Functional changes in the activity or expression level of MATE2-K caused by genetic variants in the coding or basal promoter regions of this transporter may result in changes in the expression and activity levels of drugs that are substrates for the transporter. This study was conducted to identify and functionally characterize genetic variants in the coding and basal promoter regions of MATE2-K
in populations constituting four major ethnic groups. We identified four nonsynonymous variants in the coding region. The c.485C>T variant was found at a minor allele frequency of 5.6% in the African-American SOPHIE cohort (N
= 63) and 4.7% in the African-American patients in the Kaiser South East cohort (N
= 53). This novel variant has not been reported in any known SNP database, including the 1000 Human Genomes Project.26
Recently, Kajiwara et al.9
found that the nonsynonymous MATE2-K
variant Gly211Val was associated with a complete loss of transport activity, mainly because of a decrease in MATE2-K protein expression. Although the allelic frequency of Gly211Val in the Japanese sample was 1.7%, it was not observed in our cohort of 68 individuals with Chinese ancestry. In our study, the two nonsynonymous MATE2-K
variants, c.485C>T and c.1177G>A, were associated with significantly reduced transport activities in uptake assays using various cationic compounds (). Our results from immunoblotting analysis suggest that the decreased activity levels of c.485C>T were due largely to a decrease in protein expression (). Consistent with the reduced function of Pro162Leu and Gly393Arg, we observed that Pro162 (located in the hydrophobic membrane domain ()) and Gly393 (located in the hydrophilic cytoplasm site) are conserved across multiple species. By contrast, the variants that did not affect function occurred at sites that were not evolutionarily conserved (Supplementary Figure S3
online). The model showed that the Gly393Arg, which changes a glycine to positively charged arginine, would decrease the interaction of cationic ligands with MATE2-K (). This prediction is consistent with the dramatically reduced uptake of the cationic compounds in HEK-293 cells expressing the variant Gly393Arg, despite the observation that the total protein expression for Gly393Arg is comparable to that in MATE2-K reference cells. The model also predicted that two previously identified nonsynonymous variants9
would have deleterious effects on the transport function of MATE2-K.
The most common variant of the basal promoter region in this study, g.−130G>A, was associated with a significant increase in promoter activity in reporter assays (). Transcription factor binding site analysis suggested that MZF-1 could bind to the region encompassing g.−130G>A and that the binding affinity would be variant-dependent; MZF-1 would have a higher binding affinity for the g.−130G allele than for the minor allele. MZF-1 appears to be a repressor of the chloramphenicol acetyl transferase reporter gene in nonhematopoietic cell lines.27,28
MZF-1 contains 13 zinc fingers that interact with two different consensus sequences, AGTGGGGA and CGGGNGAGGGGGAA, for fingers 1–4 and fingers 5–13
We observed that MATE2-K
contains the sequence GGAGGAGGGGCTA, which is similar to the consensus sequence recognized by MZF-1. The variant g.−130G>A results in the sequence GGAGGAGGAGCTA, which is not as good a match as the reference is. Therefore, we predicted that MZF-1 would preferentially bind to the reference DNA sequence rather than to the variant sequence. The results of EMSA supported our hypothesis. The intensity of the DNA-MZF-1 complex was decreased in the presence of g.−130G>A (). We also demonstrated that MZF-1 represses MATE2-K
transcription (), establishing that this transcription factor may be important in regulating the expression of MATE2-K. Data from microarray experiments suggest that there are higher expression levels of MZF-1 (and MATE2-K) in kidney tissue samples as compared with cancer cell lines (Supplementary Table S3
online). These data suggest that MZF-1 could play a role in regulating MATE2-K expression levels in kidney tissue.
Previously, two separate studies have shown that the MATE1
intronic variants rs2289669 and rs8065082 have significant effects on metformin response,12,15
although the mechanism underlying the effects of these SNPs has not been determined. In our study group of 189 Caucasians, we observed an association of the intronic variant rs2289669 with relative change in HbA1c level, almost at the level of statistical significance (P
= 0.055) (Supplementary Table S2
online). Unlike Becker et al.
we focused only on patients who had recently been diagnosed with T2DM and initiated on metformin monotherapy. Examination of the MATE1
intronic variant rs2289669 showed that it is not in linkage disequilibrium with the MATE2-K
variants that we genotyped. Previously, we and others have shown that OCT1 reduced-function variants (R61C, 420Del, G401S, and G465R) and an OCT2 variant (A270S) are determinants of metformin pharmacokinetics16,18,24,29,30
and pharmacodynamics in healthy subjects.17
However, our results (Supplementary Table S2
online) also support findings from other studies showing that the variants may not have a significant effect on metformin response in patients with T2DM.12,14
To our knowledge, there have been no studies investigating the effects of genetic variants of MATE2-K
on metformin response. Although a small study in Japanese diabetes patients (n
= 48) found no association between either of the MATE1
coding region variants and metformin disposition,31
that study had only two patients who were carriers of the reduced-function MATE2-K
variant Gly211Val. In our study, we focused on the common 5′-UTR variant g.−130G>A, which is associated with enhanced promoter activity. Our hypothesis was that individuals with this variant would eliminate metformin more quickly, and that the therapeutic effect in these patients would therefore be lower. Our clinical results were consistent with our hypothesis. That is, patients who were homozygous for the g.−130G>A variant had a markedly weaker response to metformin (only a 2.7% relative reduction in HbA1c vs. 15% in all others) (N
= 189 Caucasians, (ii); Mann–Whitney test P
value = 0.002). With the available data, we were not able to determine whether a pharmacokinetic mechanism was responsible for the effect of the variant on metformin response and could not evaluate the contribution of this variant to metformin pharmacokinetics. The significance was weaker when we combined the Caucasian and African-American patients; however, when we removed the data for the five patients with the reduced-function nonsynonymous variant c.485C>T, the association became stronger (compare the results in (iii) and (iv)). We proposed that the reducedfunction coding variant c.485C>T would oppose the effects of the gain-of-function 5′-UTR variant. After adjusting for the relative change in HbA1c with average daily dose and ethnicity in a linear regression model, the significance of this association remained ().
Interestingly, the recent findings of the genome-wide association study of glycemic response to metformin in ~3,000 European T2DM patients did not identify any significant SNPs (P
< 0.0001) in metformin transporter genes.32
However the platform used in that study did not include the MATE2-K g.−130G>A SNP, nor were any SNPs on the platform in strong linkage disequilibrium with the g.−130G>A SNP. Other studies of genetic factors associated with response to metformin have not included MATE2-K g.130G>A. Our study demonstrates that rare variants in MATE2-K, such as c.485C>T, and in other transporters that are functionally deleterious, if not considered in the analyses, may add complexity to the identification of significant common SNPs associated with response to metformin.
In conclusion, a common gain-of-function 5′-UTR variant of MATE2-K, g.−130G>A, is significantly associated with poor glycemic response to metformin in newly diagnosed T2DM patients. Future studies to evaluate its effects on metformin plasma levels and clearance will provide important information on the mechanism by which MATE2-K contributes to the anti-diabetes effects of metformin and on how the g.−130G>A variant, in particular, modulates metformin response. The next key challenge in pharmacogenomics research is to demonstrate that the use of this genetic information will improve outcomes for patients and lead to personalized medicine in clinical practice.33