Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Abstract

Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

NETISCE: a network-based tool for cell fate reprogramming

“…All the computed attractors are then clustered via k -means clustering. The elbow and silhouette metrics are calculated to determine an optimal k 51 , 52 . The clusters are also evaluated using the internal-marker node values.…”

“…They can be helpful to evaluate the effect of node perturbations in static networks 47 , 48 and have been applied in biochemical 49 and disease networks 50 . The signal flow analysis (SFA) method is especially suited to estimate system dynamics for non-linear complex systems 47 , and its application to biology has been recently explored 47 , 51 . SFA estimates a steady-state value for each network node based on a signal propagation equation that considers the activity of its regulators, the type of regulatory relationship (activation or inhibition), and the initial state of the node (see the “Methods” section).…”

“…SFA has been used to successfully reproduce the steady-states of signaling networks derived from ODE models and the changes in expression for network elements under different perturbations from perturbation biology experiments with up to 80% accuracy 47 . Latterly, SFA was applied to an aging-related gene regulatory network (GRN) to identify potential aging reversion targets 51 . Although Lee and colleagues did not take a control theory-based approach, aging reversion targets were predicted by evaluating SFA-simulated single node perturbations that decreased the estimated expression values of aging-related biomarker nodes compared to an unperturbed simulation.…”

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

“…They can be helpful to evaluate the effect of node perturbations in static networks 47,48 . The Signal Flow Analysis (SFA) method is especially suited to estimate system dynamics for non-linear complex systems 47 , and its application to biology has been recently explored 47,49 . SFA estimates a steady-state value for each network node based on a signal propagation equation that considers the activity of its regulators, the type of regulatory relationship (activation or inhibition), and the initial state of the node (see Methods).…”

“…SFA has been used to successfully reproduce the steady-states of signaling networks derived from ODE models and the changes in expression for network elements under different perturbations from perturbation biology experiments with up to 80% accuracy 47 . Latterly, SFA was applied to an aging-related gene regulatory network (GRN) to identify potential aging reversion targets 49 . Although Lee and colleagues did not take a control theory-based approach, aging reversion targets were predicted by evaluating SFA-simulated single node perturbations that decreased the estimated steady-state expression values of aging-related biomarker nodes compared to an unperturbed simulation.…”