Characterize the Complexities of Brain Biology

“By employing untargeted metabolomics and comparing differences in metabolite levels between tissues, we were able to map a network of perturbed metabolites which show that the function of this protein extends far beyond citrate transport, as our data point toward abnormalities in bile acids, nucleotide metabolism, and transport and/or synthesis of fatty acids.”

Milosavljevic S, Glinton KE, Li X, Medeiros C, Gillespie P, Seavitt JR, Graham BH, Elsea SH
Milosavljevic S, Glinton KE, Li X, Medeiros C, Gillespie P, Seavitt JR, Graham BH, Elsea SH. Untargeted Metabolomics of Slc13a5 Deficiency Reveal Critical Liver–Brain Axis for Lipid Homeostasis. Metabolites. 2022; 12(4):351. https://doi.org/10.3390/metabo12040351 Available under CC BY 4.0.

Many neurological disorders are attributable to genetic variation, with varying degrees of heritability, or the proportion of disease etiology due to genetic contribution. Common genetic variants with small effect size have a compounding impact on disease risk. Functional genomic studies, studies that include endophenotypic (metabolomic), transcriptomic, epigenomic, or proteomic data, link the pathways impacted by these variants to disease.

Alzheimer’s disease (AD) is one such complex neurogenetic disorder. Most studies focus on diagnosed AD patients. An article published in Scientific Reports performed a multiomic analysis on healthy adults to understand how genetic risks of AD are associated with physiological changes. The metabogenomic analysis, the integration of metabolomics and genomics data to link and infer the relationship between the presence of genomic variants and the measurable alterations in metabolites within biochemical pathways, included known genetic variants, clinical laboratory blood tests, and metabolomics. A phenome-wide association study (PWAS) linked known AD genetic variants to blood marker data and revealed 33 statistically significant single nucleotide polymorphisms (SNPs) that were associated with lipid metabolism and immune response systems, pointing to biomarkers that could be detected in early adulthood (srefer to figure). The study results highlight targets for AD prevention and new drug targets and contributes much to functional genomic knowledge of AD.

Figure 1. Statistically significant SNP-analyte associations after correcting for multiple testing (threshold FDR-adjusted p-value = 0.05), by SNP. Top panel: log-transformed beta-coefficient from the linear regression model adjusted for sex, age, and genetic principal components 1–4; markers above the zero line (orange) indicate analytes that increased in value with the minor allele, while markers below the line indicate markers that decreased in value. Second panel: FDR-adjusted − log10 p-value; orange line at FDR-p = 0.05. Proteins = red, metabolites = blue, clinical chemistries = purple. Metabolite codes: DG diacylglycerol, LC lactosylceramide, o oleoyl; a arachidonoyl, g glycerol, l linoleoyl, p palmitoyl. Third panel: minor allele frequency (MAF). Bottom panel: Total sample size for each analyte-SNP regression.

Figure from Heath L, Earls JC, Magis AT, et al. Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90. Sci Rep. 2022;12(1):6117. Published 2022 Apr 12. doi:10.1038/s41598-022-09825-2 Available under CC BY 4.0.

Metabolon has contributed extensively to publications ranging from basic research to clinical trials.