Although there are indications of a trend towards less severe acute respiratory symptoms and a decline in overall lethality from the novel Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), more and more attention has been paid to the long COVID, including the increased risk of Alzheimer's disease (AD) in COVID-19 patients. In this study, we aim to investigate the involvement of N-terminal amyloid precursor protein (APP) in SARS-CoV-2-induced amyloid-β (Aβ) pathology. Utilizing both in vitro and in vivo methodologies, we first investigated the interaction between the spike protein of SARS-CoV-2 and N-terminal APP via LSPR and CoIP assays. The in vitro impacts of APP overexpression on virus infection were further evaluated in HEK293T/ACE2 cells, SH-SY5Y cells, and Vero cells. We also analyzed the pseudovirus infection in vivo in a mouse model overexpressing human wild-type APP. Finally, we evaluated the impact of APP on pseudovirus infection within human brain organoids and assessed the chronic effects of pseudovirus infection on Aβ levels. We reported here for the first time that APP, the precursor of the Aβ of AD, interacts with the Spike protein of SARS-CoV-2. Moreover, both in vivo and in vitro data further indicated that APP promotes the cellular entry of the virus, and exacerbates Aβ-associated pathology in the APP/PS1 mouse model of AD, which can be ameliorated by N-terminal APP blockage. Our findings provide experimental evidence to interpret APP-related mechanisms underlying AD-like neuropathology in COVID-19 patients and may pave the way to help inform risk management and therapeutic strategies against diseases accordingly.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- coronavirus disease
- induced apoptosis
- mouse model
- cell cycle arrest
- endoplasmic reticulum stress
- signaling pathway
- cell death
- transcription factor
- binding protein
- protein protein
- small molecule
- mass spectrometry
- single molecule
- artificial intelligence
- deep learning
- wild type
- pi k akt
- electronic health record
- cognitive decline
- induced pluripotent stem cells
- high throughput
- machine learning