ABCMdb

ABCB1 p.Thr1256Lys

Predicted by SNAP2:	A: D (75%), C: D (75%), D: D (71%), E: D (85%), F: D (85%), G: D (80%), H: D (80%), I: D (85%), K: D (53%), L: D (85%), M: D (80%), N: N (66%), P: D (85%), Q: D (75%), R: D (80%), S: N (78%), V: D (80%), W: D (85%), Y: D (85%),
Predicted by PROVEAN:	A: D, C: D, D: D, E: D, F: D, G: D, H: D, I: D, K: D, L: D, M: D, N: D, P: D, Q: D, R: D, S: D, V: D, W: D, Y: D,

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[hide] Mechanisms of resistance to anticancer drugs: the ... Pharmacogenomics. 2005 Mar;6(2):115-38. Lepper ER, Nooter K, Verweij J, Acharya MR, Figg WD, Sparreboom A
Mechanisms of resistance to anticancer drugs: the role of the polymorphic ABC transporters ABCB1 and ABCG2.
Pharmacogenomics. 2005 Mar;6(2):115-38., [PMID:15882131]

Abstract [show]
ATP-binding cassette (ABC) genes play a role in the resistance of malignant cells to anticancer agents. The ABC gene products, including ABCB1 (P-glycoprotein) and ABCG2 (breast cancer-resistance protein [BCRP], mitoxantrone-resistance protein [MXR], or ABC transporter in placenta [ABCP]), are also known to influence oral absorption and disposition of a wide variety of drugs. As a result, the expression levels of these proteins in humans have important consequences for an individual's susceptibility to certain drug-induced side effects, interactions, and treatment efficacy. Naturally occurring variants in ABC transporter genes have been identified that might affect the function and expression of the protein. This review focuses on recent advances in the pharmacogenetics of the ABC transporters ABCB1 and ABCG2, and discusses potential implications of genetic variants for the chemotherapeutic treatment of cancer.

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126 In addition to the possible decrease in expression levels, ATPase activity in the ABCG2 +24 Intron 20 G A +40 Intron 20 C T 2547 Exon 21 A G 849 Ile to Met 2650 Exon 21 C T 884 Syn 2677 Exon 21 G T 893 Ala to Ser 2677# Exon 21 G A 893 Ala to Thr +31 Intron 22 G A 2956 Exon 24 A G 986 Met to Val 2995 Exon 24 G A 999 Ala to Thr 3151 Exon 25 C G 1051 Pro to Ala 3320 Exon 26 A C 1107 Gln to Pro 3322 Exon 26 T C 1108 Trp to Arg 3396 Exon 26 C T 1132 Syn 3421 Exon 26 T A 1141 Ser to Thr 3435** Exon 26 C T 1145 Syn 3751 Exon 28 G A 1251 Val to Ile 3767 Exon 28 C A 1256 Thr to Lys 4030 Exon 28 G C Non-coding 4036 Exon 28 A G Non-coding +21 Intron 28 T C Table 2. Summary of common genetic variants in the ABCB1 gene (continued) *cDNA numbers are relative to the ATG site and based on the cDNA sequence from GenBank accession number M14758 with an A as the reference at position 43. Login to comment

[hide] Genetic polymorphisms of ATP-binding cassette tran... Expert Opin Pharmacother. 2005 Nov;6(14):2455-73. Sakurai A, Tamura A, Onishi Y, Ishikawa T
Genetic polymorphisms of ATP-binding cassette transporters ABCB1 and ABCG2: therapeutic implications.
Expert Opin Pharmacother. 2005 Nov;6(14):2455-73., [PMID:16259577]

Abstract [show]
Pharmacogenomics, the study of the influence of genetic factors on drug action, is increasingly important for predicting pharmacokinetics profiles and/or adverse reactions to drugs. Drug transporters, as well as drug metabolism play pivotal roles in determining the pharmacokinetic profiles of drugs and their overall pharmacological effects. There is an increasing number of reports addressing genetic polymorphisms of drug transporters. However, information regarding the functional impact of genetic polymorphisms in drug transporter genes is still limited. Detailed functional analysis in vitro may provide clear insight into the biochemical and therapeutic significance of genetic polymorphisms. This review addresses functional aspects of the genetic polymorphisms of human ATP-binding cassette transporters, ABCB1 and ABCG2, which are critically involved in the pharmacokinetics of drugs.

