Potassium aspartate inhibits SH-SY5Y cell damage and apoptosis induced by ouabain and H2O2

Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase's Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term "Glutathione metabolic process", which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.

The neuroprotective effects of the combined extract of mulberry fruit and mulberry leaf against hydrogen peroxide-induced cytotoxicity in SH-SY5Y Cells

The prevalence of dementia is increasing, and most of the causes are related to neuronal cell death. Unfortunately, no effective strategy is available for protecting against this condition. Based on the use of the synergistic concept together with the positive modulation effect of both mulberry fruit and mulberry leaf on dementia, we hypothesized that the combined extract of mulberry fruit and mulberry leaf (MFML) should mitigate neuronal cell death. Neuronal cell damage was induced in SH-SY5Y cells by exposure to hydrogen peroxide at a dose of 200 μM. SH-SY5Y cells were given MFML at doses of 62.5 and 125 μg/mL before induced cytotoxicity. Then, the cell viability was determined via MTT assay, and the possible underlying mechanisms were investigated via the alterations of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), malondialdehyde (MDA), nuclear factor-κB (NF-κB), and tumor necrosis factor-alpha (TNF-α), together with apoptotic factors including (B-cell lymphoma 2) BCL2, Casapase-3 and Caspase-9. The results showed that MFML significantly enhanced cell viability. It also significantly decreased MDA level, NF-κB, TNF-α, Casapase-3, Caspase-9, but increased SOD, GSH-Px and BCL2. These data demonstrated the neuroprotective effect of MFML. The possible underlying mechanisms might occur partly via the improvement of the inappropriate apoptotic mechanisms via BCL2, Casapase-3 and Caspase-9 together with the decrease in neurodegeneration induced by the reduction of inflammation and oxidative stress. In conclusion, MFML is a potential neuroprotectant candidate against neuronal cell injury. However, toxicity, animal studies, and clinical trials are essential to confirm these benefits.