Friday, April 19, 2024
4/19/2024
PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) is the most common form of dementia in the elderly population and 6th leading cause of death in the US. Despite extensive research, there are currently no treatments that slow or stop the development of AD. With the number of AD cases expected to triple in the next 30 years, there is a pressing need to diagnose AD early in the preclinical stage. While several peptide and protein biomarkers in cerebrospinal fluid (CSF) have been used for AD diagnosis, an unequivocal diagnosis in the early phases of AD is still lacking. Perhaps more importantly, the discovery and establishment of reliable biomarkers capable of monitoring progression and degree of cognitive impairment as well as potential efficacy of therapy remains a major challenge. Furthermore, compared to CSF, serum sample provides an appealing source for biomarker discovery and screening due to less invasiveness and easier access. However, the correlation between CSF and blood protein/peptide biomarkers as well as changes in the brain structure/function and cognition in AD is not well established. In order to address these challenges and fill in existing knowledge gaps, we propose to employ a multi-faceted approach combining a suite of mass spectrometry-based technologies enabled by innovative multiplexed tagging strategies, improved sampling and separation strategies and clinically-available measures to discover, identify and evaluate candidate biomarkers of AD in CSF/serum obtained from asymptomatic cognitively-healthy middle-aged adults, older cognitively-normal adults, and patients with mild cognitive impairment (MCI) and AD. We propose the following specific aims: Specific Aim 1 – To develop novel enrichment strategies and complementary separation modalities for enhanced coverage of glycoproteome and posttranslational modification crosstalk analysis in paired CSF and serum samples from subjects in control, preclinical, MCI, AD groups, respectively. Specific Aim 2 – To enhance quantitative glycoproteomic analysis of low-abundance species in CSF and serum samples via innovative dimethylated leucine (DiLeu) boosting and BoxCar data-independent acquisition (DIA) strategies along with machine learning classification algorithms for improved diagnosis of AD. Specific Aim 3 – To validate candidate AD biomarkers, in CSF and serum samples collected from individuals with MCI and dementia, using targeted quantitative proteomics approaches enabled by isotopic DiLeu tags and affinity-bead assisted MS immunoassay along with association with AD-related clinical, cognitive and neuroimaging measures. This project uniquely integrates advances in MS-based multiplexed quantitative glycoproteomics and bioinformatics tools with neuroimaging and clinical measures to enable more comprehensive discovery and validation of CSF and serum biomarkers in AD. These biomarkers would be invaluable in improving our understanding of AD pathogenesis, designing therapeutics for patient care and more efficient clinical trials of disease modifying therapies. The advances in technology and new insights will have broad impact on translational medicine.
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