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106 Position Allele Amino acid Allele frequency in Caucasian populations Allele frequency in Japanese populatins Allele frequency in African populations n % n % n % 61 A G 21 Asn 21 Asp 799 89.7 10.3 193 100 0 100 97.5 2.5 266 T C 89 Met 89 Thr 100 99.5 0.5 145 100 0 100 100 0 307 T C 103 Phe 103 Leu 546 99.9 0.1 48 100 0 ND ND ND 325 G A 108 Glu 108 Lys ND ND ND 37 95.9 4.1 ND ND ND 781 A G 261 Ile 261 Val 100 100 0 145 100 0 100 98.5 1.5 1199 G A 400 Ser 400 Asn 696 95.0 5.0 193 100 0 100 99 1 1985 T G 662 Leu 662 Arg 100 99.5 0.5 145 100 0 100 100 0 2005 C T 669 Arg 669 Cys 100 100 0 145 100 0 100 99 1 2485 A G 829 Ile 829 Val 185 99.2 0.8 ND ND ND ND ND ND 2547 A G 849 Ile 849 Met 100 99.5 0.5 145 100 0 100 100 0 2677 G T A 893 Ala 893 Ser 893 Thr 611 55.1 42.1 2.8 241 40.0 41.1 18.9 100 90 10 0.5 2956 A G 986 Met 986 Val ND ND ND 100 99.5 0.5 ND ND ND 3151 C G 1051 Pro 1051 Ala 100 100 0 145 100 0 100 99.5 0.5 3320 A C 1107 Gln 1107 Pro 461 99.8 0.2 ND ND ND ND ND ND 3322 T C 1108 Trp 1108 Arg 100 100 0 145 100 0 100 99.5 0.5 3421 T A 1141 Ser 1141 Thr 100 100 0 145 100 0 100 88.9 11.1 3751 G A 1251 Val 1251 Ile 100 100 0 145 99 1 100 100 0 3767 C A 1256 Thr 1256 Lys 100 99.5 0.5 145 100 0 100 100 0 Data from [31-38, 203]. Login to comment
129 N21D M89T N44S H2N F103L E108K N183S G185V I261V S400N R492C A599T L662R R669C V801M A893S/T I829V I849M M986V A999T G1063A P1051A Q1107P W1108R I1145M S1141T V1251I T1256K COOH ATP-binding site ATP-binding site EXTRACELLULAR INTRACELLULAR A80E Figure 2. Login to comment

[hide] Clinical pharmacogenetics and potential applicatio... Curr Drug Metab. 2008 Oct;9(8):738-84. Zhou SF, Di YM, Chan E, Du YM, Chow VD, Xue CC, Lai X, Wang JC, Li CG, Tian M, Duan W
Clinical pharmacogenetics and potential application in personalized medicine.
Curr Drug Metab. 2008 Oct;9(8):738-84., [PMID:18855611]

