly to the SNPs mentioned previously as most ethnically significant: c.809 + 54C > A, c.809 + 64C > A, c.3079 + 62 T > C, c.4002G > A and c.4126-45G > A.

We performed analysis of linkage disequilibrium (which is defined as non-random allelic association of different loci) to summarize our results for those 44 polymorphisms which were found to be consistent with the Hardy-Weinberg model. It turned out that, as expected, the r2 coefficient was most useful for analysis, and we detected polymorphic variant pairs with strong LD: our results are very similar even to those obtained for Japanese population [13]. It can be concluded on this basis that the specific linkages between particular variants are highly evolutionary conserved and occur in every population. Human genome is organized in haplotype blocks with high LD inside them, separated one from another by regions of low LD. Blocks show no evidence of historical recombination inside. This definition of haplotype blocks could be applied to 17 SNPs which had MAF higher than 0.05, which was the lower threshold in this analysis. We found four blocks and 12 common haplotypes including nine SNPs detected during this study. We determined htSNPs which should be sufficient to identify common haplotypes in population. Low haplotype diversity of ABCC1 in Polish population is consistent with the conclusion of another study on Caucasians [15]. Although blocks for our results are small and the number of SNPs used for haplotype construction is low, the main core of blocks and their borders are retained across populations regardless of the blocks definitions used. Our blocks 1 and 2, usually connected as one in other studies, extend from intron 7 to intron 9. Our block 3 include SNPs from vicinity of exon 13 and block 4 contains SNPs from intron 28 to intron 30. It corresponds to block borders detected in other studies and suggest points of recombination which had taken place in intron 12 and in intron 27 or 28 [1315].

Analysis using PolyPhen-2 which predicts the influence of detected non-synonymous SNPs on protein functionality suggested that six of them are suspect. However, each analysis is only a statistical calculation based on a full dataset and some experimental data do not support these results. Létourneau et al. [33] examined 10 missense SNPs of ABCC1 and almost all of them, classified by PolyPhen-2 software as deleterious, affected neither expression nor functionality of the protein. A similar observation was also reported previously for the SNP c.2012G > T (p.Gly671Val), detected also by us. Although this substitution affects a residue near the highly conserved motif Walker A, it reduced mRNA expression but did not change transport properties of the protein, although PolyPhen-2 analysis clearly showed its damaging impact [34]. Recently, other data confirmed these observations and none of the amino acid substitutions: p.Arg633Gln, p.Gly671Val, p.Arg723Gln, detected also in our study, was found to change functionality of MRP1 transporter. Two substitutions altered drug resistance and inhibitor sensitivity and we found one of them, c.4002G > A (p.Ser1334=) [35].

On the other hand, there are other papers showing contrasting data for above-mentioned variants, proving their clinical association or experimental impact on protein functionality. Thus, it was demonstrated that variant c.2168G > A (p.Arg723Gln) reduced resistance activity of MRP1-overexpressing cells to drugs like daunorubicin, doxorubicin, etoposide, vinblastine and vincristine in vitro [36]. Although correlation between carrying this SNP and chemotherapy response was not observed in lung cancer patients [37], this variant was linked to the response to chemotherapy in patients with ovarian cancer [19]. Two other polymorphic variants detected in this study c.825 T > C (p.Val275=) and c.2012G > T (p.Gly671Val), were correlated with febrile neutropenia as an effect of FEC-induced hematological toxicity in breast cancer patients [38]. The major allele variant c.825 T > C (p.Val275=), in combination with another one c.*543C > T (beyond our scanning), was also associated with anthracycline-induced cardiotoxicity after chemotherapy in children with acute lymphoblastic leukemia [39]. The discrepancy observed in different studies on clinical significance of c.2012G > T (p.Gly671Val) and c.2168G > A (p.Arg723Gln) is of unclear origin. Cellular conditions in vitro in which mutants were expressed could be inadequate to reveal all their real functions, e.g. a change in mRNA structure could be more essential than the amino acid change caused by SNP [34]. Conseil and Cole [35] concluded from this discordance that comprehensive biochemical and pharmacological characterization of the respective MRP1 mutants should be performed to identify consequences of ABCC1 SNPs on phenotype because these effects might be quite selective. Additional nucleic acid-based methods should be also used to clarify the impact of individual SNPs.

These examples show that only comprehensive examination of polymorphisms and studying their phenotypes can tell more about their real influence. Therefore, novel variants like c.596C > T (p.Ser199Leu), detected in this study and qualified in silico as damaging, need to be checked in an experimental investigation. Additionally, the fact that we found no novel mutation in important nucleotide binding motifs, nor any of the mutations identified previously in vitro as deleterious in NBD loops, proved that these highly conserved regions in the protein structure are unlikely to be affected by polymorphisms occurring in the healthy population.

Conclusions

We scanned 30 exons and short flanking intron sequences of ABCC1. HRM is an efficient and sensitive method for scanning and genotyping polymorphic variants. We compared results of long-range scanning to targeted genotyping of selected SNPs showing that both methods are highly sensitive tools to study genetic variation. During our study we found 45 polymorphic variants, some of them rare and eight of them novel not previously reported. This is the first study about ABCC1 genetic variation in the Polish population and we found some ethnic differences in frequency of genetic variants compared to other populations. These results need further complementation with a larger number of probands and new data including also introns and untranslated regions as sequences with polymorphic variants which may have clinical relevance as well.

Abbreviations

ABC transporters: 

ATP-binding cassette transporters

E217βG: 

Estradiol-17-β-D-glucuronide

GSH: 

Glutathione

HRM: 

High resolution melting

htSNPs: 

Haplotype tag SNPs

HWE: 

Hardy Weinberg equilibrium

LD: 

Linkage disequilibrium

MAF: 

Minor allele frequency

MRP1: 

Multidrug resistance-associated protein 1

NBD: 

Nucleotide binding domain

TM: 

Transmembrane helice

TMD: 

Transmembrane domain

Declarations

Acknowledgments

The authors would like to thank the reviewers for their comments that help to improve the manuscript. In addition we would like to thank Łukasz Pułaski for fruitful discussion and language editing. This work was supported by the Polish POIG grant 01.01.02-10-005/08 TESTOPLEK from the European Regional Development Fund.

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

Authors’ Affiliations

(1)
Biobank Lab, Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Łódź
(2)
Institute of Medical Biology, Polish Academy of Sciences
(3)
Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Łódź

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© Słomka et al. 2015

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