Scientific ReportsSystems BiologyPeer-reviewed journal article
Universal transcriptomic signature of age reveals temporal scaling of Caenorhabditis elegans aging trajectories
Andrei E. TarkhoviD, Ramani Alla, Srinivas AyyadevaraiD, Mikhail A. PyatnitskiyiD, L. I. Men’shikov, Robert J. Shmookler ReisiD, Peter O. FedicheviD
Abstract
We collected 60 age-dependent transcriptomes for C. elegans strains including four exceptionally long-lived mutants (mean adult lifespan extended up to 9.4-fold) and three examples of RNAi treatments that increased lifespan by 19 - 35%. Principal Component Analysis (PCA) reveals aging as a transcriptomic drift along a single direction, consistent across the vastly diverse biological conditions and coinciding with the first principal component, a hallmark of the criticality of the underlying gene regulatory network. We, therefore, expected that the organisms aging state could be characterized by a single number closely related to vitality deficit or biological age. The \"aging trajectory\", i.e. the dependence of the biological age on chronological age, is then a universal stochastic function modulated by the network stiffness; a macroscopic parameter reflecting the network topology and associated with the rate of aging. To corroborate this view, we used publicly available datasets to define a transcriptomic biomarker of age and observed that the rescaling of age by lifespan simultaneously brings together aging trajectories of transcription and survival curves. In accordance with the theoretical prediction, the limiting mortality value at the plateau agrees closely with the mortality rate doubling exponent estimated at the cross-over age near the average lifespan. Finally, we used the transcriptomic signature of age to identify possible life-extending drug compounds and successfully tested a handful of the top ranking molecules in C. elegans survival assays and achieved up to a +30% extension of mean and median lifespan.
- organism: C. elegans
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 66
- FWCI
- 2.82
- Field percentile
- top 9%
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