Abstract [show]
The current 'fixed-dosage strategy' approach to medicine, means there is much inter-individual variation in drug response. Pharmacogenetics is the study of how inter-individual variations in the DNA sequence of specific genes affect drug responses. This article will highlight current pharmacogenetic knowledge on important drug metabolizing enzymes, drug transporters and drug targets to understand interindividual variability in drug clearance and responses in clinical practice and potential use in personalized medicine. Polymorphisms in the cytochrome P450 (CYP) family may have had the most impact on the fate of pharmaceutical drugs. CYP2D6, CYP2C19 and CYP2C9 gene polymorphisms and gene duplications account for the most frequent variations in phase I metabolism of drugs since nearly 80% of drugs in use today are metabolised by these enzymes. Approximately 5% of Europeans and 1% of Asians lack CYP2D6 activity, and these individuals are known as poor metabolizers. CYP2C9 is another clinically significant drug metabolising enzyme that demonstrates genetic variants. Studies into CYP2C9 polymorphism have highlighted the importance of the CYP2C9*2 and CYP2C9*3 alleles. Extensive polymorphism also occurs in a majority of Phase II drug metabolizing enzymes. One of the most important polymorphisms is thiopurine S-methyl transferases (TPMT) that catalyzes the S-methylation of thiopurine drugs. With respect to drug transport polymorphism, the most extensively studied drug transporter is P-glycoprotein (P-gp/MDR1), but the current data on the clinical impact is limited. Polymorphisms in drug transporters may change drug's distribution, excretion and response. Recent advances in molecular research have revealed many of the genes that encode drug targets demonstrate genetic polymorphism. These variations, in many cases, have altered the targets sensitivity to the specific drug molecule and thus have a profound effect on drug efficacy and toxicity. For example, the beta (2)-adrenoreceptor, which is encoded by the ADRB2 gene, illustrates a clinically significant genetic variation in drug targets. The variable number tandem repeat polymorphisms in serotonin transporter (SERT/SLC6A4) gene are associated with response to antidepressants. The distribution of the common variant alleles of genes that encode drug metabolizing enzymes, drug transporters and drug targets has been found to vary among different populations. The promise of pharmacogenetics lies in its potential to identify the right drug at the right dose for the right individual. Drugs with a narrow therapeutic index are thought to benefit more from pharmacogenetic studies. For example, warfarin serves as a good practical example of how pharmacogenetics can be utilized prior to commencement of therapy in order to achieve maximum efficacy and minimum toxicity. As such, pharmacogenetics has the potential to achieve optimal quality use of medicines, and to improve the efficacy and safety of both prospective and licensed drugs.

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532 Nucleotide change rs number Amino acid change 49T>C rs28381804 F17L 61A>G rs61615398; rs9282564 N21D 131A>G rs1202183 N44S 178A>C rs41315618 I60L 239C>A rs9282565 A80E 266T>C Rs35810889 M89T 431T>C rs61607171 I144T 502G>A rs61122623 V168I 548A>G rs60419673 N183S 554G>T rs1128501 G185V 781A>G rs36008564 I261V 1199G>A rs2229109 S400N 1696G>A rs28381902 E566K 1777C>T rs28381914 R593C 1778G>A rs56107566 R593H 1795G>A rs2235036 A599T 1837G>T rs57001392 D613Y 1985T>G rs61762047 L662R 2005C>T rs35023033 R669C 2207A>T rs41316450 I736K 2398G>A rs41305517 D800N 2401G>A rs2235039 V801M 2485A>G rs2032581 I829V 2506A>G rs28381967 I836V 2547A>G rs36105130 I849M 2677T>A/G rs2032582 S893A/T 2975G>A rs56849127 S992N 3151C>G rs28401798 P1051A 3188G>C rs2707944 G1063A 3262G>A rs57521326 D1088N 3295A>G rs41309225 K1099E 3320A>C rs55852620 Q1107P 3322T>C rs35730308 W1108R 3410G>T rs41309228 S1137I 3421T>A rs2229107 S1141T 3502A>G rs59241388 K1168E 3669A>T rs41309231 E1223D 3751G>A rs28364274 V1251I 3767C>A r35721439 T1256K Data are from NCBI dbSNP (access date: 2 August 2008). Login to comment

[hide] Sequence diversity and haplotype structure in the ... Pharmacogenetics. 2003 Aug;13(8):481-94. Kroetz DL, Pauli-Magnus C, Hodges LM, Huang CC, Kawamoto M, Johns SJ, Stryke D, Ferrin TE, DeYoung J, Taylor T, Carlson EJ, Herskowitz I, Giacomini KM, Clark AG
Sequence diversity and haplotype structure in the human ABCB1 (MDR1, multidrug resistance transporter) gene.
Pharmacogenetics. 2003 Aug;13(8):481-94., [PMID:12893986]

