Genetic variation influences glutamate concentrations in brains of patients with multiple sclerosis

Abstract

Glutamate is the main excitatory neurotransmitter in the mammalian brain. Appropriate transmission of nerve impulses through glutamatergic synapses is required throughout the brain and forms the basis of many processes including learning and memory. However, abnormally high levels of extracellular brain glutamate can lead to neuroaxonal cell death. We have previously reported elevated glutamate levels in the brains of patients suffering from multiple sclerosis. Here two complementary analyses to assess the extent of genomic control over glutamate levels were used. First, a genome-wide association analysis in 382 patients with multiple sclerosis using brain glutamate concentration as a quantitative trait was conducted. In a second approach, a protein interaction network was used to find associated genes within the same pathway. The top associated marker was rs794185 (P < 6.44 x 10(-7)), a non-coding single nucleotide polymorphism within the gene sulphatase modifying factor 1. Our pathway approach identified a module composed of 70 genes with high relevance to glutamate biology. Individuals carrying a higher number of associated alleles from genes in this module showed the highest levels of glutamate. These individuals also showed greater decreases in N-acetylaspartate and in brain volume over 1 year of follow-up. Patients were then stratified by the amount of annual brain volume loss and the same approach was performed in the 'high' (n = 250) and 'low' (n = 132) neurodegeneration groups. The association with rs794185 was highly significant in the group with high neurodegeneration. Further, results from the network-based pathway analysis remained largely unchanged even after stratification. Results from these analyses indicated that variance in the activity of neurochemical pathways implicated in neurodegeneration is explained, at least in part, by the inheritance of common genetic polymorphisms. Spectroscopy-based imaging provides a novel quantitative endophenotype for genetic association studies directed towards identifying new factors that contribute to the heterogeneity of clinical expression of multiple sclerosis.

Figure 1

Genome-wide scan for allele frequency differences related to in vivo glutamate concentration in multiple sclerosis brains. (A) P-values from the linear regression with glutamate concentration, controlled by disease duration, age of onset and DRB1 status. (B) Quantile–quantile plots of these test statistics.

Figure 2

Module 14. A graphical representation of the overall highest scoring module from the protein interaction network. Circles represent proteins and lines represent interactions among them. Proteins are coloured according to their relationship to glutamate. Green = glutamate receptor and transporter organization; red = TGF-β signalling; pink = regulators of glutamatergic synaptic activity; yellow = glutamate receptors; blue = axon guidance; grey = unclassified.

Figure 3

Domain knowledge scores. Mean DKSs were calculated for genes in Module 14 (black bar), and for the top associated genes from the same protein network (whether they interact or not) (grey bar). Also, the mean DKS of the top associated genes from the original GWAS is shown. The mean DKS of genes in Module 14 is significantly higher than those of the other two lists of associated genes (Welch’s t-test, bars represent SEM).

Figure 4

Correlation between glutamate genetic score and relevant variables. (A) Correlation of glutamate genetic scores with grey matter glutamate concentration. (B) Correlation of genetic scores with NAA change over 1 year. Genetic scores explain more variance in NAA decline than expected given the a priori correlation between glutamate level and NAA decline. (C) Similarly, correlation between genetic scores and brain atrophy was significant and higher than that expected from a priori correlation between glutamate level and brain atrophy.