Alzheimer’s disease (AD) is a neurodegenerative disease affecting 1 in 9 people over the age of 65. AD is usually diagnosed after the onset of noticeable neuropsychiatric deterioration, such as memory loss, speech impairment, and social withdrawal. Early changes in the brain are suspected to begin over 20 years before any detectable symptoms of the disease. Our lab focuses on two pertinent challenges in the field: Understanding the molecular alterations underlying the disease in order to aid the development of novel therapies and developing early detection approaches that would allow for timely interventions.
Our research is funded by NIH/NIA grants and integrated into several national and international consortia, including the Alzheimer’s Disease Metabolomics Consortium (ADMC) and the Alzheimer’s Gut Microbiome Project (AGMP). We analyze omics data from large cohort studies, including ADNI, ROS/MAP, Mayo Clinic, UK Biobank, and others.
Recently, we published a landscape of metabolic alterations in AD, based on 500 human post-mortem brain samples taken from the ROS/MAP cohort. Currently, we are exploring AD-associated metabolic alterations across four different brain regions, which will provide a more comprehensive picture of AD’s metabolic imprint in the brain. These brain-based association analyses provide novel metabolic insights into AD, inform follow-up experiments in model systems, and potentially pave the way for novel therapeutic strategies.
In other projects, we are investigating personalized dietary or lifestyle interventions that can increase individual resilience against bioenergetic disturbances in AD, studying the molecular crosstalk across the blood-brain barrier, determining the role of the gut microbiome in AD, and developing novel network-based approaches for drug repositioning.
Our lab works on a variety of further research topics, including:
Type 1 and Type 2 diabetes research
Biomarkers and mechanisms of COVID-19 severity
Inference of protein glycosylation pathways from large-scale glycomics data
Novel approaches for network inference based on Gaussian graphical models (GGMs)
Statistical approaches for the preprocessing and analysis of omics data, and metabolomics in particular
Multi-omics integration methods
Variational Autoencoders & Deep Learning