Abstract [show]
OBJECTIVES: There is increasing evidence that polymorphism of the ABCB1 (MDR1) gene contributes to interindividual variability in bioavailability and tissue distribution of P-glycoprotein substrates. The aim of the present study was to (1) identify and describe novel variants in the ABCB1 gene, (2) understand the extent of variation in ABCB1 at the population level, (3) analyze how variation in ABCB1 is structured in haplotypes, and (4) functionally characterize the effect of the most common amino acid change in P-glycoprotein. METHODS AND RESULTS: Forty-eight variant sites, including 30 novel variants and 13 coding for amino acid changes, were identified in a collection of 247 ethnically diverse DNA samples. These variants comprised 64 statistically inferred haplotypes, 33 of which accounted for 92% of chromosomes analyzed. The two most common haplotypes, ABCB1*1 and ABCB1*13, differed at six sites (three intronic, two synonymous, and one non-synonymous) and were present in 36% of all chromosomes. Significant population substructure was detected at both the nucleotide and haplotype level. Linkage disequilibrium was significant across the entire ABCB1 gene, especially between the variant sites found in ABCB1*13, and recombination was inferred. The Ala893Ser change found in the common ABCB1*13 haplotype did not affect P-glycoprotein function. CONCLUSION: This study represents a comprehensive analysis of ABCB1 nucleotide diversity and haplotype structure in different populations and illustrates the importance of haplotype considerations in characterizing the functional consequences of ABCB1 polymorphisms.

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103 A total of 30 segregating sites have not been previously described, including eight non-synonymous changes coding for the following amino acid changes: Met89Thr, Leu662Arg, Arg669Cys, Ile849Met, Pro1051Ala, Trp1108Arg, Val1251Ile, and Thr1256Lys. Login to comment
111 These ten variants included five non-synonymous sites (Ile261Val, Leu662Arg, Pro1051Ala, Ser1141Thr, and Thr1256Lys). Login to comment
141 Unauthorized reproduction of this article is prohibited. Table 1 Genetic variation in ABCB1 Allele frequencyd Variant cDNA NT DNA/AA AA Total CA AA AS ME PA No.a positionb changec position change (n ¼ 494) (n ¼ 200) (n ¼ 200) (n ¼ 60) (n ¼ 20) (n ¼ 14) 1.1Ã (À274) G to A Intron À1 0.006 0 0.016 0 0 0 1.2Ã (À223) C to T Intron À1 0.002 0.005 0 0 0 0 1.3Ã (À146) T to C Intron À1 0.002 0 0.005 0 0 0 1.4Ã (À60) A to T Intron À1 0.004 0 0.010 0 0 0 1.5 (À41) A to G Intron À1 0.002 0 0 0.017 0 0 1.6Ã À241 G to A Non-coding 0.002 0 0 0.017 0 0 1.7 À145 C to G Non-coding 0.002 0 0 0.017 0 0 1.8 À129 T to C Non-coding 0.060 0.051 0.071 0.036 0.100 0.071 1.9 À43 A to G Non-coding 0.012 0 0.020 0.036 0 0 1.10Ã (+140) C to A Intron 1 0.013 0.005 0.021 0 0 0.071 1.11Ã (+237) G to A Intron 1 0.004 0 0.010 0 0 0 2.1 À4 C to T Non-coding 0.004 0 0.010 0 0 0 2.2 À1 G to A Non-coding 0.036 0.080 0.005 0 0.050 0 2.3 61 A to G 21 Asn to Asp 0.045 0.080 0.025 0.017 0 0 4.1Ã (À8) C to G Intron 3 0.002 0.005 0 0 0 0 4.2Ã 266 T to C 89 Met to Thr 0.002 0.005 0 0 0 0 5.1 (À25) G to T Intron 4 0.210 0.158 0.300 0.067 0.200 0.286 8.1 729 A to G 243 Syn 0.002 0.005 0 0 0 0 8.2Ã 781 A to G 261 Ile to Val 0.006 0 0.015 0 0 0 10.1Ã (À44) A to G Intron 9 0.400 0.450 0.255 0.685 0.450 0.571 11.1Ã (À41) T to G Intron 10 0.002 0 0 0.017 0 0 11.2 1199 G to A 400 Ser to Asn 0.014 0.025 0.010 0 0 0 12.1Ã (À4) G to A Intron 11 0.002 0 0.005 0 0 0 12.2 1236 C to T 412 Syn 0.385 0.459 0.209 0.685 0.450 0.571 12.3Ã 1308 A to G 436 Syn 0.002 0 0.005 0 0 0 12.4Ã (+17) G to A Intron 12 0.008 0 0.020 0 0 0 12.5 (+44) C to T Intron 12 0.088 0.046 0.168 0 0 0 13.1 (+24) C to T Intron 13 0.530 0.521 0.542 0.540 0.450 0.571 14.1 1617 C to T 539 Syn 0.002 0.005 0 0 0 0 14.2 (+38) A to G Intron 14 0.540 0.505 0.540 0.683 0.450 0.500 15.1 (+38) G to A Intron 15 0.004 0.005 0.005 0 0 0 16.1Ã 1985 T to G 662 Leu to Arg 0.002 0.005 0 0 0 0 16.2Ã 2005 C to T 669 Arg to Cys 0.004 0 0.010 0 0 0 18.1Ã (À27) A to G Intron 17 0.008 0.010 0.005 0 0.050 0 20.1Ã (+8) C to G Intron 20 0.002 0 0.005 0 0 0 20.2 (+24) G to A Intron 20 0.126 0.121 0.150 0.067 0.200 0 20.3Ã (+40) C to T Intron 20 0.014 0 0.035 0 0 0 21.1Ã 2547 A to G 849 Ile to Met 0.002 0.005 0 0 0 0 21.2 2650 C to T 884 Syn 0.004 0.005 0.005 0 0 0 21.3a 2677 G to T 893 Ala to Ser 0.308 0.464 0.100 0.450 0.400 0.357 21.3b 2677 G to A 893 Ala to Thr 0.035 0.036 0.005 0.067 0 0.357 22.1 (+31) G to A Intron 22 0.002 0 0.005 0 0 0 25.1Ã 3151 C to G 1051 Pro to Ala 0.002 0 0.005 0 0 0 26.1Ã 3322 T to C 1108 Trp to Arg 0.002 0 0.005 0 0 0 26.2 3421 T to A 1141 Ser toThr 0.047 0 0.111 0 0.050 0 26.3 3435 C to T 1145 Syn 0.392 0.561 0.202 0.400 0.500 0.500 28.1 3751 G to A 1251 Val to Ile 0.002 0 0 0 0.050 0 28.2 3767 C to A 1256 Thr to Lys 0.002 0.005 0 0 0 0 28.3 (+21) T to C Intron 28 0.031 0 0.077 0 0 0 a Variants are numbered sequentially by exon. Login to comment
164 M89T I849M V1251I T1256K S1141TW1108R P1052AR669C A893S/T L662R Cytoplasm S400N I261V N21D Extracellular Fig. 1. Login to comment

