Computational Neurobiology

Projects

ADMC: Large-scale metabolomics for studying AD

The Alzheimer Disease Metabolomics Consortium (ADMC) is a bold initiative that brings together leaders in Alzheimer’s disease (AD), clinical and basic research to work in close collaboration with centers of excellence in metabolomics, genetics, biochemistry, engineering, and bioinformatics. We aim to define metabolic failures across the trajectory of AD connecting peripheral and central nervous system changes to gain a deeper understanding of disease mechanisms.

In the ADMC, we are applying a multitude of different NMR- and MS-based targeted and non-targeted metabolomics and lipidomics platforms. For several of those, we have profiled blood compound concentrations in about 1,600 subjects of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study phases 1, GO, and 2, as well as in a subset of participants of the ROS/MAP cohorts. For the latter, we have used matched serum and brain samples in order to model metabolic interfaces between compartments. One central effort in our work is to contribute to standardized processing protocols in metabolomics. Following the NIH open science initiative and via multiple outlets (most prominently, the AMP-AD Knowledge Portal), we make all our data and code available to consortium members and the research community.

More information on the ADMC is available here.

AMP-AD: candidate target identification and prioritization using an integrated molecular atlas of AD

The central goal of the AMP-AD Target Discovery and Preclinical Validation Project is to shorten the time between the discovery of potential drug targets and the development of new drugs for Alzheimer’s disease treatment and prevention, by integrating the analyses of large-scale molecular data from human blood and brain samples with network modeling approaches and experimental validation. To achieve this goal, we have set out to develop a genetically anchored integrated molecular atlas of AD that intersects genetics with transcriptomic, proteomic, metabolomic, and phenotypic data. This concept makes use of reconstructed metabolic pathways derived by partial correlation analysis to interlink the different modules detected by genetic associations. To include large-scale datasets from a multitude of sources, we use the web-based SNiPA annotation tool (see Tools section) that integrates a long list of genomic, molecular, and phenotypic data. This data is extended by metabolic links yielded through the work of the ADMC, as well as with phenotypic readouts and genetic links thereof in the ADNI cohorts, as well as with transcriptomic and proteomic networks generated by our AMP-AD partners.

More information on AMP-AD is available here.

M2OVE-AD: Disease sub-classification for precision medicine in AD

Connecting metabolomics data to genomics, imaging and other omics data in a systems biochemical approach, we seek to sub-stratify and sub-classify AD stepping towards a precision medicine approach for disease management. The metabolome provides a readout on the current physiological status of an individual, and clustering subjects by their metabolic profiles can thus identify subpopulations and risk groups within a study cohort. Utilizing the rich phenotypic data available for ADNI participants, the derived clusters can be annotated with clinical features that distinguish between the identified groups. Already, several risk factors, such as female sex and APOE e4 status, have been shown to enable stratified analyses that can identify risk-group specific molecular changes linked to the trajectory of AD. The power of metabolomics in such endeavors lies in its sensitivity towards the individual genetic and environmental background. One prominent example for this is the highly significant metabolic imprint of sex. Using supervised and unsupervised learning techniques, we thus are dissecting patients into subgroups to tailor our candidate target selection approaches to group-specific risk profiles.

More information on M2OVE-AD is available here.

MDPMC: Large-scale metabolomics for studying major depressive and bipolar disorder

The Mood Disorders Precision Medicine Consortium (MDPMC) is an integrated team of academic researchers dedicated to improving the lives of patients who suffer from major depressive disorder and bipolar disorder. Comprised of leading experts in the fields of genetics, metabolomics, neuroimaging, bioinformatics, and clinical trials, the MDPMC’s mission is to achieve the precision medicine goals of individualizing treatment for mood disorder patients based on an integrated biological understanding of their illnesses and variation in response to treatments.

With the state of its funding, expertise, and organization, the MDPMC is poised to make substantial gains in our understanding of the molecular variation underlying the heterogeneity of mood disorders, and the pharmacogenetic and pharmacometabolomic basis for variation in treatment response. Moving forward, the MDPMC will apply these results to analyses of the gut microbiome and the fMRI and PET neuroimaging data from Helen Mayberg’s studies at Emory. Our vision of creating a richly integrated understanding of the sources of biological variation in mood disorders is the driving force behind the MDPMC, thereby enabling us to develop clinically actionable individualized treatment approaches for patients suffering from these conditions.

More information on the MDPMC is available here.

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