Genome-wide meta-analysis of homocysteine and methionine metabolism identifies five one carbon metabolism loci and a novel association of ALDH1L1 with ischemic stroke

Abstract

Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of common diseases such as stroke, cardiovascular disease (CVD), cancer and dementia. The FOCM is the sole source of de novo methyl group synthesis, impacting many biological and epigenetic pathways. However, the genetic determinants of elevated tHcy (hyperhomocysteinemia), dysregulation of methionine metabolism and the underlying biological processes remain unclear. We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism, characterized by post-methionine load test tHcy, in 2,710 participants from the Framingham Heart Study (FHS) and 2,100 participants from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, and then examined the association of the identified loci with incident stroke in FHS. Five genes in the FOCM pathway (GNMT [p = 1.60 × 10(-63)], CBS [p = 3.15 × 10(-26)], CPS1 [p = 9.10 × 10(-13)], ALDH1L1 [p = 7.3 × 10(-13)] and PSPH [p = 1.17 × 10(-16)]) were strongly associated with the difference between pre- and post-methionine load test tHcy levels (ΔPOST). Of these, one variant in the ALDH1L1 locus, rs2364368, was associated with incident ischemic stroke. Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism. Additionally, a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP. Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism, and possibly the epigenome, impacting disease risk. These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets.

Figure 1. Meta-analysis and chromosome 2, 3, 6, 7 and 21 regional association of single nucleotide polymorphisms (SNPs) for ΔPOST in both VISP and FHS cohorts.

A sample size-weighted meta-analysis was used. (A) Manhattan plot of meta-analysis association results for the combined VISP and FHS samples. Association p-values are noted on the Y axis (-log₁₀ P value), with points above the dashed line indicating SNPs reaching or exceeding genome-wide significance (P≤5×10⁻⁸). (B–F) Locus Zoom plots showing the regional association of chromosomes 2, 3, 6, 7 and 21. Y-axis shows –log₁₀ p-value≤0.01. X-axis shows Mb position on each chromosome. Each circle represents an independent SNP and color shading represents r² values.

Figure 2. Haplotype analysis of Chr6 SNPs significantly-associated with ΔPOST in the VISP cohort.

Haplotype analyses of the 10 most-associated SNPs, which are all genome wide significant, from chromosome 6, were performed using Haploview version 4.2 . (A) Shows results for the chromosome 6 genomic haplotype structure in VISP, encompassing the GNMT gene. Genetic coordinates, Entrez gene structure, haplotype blocks and linkage disequilibrium (LD) pattern between SNPs are shown. Within LD pattern, r² values are shown and are represented by shading. The darker the shading the closer the r² value is to 1.0. All SNPs assessed were genotyped. (B) Shows haplotypes generated by Haploview for haplotype blocks 1 and 2. Haplotype block 2 is characterized by two major haplotypes, which account for 80% of haplotypes observed. (C) Shows the mean ΔPOST values in the VISP sample for each of the 2 major haplotype block 2 haplotypes. (_*) p≤0.001 by student's T-test; error bars represent standard deviation from the mean (SD).

Figure 3. GNMT promoter analysis of major haplotype groups.

(A) Histogram shows mean luciferase activity of the high-methionine-metabolizing haplotype (TCCGGAT) and the low-methionine-metabolizing haplotype (CGTCTGC) represented by constructs GNMT^ΔHighLuc and GNMT^ΔLowLuc cultured in standard DMEM with L-methionine (0.2 mM). (B) Shows GNMT qPCR analysis in HepG2 cells. Culturing conditions are as follows: (+)Met = standard complete DMEM cultured for 48 hours, (−)Met = complete DMEM without L-Methionine culture for 48 hours, (−)Met ADD 0.2 = DMEM without L-Methionine culture for 24 hours, addition of L-Methionine at 0.2 mM for 24 hours, (−)Met ADD 0.4 = DMEM without L-Methionine culture for 24 hours, addition of L-Methionine at 0.4 mM for 24 hours, (−)Met ADD 0.8 = DMEM without L-Methionine culture for 24 hours, addition of L-Methionine at 0.8 mM for 24 hours. (C) (−)Met GNMT^ΔHighLuc and (−)Met GNMT^ΔLowLuc represent HepG2 cells transfected with GNMT^ΔHighLuc and GNMT^ΔLowLuc constructs and cultured without L-methionine for 48 hours. ADD 0.2 GNMT^ΔLowLuc and ADD 0.2 GNMT^ΔHighLuc represent HepG2 cells transfected with GNMT^ΔHighLuc and GNMT^ΔLowLuc constructs cultured without L-methionine for 24 hours and with 0.2 mM L-methionine for 24 hours. * P<0.05 by students t-test. N = 3 biological replicates for all analyses. Error bars represent standard deviation from the mean (SD).

Figure 4. Epigenetic Evaluation of rs11752813.

(A) Average ΔPOST (µmol/L) in VISP trial based on rs11752813 genotype. (*) p<0.001 by one-way ANOVA and all genotypes are significantly different by students t-test. (B) Percent methylation analyzed by bisulfite pyrosequencing of n = 23 individuals (G/G) and n = 6 individuals (C/C). (C) Linear regression analysis of percent methylation of rs11752813 based on genotype and ΔPOST (µmol/L).

Figure 5. Risk score in FHS and VISP studies.

(A) Distribution of risk scores among the FHS sample shows a normal distribution. Y-axis represents the number of individuals who have the given risk score seen on the x-axis. (B) X-axis represents the number of risk variants per subject in FHS. Y-axis represents the average ΔPOST value for each group containing a specific risk score. (C) Distribution of risk scores among the VISP sample shows a normal distribution. Y-axis represents the number of individuals who have the given risk score seen on the x-axis. (D) X-axis represents the number of risk variants per subject in VISP; Y-axis represents the average ΔPOST value for each group containing a specific risk score. Risk variants considered were SNPs at each of the 5 loci most significantly associated with ΔPOST in the meta-analysis. For each SNP a score of 0 was applied for homozygous non-risk variant, 1 for heterozygous at risk variant and 2 for homozygous at risk variant, derived from dosage values of 1000genomes imputation.