https://www.selleckchem.com/products/n-formyl-met-leu-phe-fmlp.html COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway. IFITM3 is a viral restriction protein that enables sequestration of viral particles and subsequent trafficking to lysosomes. Recently, IFITM3 upregulation was found to induce gamma - secretase activity and the production of amyloid beta. The purpose of this study was to determine whether dysregulation of IFITM3-dependent pathways was present in neurons and peripheral immune cells donated by AD patients. As a secondary aim, we sought to determine whether these perturbations could be induced by viruses, including SARS-CoV-2. Gene set enrichment analyses (GSEA) previously performed on publicly available transcriptomic data from tissues donated by AD patients were screened for enriched pathways containing IFITM3. Subsequently, signature containing IFITM3, derived from entorhinal cortex (EC) neurons containing neurofibrillary tangles (NFT) was screened for overlap with curated, publicly