[hide] Functional implications of genetic polymorphisms i... Pharm Res. 2004 Jun;21(6):904-13. Pauli-Magnus C, Kroetz DL
Functional implications of genetic polymorphisms in the multidrug resistance gene MDR1 (ABCB1).
Pharm Res. 2004 Jun;21(6):904-13., [PMID:15212152]

Abstract [show]
The multidrug resistance (MDR1) gene product P-glycoprotein is a membrane protein that functions as an ATP-dependent efflux pump, transporting exogenous and endogenous substrates from the inside of cells to the outside. Physiological expression of P-glycoprotein in tissues with excretory or protective function is a major determinant of drug disposition and provides a cellular defense mechanism against potentially harmful compounds. Therefore, P-glycoprotein has significant impact on therapeutic efficacy and toxicity as it plays a key role in absorption of oral medications from the intestinal tract, excretion into bile and urine, and distribution into protected tissues such as the brain and testes. There is increasing interest in the possible role of genetic variation in MDR1 in drug therapy. Numerous genetic polymorphisms in MDR1 have been described, some of which have been shown to determine P-glycoprotein expression levels and substrate transport. Furthermore, some of these polymorphisms have an impact on pharmacokinetic and pharmacodynamic profiles of drug substrates and directly influence outcome and prognosis of certain diseases. This review will focus on the impact of genetic variation in MDR1 on expression and function of P-glycoprotein and the implications of this variation for drug therapy and disease risk.

