Abstract Text:
Background: Age-related neurological illnesses like Alzheimer's disease (AD) are steadily becoming more prevalent among the elderly population in India and around the world. Cognitive deficits are caused by a progressive loss of normal brain functions. Increased production of amyloid (Aβ) and the development of neurofibrillary tangles (NFTs) are the two most significant pathogenic events that take place during AD progression. Until today, many attempts have been made towards discovering novel targets that are more precise and could identify the progression of the disease at a very early stage.
Method: In the present study, the Human Protein Atlas (HPA) database was used to filter out the total human secretome. Using the single-cell type proteome of the HPA database, neuronal proteins were compared with the total human secretome to discover overlapping proteins and their respective genes. Further, Alzheimer's disease-associated genes were extracted using the DisGeNet database and ranked according to their gene-disease association (gda) score. Further, different proteoforms of Aβ were used to treat neuronal cells in vitro to mimic the early stages of AD-like pathology. The expression of secretory protein genes in an in vitro model system has been analysed.
Result: Out of 1678 genes, 24 neuronal genes were identified that encoded secretory proteins. These genes were further categorized according to their protein function. Specific primers for the top GDAS score genes were designed, and expression analyses were conducted. Analyses revealed modulation in the expression of the APOE, MAPT, NPY, NGF, and RELN genes.
Conclusion: The aim of this study was to discover early-stage biomarkers for Alzheimer's disease. In the present study, we have tried to identify modulation in the genes coding for secretory proteins, which might be reflected at protein levels as well. Currently, our lab is working on fully understanding the modulation of the AD secretome, which would enable us to identify AD-specific early-stage biomarkers.