1. Genetic Variation Influences Glutamate Concentrations in Brains of Patients with Multiple Sclerosis Robby Bonanno

2. L-Glutamate Mammalian Neurotransmitter Ligand-gated ion channels G-Protien coupled receptors Learning and Memory Processes Excess in Synaptic Cleft neuroaxonal injury and death disorders (contributes to Multiple Sclerosis)

3. Multiple Sclerosis Common disease in young adults (esp. females) Fatty myelin sheaths around axons in CNS damaged (by bodies own immune system causing scarring in white matter of brain and spinal cord) Elevated L-Glutamate Conc. Any neurological symptoms possible (axons no longer transmit signals) Combination of genetic and imaging methods/data to better understand neurological diseases N-acetylaspartate (NAA) Marker indicating neuroaxial damage

4. Hypothesis “We hypothesized that variation in brain glutamate concentrations measured by in vivo magnetic resonance spectroscopy could be used as a quantitative trait and combined with available high-density genotyping data to identify genetic contributions to glutamate-mediated toxicity in multiple sclerosis.”

5. Methods Used 382 subjects Genotypes determined and reported in Genome Wide Association Study (GWAS) Sentrix HumanHap550 BeadChip Brain Volume Measured (along with change in volume over time) Glutamate and NAA concs. measured Two-dimensional echo time-averaged proton spectroscopic imaging

6. Methods Each gene product in a highly specified protein interaction network was assigned a number corresponding to the P-value of the most strongly associated single-nucleotide polymorphism (SNP) for that gene with the trait (only P-values < 0.05 were considered) Groups of gene products interacting with glutamate were also identified Risk allele for each associated SNP - The allele showing the highest frequency among patients with high baseline in vivo glutamate levels.

7. The top associated marker was rs794185 (P56.44107), a SNP in chromosome 3p26.2 that maps to intron 6 of the gene coding for sulphatase modifying factor 1 (SUMF1). SUMF1 mutation leads to multiple sulphatase deficiency, a lysosomal storage disorder. DNA variants in this gene may indirectly regulate extracellular glutamate (by altering the activity of steroid sulphatases) Network-based approach did indeed identify biologically related genes (Modules based on Protein Interaction Network - PIN) Methods

8. Determination of Module Relevance Thirty-four modules were found to be significantly associated with in vivo glutamate concentration. To assess the relative importance of these modules, several criteria were considered. – literature search was done to determine relevance of component genes with glutamate biology. – gene association with related phenotypes (NAA decline and brain atrophy change) were measured. Module 14 (composed of 70 genes) was the top scoring module through these considerations.

9. Results A module-specific genetic score was computed for each patient (the score was derived from the number of risk alleles carried at each gene represented in the module). As predicted, patients with the highest glutamate levels in grey matter were more likely to display the highest genetic scores.

10. 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-b signalling; pink = regulators of glutamatergic synaptic activity; yellow = glutamate receptors; blue = axon guidance; grey = unclassified.

11. Results A genetic score quantified the genetic load of a given individual (considers the number of alleles associated with the trait - glutamate concentration). Individuals with higher genetic scores = more likely to show elevated glutamate levels. The significant correlation between genetic scores and NAA decline over a year is above what would be expected Suggests that any effect these genes have on NAA is mediated by glutamate levels.

12. 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. (C) Correlation between genetic scores and brain atrophy was significant and higher than that expected

13. Experimental Modifications Two further genome-wide association studies were conducted Grouped study participants by high or low brain atrophy (neurodegeneration). – This data revealed that SUMF1 is highly associated with glutamate concentrations only in the group with high neurodegeneration. – Suggests that other mechanisms may be involved in the regulation and maintenance of glutamate concentrations in patients with less neurodegeneration.

14. Summary Identified genetic variation in genes associated with in vivo glutamate (measured in grey matter of multiple sclerosis patients) Common variations in a limited group of functionally related genes contribute significantly to NAA decay and brain volume in multiple sclerosis. Show importance of glutamate in multiple sclerosis biology and generate new hypotheses that link genetic variation with disease progression