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Characterize the Complexities of Brain Biology

Explore the pivotal role of metabolomics in neuroscience research, and learn how metabolomics can accelerate neuroscience outcomes.

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Featured Neuroscience Resources

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Metabolomics for Neuroscience Volume 1

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On Demand Webinar: Untargeted Metabolomics in Large Cohort Population Health Research

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Applications in Gut-Brain-Axis Research

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Metabolomics in Neurology

Functional markers for neurological research can be elusive. Due to the complex and fragile nature of the brain, sampling from living cohorts, navigating the blood-brain barrier, and characterizing the brain phenotype can be difficult or impossible for most studies to accomplish.

Metabolomics provides a functional endpoint that neurological researchers can easily incorporate into their studies. Small molecule biochemistry can elucidate the association and significance of a metabolomic pathway and its correlating impact on the presence and severity of neurological conditions. With markers present in other, less-invasive biological matrices that support neurological research, and the growing importance of the microbiome to explore associations across the gut-brain axis, what more can you find with metabolomics?

Longitudinal Observation of Pathological Mechanisms
Biomarker Identification
Associating Metabolic Pathways to Neurological Conditions

Longitudinal Observation of Pathological Mechanisms

Metabolomics helps researchers look deeper into how various neurological conditions develop by providing detailed metabolic profiles. Observing changes in metabolic pathways can shine a light on how cellular processes change and reveal new potential targets for therapy.

For example, metabolomics was able to identify a compound produced by a microbe in a mouse model of amyotrophic lateral sclerosis that may rescue motor dysfunction. Blacher, Et al. Nature 2019, 572:474-484

Depleting the microbiome of Sod1-Tg ALS mouse model exacerbates motor symptoms

Biomarker Identification

Biomarker identification via global metabolomics can highlight the connection between metabolomics and neurological disease as well as open the door to new therapies. Leveraging global metabolomics for a comprehensive viewpoint will support the development of non-invasive diagnostics and therapeutics that have the capability to improve the lives of patients.

With our industry-leading library of over 5,400 metabolites, Metabolon has the broadest coverage and capability to see potential biomarkers in your data. Metabolon’s deep experience in metabolomics plays a vital role in future advancements of the neuroscience field.

Associating Metabolic Pathways to Neurological Conditions

Neurological Drug Development and Personalized Medicine

Metabolomics can reveal alterations in metabolic pathways, aiding in the identification of potential targets for additional research and the development of new drugs Additionally, it allows treatments to potentially be adapted by identifying specific metabolic changes within different neurological conditions.

Using Predictive Modelling and Prognostication

Using metabolomic data, we can model and predict how a disease might progress and how it might respond to treatment. This might also help identify markers that can give insights into the expected long-term outcomes of neurological conditions.

Assessing the Neurological Impact of Exposome

Using metabolomic data, we can model and predict how a disease might progress and how it might respond to treatment. This might also help identify markers that can give insights into the expected long-term outcomes of neurological conditions.

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“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.

Metabolomics Applications for Neuroscience Research

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Alzehimer’s Disease

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Autism Spectrum Disorder

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Amyotrophic Lateral Sclerosis (ALS)

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Epilepsy

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Parkison’s Disease

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Neuropsychiatric Disorders

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Stress

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Huntington’s Disease

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Chronic Fatigue Syndrome

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Alzehimer’s Disease

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Austism Spectrum Disorder

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Amyotrophic Lateral Sclerosis (ALS)

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Epilepsy

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Parkinson’s Disease

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Neuropsychiatric Disorders

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Stress

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Huntington’s Disease

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Chronic Fatigue Syndrome

CASE STUDY

Metabolomics Helps Make Sense of Complex Neurogenetic Diseases

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.

Neuroscience Research Publications and Citations

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

See All Neuroscience Research Publications

Neuroscience Knowledge Base

Dive deeper by viewing our case studies and webinars. Learn more about how Metabolon furthers neuroscience research.

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Metabolomics for Neuroscience Volume 1

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On Demand Webinar: Untargeted Metabolomics in Large Cohort Population Health Research

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Applications in Gut-Brain-Axis Research

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References

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