A "silent" polymorphism in the MDR1 gene changes substrate specificity
ABSTRACT Synonymous single-nucleotide polymorphisms (SNPs) do not produce altered coding sequences, and therefore they are not expected to change the function of the protein in which they occur. We report that a synonymous SNP in the Multidrug Resistance 1 (MDR1) gene, part of a haplotype previously linked to altered function of the MDR1 gene product P-glycoprotein (P-gp), nonetheless results in P-gp with altered drug and inhibitor interactions. Similar mRNA and protein levels, but altered conformations, were found for wild-type and polymorphic P-gp. We hypothesize that the presence of a rare codon, marked by the synonymous polymorphism, affects the timing of cotranslational folding and insertion of P-gp into the membrane, thereby altering the structure of substrate and inhibitor interaction sites.
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ABSTRACT: Previous studies reported the associations between the ATP-binding cassette sub-family B member 1 (ABCB1, also known as MDR1) polymorphisms and their haplotypes with risk of response to antiepileptic drugs in epilepsy, however, the results were inconclusive. The Pubmed, Embase, Web of Science, CNKI and Chinese Biomedicine databases were searched up to July 15, 2014. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a fixed-effects or random-effects model based on heterogeneity tests. Meta-regression and Galbraith plot analysis were carried out to explore the possible heterogeneity. A total of 57 studies involving 12407 patients (6083 drug-resistant and 6324 drug-responsive patients with epilepsy) were included in the pooled-analysis. For all three polymorphisms (C3435T, G2677T/A, and C1236T), we observed a wide spectrum of minor allele frequencies across different ethnicities. A significantly decreased risk of AEDs resistance was observed in Caucasian patients with T allele of C3435T variant, which was still significant after adjusted by multiple testing corrections (T vs C: OR=0.83, 95%CI=0.71-0.96, p=0.01). However, no significant association was observed between the other two variants and AEDs resistance. Of their haplotypes in ABCB1 gene (all studies were in Indians and Asians), no significant association was observed with AEDs resistance. Moreover, sensitivity and Cumulative analysis showed that the results of this meta-analysis were stable. In summary, this meta-analysis demonstrated that effect of C3435T variant on risk of AEDs resistance was ethnicity-dependent, which was significant in Caucasians. Additionally, further studies in different ethnic groups are warranted to clarify possible roles of haplotypes in ABCB1 gene in AEDs resistance, especially in Caucasians.PLoS ONE 01/2015; 10(3):e0122043. DOI:10.1371/journal.pone.0122043 · 3.53 Impact Factor
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ABSTRACT: Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data - ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin's theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics. Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches. This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems - from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.BMC Evolutionary Biology 05/2015; 15(1):76. DOI:10.1186/s12862-015-0352-y · 3.41 Impact Factor