Identifying molecular targets for reverse-aging using Boolean network modeling and analysis of single-cell transcriptomic data of human dermal fibroblast cells

Abstract

Aging, previously known as an irreversible biological process, is a major risk factor for diseasesincluding cancer, diabetes, and cardiovascular disorders. However, even in the case of senomorphics, inwhich cellular senescence-related phenotypes are removed without killing the cell, such fail to cureaging as they are unable to reverse cellular function. To address this problem, a Boolean networkmodel to explain the healthy aging process was constructed using single-cell RNA sequencing data. TheQuine-McCluskey algorithm was used to infer the logic. Upon model simulations, a target to reverseaging, upregulation of JUN and HES1 in combination, was discovered to reverse the aged transcriptometo a young transcriptome. Literature search on JUN and HES1 was performed and showed that whilstJUN and HES1 individually reverse senescent phenotypes, such failed in restoring cellular function. Suchresults offer the possibility of upregulation of JUN and HES1 in combination as a novel target toreverse cellular function and senescent phenotypes. This study provides insight for a new therapeuticstrategy to reverse aging.