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28 6, June 2004 ((c) 2004) 9040724-8741/04/0600-0904/0 (c) 2004 Plenum Publishing Corporation Table I. MDR1 Coding Variants cDNA positiona NT change DNA/AA position AA change Allele frequencyb Total (n ‫ס‬ 494) CA (n ‫ס‬ 200) AA (n ‫ס‬ 200) AS (n ‫ס‬ 60) ME (n ‫ס‬ 20) PA (n ‫ס‬ 14) 61 A to G 21 Asn to Asp 0.045 0.080 0.025 0.017 0 0 266 T to C 89 Met to Thr 0.002 0.005 0 0 0 0 729 A to G 243 Syn 0.002 0.005 0 0 0 0 781 A to G 261 Ile to Val 0.006 0 0.015 0 0 0 1199 G to A 400 Ser to Asn 0.014 0.025 0.010 0 0 0 1236 C to T 412 Syn 0.385 0.459 0.209 0.685 0.450 0.571 1308 A to G 436 Syn 0.002 0 0.005 0 0 0 1617 C to T 539 Syn 0.002 0.005 0 0 0 0 1985 T to G 662 Leu to Arg 0.002 0.005 0 0 0 0 2005 C to T 669 Arg to Cys 0.004 0 0.010 0 0 0 2547 A to G 849 Ile to Met 0.002 0.005 0 0 0 0 2650 C to T 884 Syn 0.004 0.005 0.005 0 0 0 2677 G to T 893 Ala to Ser 0.308 0.464 0.100 0.450 0.400 0.357 2677 G to A 893 Ala to Thr 0.035 0.036 0.005 0.067 0 0.357 3151 C to G 1051 Pro to Ala 0.002 0 0.005 0 0 0 3322 T to C 1108 Trp to Arg 0.002 0 0.005 0 0 0 3421 T to A 1141 Ser to Thr 0.047 0 0.111 0 0.050 0 3435 C to T 1145 Syn 0.392 0.561 0.202 0.400 0.500 0.500 3751 G to A 1251 Val to Ile 0.002 0 0 0 0.050 0 3767 C to A 1256 Thr to Lys 0.002 0.005 0 0 0 0 a cDNA numbers are relative to the ATG site and based on the cDNA sequence from GenBank accession number M14758. Login to comment

[hide] Ethnic differences in genetic polymorphisms of CYP... Drug Metab Pharmacokinet. 2004 Apr;19(2):83-95. Ozawa S, Soyama A, Saeki M, Fukushima-Uesaka H, Itoda M, Koyano S, Sai K, Ohno Y, Saito Y, Sawada J
Ethnic differences in genetic polymorphisms of CYP2D6, CYP2C19, CYP3As and MDR1/ABCB1.
Drug Metab Pharmacokinet. 2004 Apr;19(2):83-95., [PMID:15499174]

Abstract [show]
Metabolic capacities for debrisoquin, sparteine, mephenytoin, nifedipine, and midazolam, which are substrates of polymorphic CYP2D6, CYP2C19, and CYP3A, have been reported to exhibit, in many cases, remarkable interindividual and ethnic differences. These ethnic differences are partly associated with genetic differences. In the case of the drug transporter ABCB1/MDR1, interindividual differences in its transporter activities toward various clinical drugs are also attributed to several ABCB1/MDR1 genetic polymorphisms. In this review, the existence and frequency of various low-activity alleles of drug metabolizing enzymes as well as populational drug metabolic capacities are compared among several different races or ethnicities. Distribution of nonsynonymous ABCB1/MDR1 SNPs and haplotype frequency in various races are summarized, with the association of nonsynonymous SNPs with large functional alterations as a rare event.

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85 Ethnic dierences in nonsynonymous SNPs of ABCB1W MDR1 cDNA positiona Position and amino acid change C AA AS J 61AÀG N21D 0.080 0.025 0.017 0 266TÀC M89T 0.005 0 0 0 781AÀG I261V 0 0.015 0 0 1199GÀA S400N 0.025 0.010 0 0 1985TÀG L662R 0.005 0 0 0 2005CÀT R669C 0 0.010 0 0 2547AÀG I849M 0.005 0 0 0 2677GÀT A893S 0.464 0.100 0.450 0.403 2677GÀA A893T 0.036 0.005 0.067 0.200 3151CÀG P1051A 0 0.005 0 0 3322TÀC W1108R 0 0.005 0 0 3421TÀA S1141T 0 0.111 0 0 3751GÀA V1251I 0 0 0 0.010 3767CÀA T1256K 0.005 0 0 0 C, 100 Caucasians; AA, 100 African-Americans; AS, 30 Asians; J, 145 Japanese (our study 116) ). Login to comment

