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The first strong evidence that DNA methylation levels could generate accurate predictions of aging was published by a UCLA team in 2011 (Bocklandt & Horvath). Soon after, the Ideker and Zhang labs at UCSD published the Hannum epigenetic clock (Hannum 2013) which consisted of 71 CpG markers that can accurately estimate the age of a subject based on blood methylation levels. That same year, Horvath developed the first multi-tissue epigenetic clock, based on a larger set of 353 CpG markers.  Because it works across different tissues with different patterns of epigenetic expression, it is more likely to represent fundamental aging processes than a tissue-specific clock. This also gives it wider applicability, not requiring adjustments or offsets when used in measuring different cell types.

Since 2013 several different epigenetic aging clocks have been developed, targeted at different requirements and applications:

DNAm Stubbs & Reik mouse clock (2017)
– A multi-tissue DNA methylation age predictor in mice.

DNAm PhenoAge clock (Levine & Horvath 2018)
– An epigenetic biomarker of aging for lifespan and healthspan.

DNAm Skin & blood clock (Horvath & Raj 2018)
– A highly accurate clock that tracks the dynamic aging of human cells cultured in vitro.

Ribosomal DNA multi-species clock (Wang & Lemos 2019)
– rDNA is the most evolutionarily conserved segment of the genome and gives origin to the nucleolus. This clock accurately estimates individual age within species and operates across species as distant as humans, mice and dogs.

Epigenetic clocks are sometimes described as “degenerate”, meaning that there are many hundreds or even thousands of different clocks that can be devised by reesearchers which are capable of yielding age predications of similar accuracy.

The challenge for the future is to develop new clocks which are optimized to meet specific requirements. For drug discovery it would be useful to develop cell lines that report their age, enabling high throughput drug screens for aging. The same technology would allow researchers to perform single or combinatorial genetic screens (e.g. CRISPR) to identify genes or gene combinations that affect aging, with the potential to dramatically increase our knowledge of aging biology.


The most prominent diseases after the age of 65 years are linked to organs and tissues with the highest energy demand on their mitochondria – the brain and the heart.These diseased organs also age prematurely according to the epigenetic aging clock.

Most cells generate most of their energy using mitochondria. Mitochondria are organelles that specialize in producing ATP by oxidative phosphorylation (OXPHOS). Mitochondria retain their own genome encoding proteins critical for energy production. Unlike the nuclear genome, which contains two copies of each gene (with certain exceptions), the mitochondria contain hundreds to thousands of gene copies. With age, the mitochondrial genome number decreases and the surviving mtDNA genomes suffer an increasing level of damage.

It has been known for many years that mitochondria generate signals that regulate gene expression via retrograde signalling. In 2014 Picard & Wallace demonstrated that single mtDNA mutations can cause a set of transcriptional responses within cells. This qualitative regulation of nuclear gene expression by quantitative changes in mtDNA levels challenges the single mutation-single disease model which underpins so much of today’s drug discovery programs.

In 2019 Kopinski & Wallace followed up this finding by demonstrating that changes in mtDNA heteroplasmy cause changes in mitochondrial intermediates and redox state, which result in distinctive histone modification changes. Thus, changes in the mitochondrial genotype change mitochondrial metabolism, which change the epigenome and transcriptome, which induces distinct clinical phenotypes. This manifests as diabetes, neurodegenerative disease or as MELAS (a rare mitochondrial disease) depending on the percentage of mutant m.3243 A > G mitochondrial genomes.


It has been known for > 25 years that under certain conditions, the cell can reduce the proportion of damaged mitochondrial genomes (Tonsgard & Getz 1990, Dunbar & Holt 1995, Manfredi & Schon 1999). The mechanism was never uncovered and these experimental observations faded into obscurity.

During his PhD project at the MRC Laboratory of Molecular Biology at the University of Cambridge, Dr Daniel Ives harnessed bioinformatic databases and analytic tools to identify small molecule compounds that trigger this effect in cells from individuals with the orphan disease MELAS. He demonstrated that these could increase oxygen consumption in MELAS cells from ~5% to 100% the level of age matched healthy individuals.

Our subsequent research into the underlying mechanism of this effect suggests that these drugs act by increasing competition between mitochondria for scarce resources. Mitochondria import the majority of their proteins. When this protein supply is constrained, undamaged mitochondria are able to out-compete the damaged mitochondria and this gives them a replicative advantage.

SB002 is a molecule discovered by Shift Bioscience to be particularly effective in promoting this greater level of mitochondrial competition inside the cell. We have demonstrated that SB002 can be used to reduce the proportion of damaged mitochondria sourced from an individual with Parkinson’s disease, resulting in an increase in oxygen consumption.

Mice engineered to elevate the rate of damage accumulation in their mitochondrial genomes (POLG mice) exhibit accelerated aging. Recently we have demonstrated that SB002 can slow the progression of visible signs of aging in POLG mice. During treatment with the drug, internal aging markers (heart hypertrophy, elevated glucose) are reduced.

We will minimize costs and timescales for clinical development of this family of drug molecules by first targeting the orphan disease MELAS, which is caused by inherited mitochondrial dysfunction. A clinical trial for MELAS requires fewer participants due to the rarity of the disease and the larger degree of unmet clinical need. Drug efficacy is easier to demonstrate due to clearer biological/clinical endpoints.

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