ABSTRACT
Declines in physiological functions are the predominant risk factors for age-related diseases, such as cancers and neurodegenerative diseases. Therefore, delaying the aging process is believed to be beneficial in preventing the onset of age-related diseases. Previous studies have demonstrated that Graptopetalum paraguayense (GP) extract inhibits liver cancer cell growth and reduces the pathological phenotypes of Alzheimer's disease (AD) in patient IPS-derived neurons. Here, we show that GP extract suppresses ß-amyloid pathology in SH-SYS5Y-APP695 cells and APP/PS1 mice. Moreover, AMP-activated protein kinase (AMPK) activity is enhanced by GP extract in U87 cells and APP/PS1 mice. Intriguingly, GP extract enhances autophagy in SH-SYS5Y-APP695 cells, U87 cells, and the nematode Caenorhabditis elegans, suggesting a conserved molecular mechanism by which GP extract might regulate autophagy. In agreement with its role as an autophagy activator, GP extract markedly diminishes mobility decline in polyglutamine Q35 mutants and aged wild-type N2 animals in C. elegans. Furthermore, GP extract significantly extends lifespan in C. elegans.
Subject(s)
Aging/drug effects , Crassulaceae/chemistry , Plant Extracts/pharmacology , AMP-Activated Protein Kinases/drug effects , Amyloid beta-Peptides/drug effects , Animals , Autophagy/drug effects , Caenorhabditis elegans/drug effects , Cell Culture Techniques , Disease Models, Animal , Humans , Longevity/drug effects , Mice , Mice, TransgenicABSTRACT
Alzheimer's disease (AD) is the most common type of dementia and also one of the leading causes of death worldwide. However, the underlying mechanisms remain unclear, and currently there is no drug treatment that can prevent or cure AD. Here, we have applied the advantages of using induced pluripotent stem cell (iPSC)-derived neurons (iNs) from AD patients, which are able to offer human-specific drug responsiveness, in order to evaluate therapeutic candidates for AD. Using approach involving an inducible neurogenin-2 transgene, we have established a robust and reproducible protocol for differentiating human iPSCs into glutamatergic neurons. The AD-iN cultures that result have mature phenotypic and physiological properties, together with AD-like biochemical features that include extracellular ß-amyloid (Aß) accumulation and Tau protein phosphorylation. By screening using a gene set enrichment analysis (GSEA) approach, Graptopetalum paraguayense (GP) has been identified as a potential therapeutic agent for AD from among a range of Chinese herbal medicines. We found that administration of a GP extract caused a significantly reduction in the AD-associated phenotypes of the iNs, including decreased levels of extracellular Aß40 and Aß42, as well as reduced Tau protein phosphorylation at positions Ser214 and Ser396. Additionally, the effect of GP was more prominent in AD-iNs compared to non-diseased controls. These findings provide valuable information that suggests moving extracts of GP toward drug development, either for treating AD or as a health supplement to prevent AD. Furthermore, our human iN-based platform promises to be a useful strategy when it is used for AD drug discovery.
Subject(s)
Alzheimer Disease/drug therapy , Amyloid beta-Peptides/genetics , Crassulaceae/chemistry , Peptide Fragments/genetics , tau Proteins/genetics , Alzheimer Disease/genetics , Alzheimer Disease/pathology , Basic Helix-Loop-Helix Transcription Factors/genetics , Cell Differentiation/drug effects , Drug Discovery , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/pharmacology , Gene Expression Regulation/drug effects , Humans , Induced Pluripotent Stem Cells/drug effects , Induced Pluripotent Stem Cells/metabolism , Nerve Tissue Proteins/genetics , Neurons/drug effects , Neurons/pathologyABSTRACT
Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson's disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson's disease by the Unified Parkinson's Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.