Advances in pharmacological interventions of aging in mice

Haematococcus Pluvialis Extends Yeast Lifespan and Improves Slc25a46 Gene Knockout-Associated Mice Phenotypic Defects

SCOPE

Aging has become one of major concern worldwide. It is therefore of great significance in finding food resources as therapeutic candidates for aging-related functional decline improvement and prevention. This study aimed to define the potency of Haematococcus pluvialis (H. pluvialis) as an anti-aging food resource.

METHODS AND RESULTS

Yeast is used to explore the anti-aging effects of H. pluvialis. The result showed that H. pluvialis extract could effectively extend yeast chronological lifespan (CLS) by reducing intracellular reactive oxygen species (ROS) levels, promoting mitochondrial membrane potential (MMP) levels and accumulating storage carbohydrate (glycogen). Subsequently, Slc25a46 knockout (Slc25a46-/- ) mice with mitochondrial dysfunction are fed with 100 mg kg-1 H. pluvialis extracts for 10 days. The in vivo data demonstrated that H. pluvialis extract could effectively improve the phenotypic deficits, including underweight, muscle weakness, redox imbalance, and mitochondrial respiratory chain dysfunction, etc., in Slc25a46-/- mice.

CONCLUSIONS

This work highlights that the mitochondria may be a potential therapeutic target for combating aging, and demonstrated that H. pluvialis, as a dietary supplement, may potentially be an effective preventive substance that may contribute to the promotion of healthy aging.

Random forest classification for predicting lifespan-extending chemical compounds

Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

Obesity and aging: Molecular mechanisms and therapeutic approaches

The epidemic of obesity is a major challenge for health policymakers due to its far-reaching effects on population health and potentially overwhelming financial burden on healthcare systems. Obesity is associated with an increased risk of developing acute and chronic diseases, including hypertension, stroke, myocardial infarction, cardiovascular disease, diabetes, and cancer. Interestingly, the metabolic dysregulation associated with obesity is similar to that observed in normal aging, and substantial evidence suggests the potential of obesity to accelerate aging. Therefore, understanding the mechanism of fat tissue dysfunction in obesity could provide insights into the processes that contribute to the metabolic dysfunction associated with the aging process. Here, we review the molecular and cellular mechanisms underlying both obesity and aging, and how obesity and aging can predispose individuals to chronic health complications. The potential of lifestyle and pharmacological interventions to counter obesity and obesity-related pathologies, as well as aging, is also addressed.