Model-based identification of drug targets that revert disrupted metabolism and its application to ageing

Area of Science:

• Metabolic engineering
• Computational biology
• Systems biology

Background:

• Genome-scale metabolic models (GSMMs) and 'omics' data are increasingly available for drug target identification.
• Current GSMM-based methods primarily focus on identifying targets for cell death (e.g., antibiotics, cancer).
• Therapeutic strategies for metabolic diseases require restoring healthy metabolic states, differing from cytotoxic approaches.

Purpose of the Study:

• To develop a novel algorithm, the metabolic transformation algorithm (MTA), for identifying drug targets aimed at restoring normal metabolism.
• To validate the predictive accuracy and experimental utility of MTA in identifying therapeutic targets.

Main Methods:

• Development of a generic metabolic transformation algorithm (MTA).
• Validation of MTA's prediction accuracy using known perturbation datasets.
• Experimental validation of predicted yeast lifespan-extending genes (GRE3, ADH2) and their hormetic effects.
• Analysis of MTA's predictions for human aging targets against known lifespan-extending genes and caloric restriction data.

Main Results:

• MTA's prediction accuracy was comprehensively validated against known perturbation data.
• Two predicted yeast genes, GRE3 and ADH2, were experimentally validated as lifespan-extending targets, exhibiting hormesis.
• MTA identified potential drug targets for human aging, enriched with known lifespan-extending genes and genes affected by caloric restriction mimetics.

Conclusions:

• MTA provides a novel computational approach for identifying drug targets in metabolic disorders.
• The algorithm successfully predicts targets for restoring metabolic health and extending lifespan.
• MTA represents a promising tool for discovering therapeutic interventions for aging and other metabolic diseases.