Silica Nanoparticle Exposure Implicates β-Amyloid (1-42) Inbound and the Accelerating Alzheimer's Disease Progression in Mice Overexpressing Mutated Forms of Human Amyloid Precursor Protein and Presenilin 1 Genes.
Wei WeiHang SunBingwei YangChengyu ZhuErqun SongYang SongPublished in: Chemical research in toxicology (2024)
The increasing nanoparticle (NP) applications in the biomedical field have become an emerging concern regarding human health. NP exposure may play a role in the accelerating Alzheimer's disease (AD) progression; however, the etiology of this disorder is complex and remains largely unclear. Here, we identified that intravenous injection of silica NPs (SiNPs) caused the blood-brain barrier breakdown via downregulating tight junction-related gene expressions. Meanwhile, SiNPs upregulate the transport receptor for advanced glycation end products (RAGE) that govern the β-amyloid (Aβ) influx to the brain; however, low-density lipoprotein receptor-related protein 1 (LRP1) that controls the efflux of Aβ from the brain was not affected. Consequently, an increase in Aβ burden in the brain of SiNP-challenged APP/PS1 mice was found. Intriguingly, plasma apolipoprotein E (ApoE) adsorbed on the surface of SiNPs partially relieves this effect. Using ApoE knockout (ApoE -/- ) mice, we confirmed that SiNPs covered with serum without ApoE showed further elevated AD symptoms. Together, this study offered a compilation of data to support the potential risk factors of NP exposure and AD pathology.
Keyphrases
- cognitive decline
- human health
- risk factors
- high fat diet
- high fat diet induced
- low density lipoprotein
- resting state
- white matter
- risk assessment
- mild cognitive impairment
- wild type
- genome wide
- endothelial cells
- climate change
- functional connectivity
- binding protein
- cerebral ischemia
- big data
- type diabetes
- copy number
- electronic health record
- machine learning
- protein protein
- transcription factor
- physical activity
- adipose tissue
- pluripotent stem cells
- deep learning
- skeletal muscle
- data analysis