[hide] Functional hot spots in human ATP-binding cassette... Protein Sci. 2010 Nov;19(11):2110-21. Kelly L, Fukushima H, Karchin R, Gow JM, Chinn LW, Pieper U, Segal MR, Kroetz DL, Sali A
Functional hot spots in human ATP-binding cassette transporter nucleotide binding domains.
Protein Sci. 2010 Nov;19(11):2110-21., [PMID:20799350]

Abstract [show]
The human ATP-binding cassette (ABC) transporter superfamily consists of 48 integral membrane proteins that couple the action of ATP binding and hydrolysis to the transport of diverse substrates across cellular membranes. Defects in 18 transporters have been implicated in human disease. In hundreds of cases, disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs). The functional impact of the majority of ABC transporter nsSNPs has yet to be experimentally characterized. Here, we combine experimental mutational studies with sequence and structural analysis to describe the impact of nsSNPs in human ABC transporters. First, the disease associations of 39 nsSNPs in 10 transporters were rationalized by identifying two conserved loops and a small alpha-helical region that may be involved in interdomain communication necessary for transport of substrates. Second, an approach to discriminate between disease-associated and neutral nsSNPs was developed and tailored to this superfamily. Finally, the functional impact of 40 unannotated nsSNPs in seven ABC transporters identified in 247 ethnically diverse individuals studied by the Pharmacogenetics of Membrane Transporters consortium was predicted. Three predictions were experimentally tested using human embryonic kidney epithelial (HEK) 293 cells stably transfected with the reference multidrug resistance transporter 4 and its variants to examine functional differences in transport of the antiviral drug, tenofovir. The experimental results confirmed two predictions. Our analysis provides a structural and evolutionary framework for rationalizing and predicting the functional effects of nsSNPs in this clinically important membrane transporter superfamily.

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72 Predictions of the Functional Effects of 40 nsSNPs in ABC Transporters Comon name HUGO name Mutation NBD Prediction BSEP ABCB11 E592Q NBD1 Neutral BSEP ABCB11 N591S NBD1 Neutral BSEP ABCB11 Q558H NBD1 Neutral BSEP ABCB11 V444A NBD1 Neutral BSEP ABCB11 E1186K NBD2 Disease MDR1 ABCB1 P1051A NBD2 Neutral MDR1 ABCB1 S1141T NBD2 Neutral MDR1 ABCB1 T1256K NBD2 Disease MDR1 ABCB1 V1251I NBD2 Neutral MDR1 ABCB1 W1108R NBD2 Disease MRP2 ABCC2 I670T NBD1 Disease MRP2 ABCC2 L849R NBD1 Disease MRP2 ABCC2 C1515Y NBD2 Disease MRP3 ABCC3 D770N NBD1 Neutral MRP3 ABCC3 K718M NBD1 Neutral MRP3 ABCC3 T809M NBD1 Disease MRP3 ABCC3 V765L NBD1 Disease MRP3 ABCC3 Q1365R NBD2 Disease MRP3 ABCC3 R1297H NBD2 Disease MRP3 ABCC3 R1348C NBD2 Disease MRP3 ABCC3 R1381S NBD2 Disease MRP4 ABCC4 G487E NBD1 Disease MRP4 ABCC4 K498E NBD1 Neutral MRP4 ABCC4 R1220Q NBD2 Neutral MRP4 ABCC4 T1142M NBD2 Neutral MRP4 ABCC4 V1071I NBD2 Neutral MRP6 ABCC6 I1330L NBD1 Neutral MRP6 ABCC6 I742V NBD1 Neutral MRP6 ABCC6 P664S NBD1 Neutral MRP6 ABCC6 R724K NBD1 Neutral MRP6 ABCC6 R769K NBD1 Neutral MRP6 ABCC6 A1291T NBD2 Neutral MRP6 ABCC6 E1369K NBD2 Neutral MRP6 ABCC6 G1327E NBD2 Disease MRP6 ABCC6 L1416R NBD2 Disease MRP6 ABCC6 R1268Q NBD2 Disease MRP6 ABCC6 R1461H NBD2 Disease MXR ABCG2 I206L NBD1 Neutral MXR ABCG2 P269S NBD1 Disease MXR ABCG2 Q141K NBD1 Neutral nsSNPs. Login to comment