Development of a Novel Biomarker for an early detection of Alzheimer disease

Abstract

BackgroundAlzheimer’s disease (AD), a neurodegenerative disease, is the most common form of dementia among older patients and is the 6th leading cause of death in the US. Almost 1 in 10 people over the age of 65 have AD and every 65 second someone in the US develop AD. Worldwide 30 million people have AD, and 5.8 million American are living with this disease where by 2050, it is projected to be 14 million. Problems in AD and other forms of dementia involve an irreversible brain tissue damage and destruction, leading to progressive cognitive impairment led difficulty of performing simple tasks. In 2019, AD and other dementia in US costed $290 billion. By 2050, these costs could rise as high as 1.1 trillion. Due to technological advancements in artificial intelligence, the computer can not only plan & predict the path or course for a robot, but for multiple proteins and their metabolites. Several digital models and simulations can determine the interactions and commonality between proteins of all sorts. The goal of this project is to identify biological markers by metabolomic meta‐analysis that correlate with the progression of AD and be integrated to a prototype biosensor capable of detecting the stable biomarker for an early detection of dementia. This project analyzes the commonalties in dementia related proteins through data mining and analysis of the cerebrospinal fluid and blood metabolome to see (when altered in Alzheimer disease) the metabolic reactions that could potentially relate to phenotypes detectable for an early onset Alzheimer's. Along with that, the project utilizes the novel findings to assemble a detection system via electrochemical analysis by observing the difference in the relative resistance between varying solution of key analyte changed in Alzheimer’s patient.Material and MethodsPlastic straw, Hydrogel, Electrical resistant measuring machine and specialized Electrode. Protein sequences analysis: Information on the proteins that are associated with the Alzheimer diseases were obtained from PubMed; Amino acid sequences of these proteins (in FASTA format) were obtained through ExPASy. The amino acid sequences of proteins in FASTA format were obtained from the UniProt database in Expasy. Clastalw‐Multiple Alignment: The commonality in the protein sequences was deduced obtained by aligning multiple protein sequences through the Clustal‐W.Results and ConclusionBioinformatics tools and computer algorithms shows three dimensional structures of Alzheimer’s disease associated proteins where majority of them were associated with the membrane. Mutational analysis indicated that most of them in AD fall into transmembrane regions of the protein which may be due to change in lipid composition of the cells. Metabolomics studies indicated that the relative abundance of lipid metabolites was relatively low in the blood of AD compared to healthy or MCI. In contrast, the cerebrospinal fluid levels of lipid metabolites were higher in AD/dementia patients compared to their healthy counterparts. Increased lipid metabolomic disturbances in CSF also correlated with enlarged ventricle filled with CSF in AD patients. Digital models and simulations studies of this project demonstrated the interactions and commonality between proteins of AD and demonstrated their folding. Analysis of the CSF and blood metabolome provided a strong AD phenotypes detectability for early onset Alzheimer and may be useful as detectable marker for the cognitive impairment.