the science behind chronomics digital€¦ · introduction there is a revolution in health and...

The Science Behind Chronomics

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IntroductionThere is a revolution in health and wellbeing that is being driven by technological advancements in biology over the last 20 years. Research and medicine has moved from a concrete biological entity (e.g. a gene, a protein) to entire systems (i.e. all genes - genomics, all proteins - proteomics) [1]. We live in the era of the omics. Today, this technology is moving out of the lab and into the hands of individuals.

Despite the massive number of studies in the genetics fi eld, the epigenetics research community has only recently been able to access omics technologies and generate enough big data associated with their type of measurements. Recent epigenome-wide association studies (or EWAS) have identifi ed epigenetic marks with many different conditions [2]. Most studies focus on unravelling new biological insights into disease, but do not leverage that information to build accurate biomarkers. For this reason, the concept of direct-to-consumer epigenetic testing has not existed until now.

Origins of ChronomicsThe Chronomics team met while completing PhDs at the University of Cambridge, combining expertise in epigenetics, computational biology and ageing research. Our founders were pioneers in using machine learning to build epigenetic predictors of biological age. They saw the potential of this methodology to predict many more complex phenotypes, to effectively provide updated risks for developing many diseases and conditions [3, 4].

Why epigenetics?Epigenetics can be defi ned as the study of mitotically and/or meiotically stable heritable changes in gene function that cannot be explained by changes in DNA sequence [5]. Epigenetic mechanisms include DNA methylation, histone modifi cations and noncoding RNAs. Together they regulate gene expression and allow the creation of different cell types and functions starting from the same set of genes, which is needed to build complex multi-cellular organisms from a single blueprint. Unlike your genome (your DNA), which is fi xed from birth, epigenetic marks change during your lifetime upon exposure to different cues, including internal signals (e.g. hormones responses triggered by a certain mental state), external environmental factors (e.g. exposure to air pollution or chemical compounds) or life choices (e.g. type of diet, smoking, . . . ) [6]. Therefore, epigenetic information constitutes a biological layer that holistically captures the complex state of a biological system, combining multiple effects, including genetics, genetic-environmental interactions or the cellular composition of the tissue.

The Science Behind Chronomics The Science Behind Chronomics

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Fig. 1: The diagram shows how DNA (displayed in grey) is wrapped and condensed into chromosomes with the help of many epigenetic marks, including DNA methylation (-Me groups), histone modifi cations (in the histone tails of nucleosomes, displayed as purple beads) or non-coding RNAs. These marks also help to regulate gene expression and confi gure cellular identity (e.g. Becoming a muscle cell or a skin cell) [7].

Technologies to measure DNA methylationToday we can measure DNA methylation at single base pair resolution with very highthroughput using next generation sequencing technology [13–16]. This rich, but largely unexplored, data source has enormous potential in research and in healthcare [17, 18]. The most widely used single base resolution methods currently rely on a chemical reaction step called bisulphite conversion. Treatment of DNA with sodium bisulphite enables cytosines to be differentiated based upon whether they are methylated or unmethylated. Methylated cytosines remain unchanged, whereas unmethylated cytosines are converted into the base uracil (U) and then, after PCR amplifi cation (with A,C,G,T deoxynucleotides), into thymine (T) [19]. This amplifi ed library (or DNA) pool can then be sequenced and the data processed to map methylation patterns.

Choosing the right tissueDifferent cell types have different epigenomes. This allows them, starting with the same genetic code, to express different subsets of genes and specialise in their function, such as transmitting nerve impulses in the case of neurons or secreting antibodies in the case of B cells. Therefore, since different tissues are composed of different cell types, they show different profi les when assessed for their DNA methylation patterns [20].

At Chronomics we use saliva as our tissue of choice. Saliva represents a very attractive source for the next generation of biomarker studies [21] and it contains a mix of buccal epithelial cells and leukocytes, from which DNA can be successfully extracted [22]. Using saliva as our main DNA source has many associated benefi ts, including:

• Ease-of-use and non-invasive sampling process.

• DNA methylation profi ling in saliva samples is becoming increasingly common and has proven useful in studies looking at many different diseases and conditions, such as Parkinson’s [23], respiratory allergy [24], attention-defi cit/hyperactivity disorder [25], head and neck cancer [26] and obesity [27, 28]. This allows for the direct translation of many discoveries from the literature into our platform.

• DNA methylation profi les of saliva are correlated with those in blood (R2 = 0.97) and brain (R2 = 0.86) [29], which makes it a fantastic surrogate tissue to capture effects associated with many complex traits [30–32].

• Buccal cells seem to show more stable DNA methylation profi les in longitudinal studies when compared with other tissues [33], which will be a core component of the Chronomics’ repeatable DNA test.

Building predictors from DNA methylationIn 2013 Professor Steve Horvath of UCLA showed that biological age can be accurately predicted across different tissues using DNA methylation data [46]. In addition, he and others have shown that this model is affected by disease states associated with environmental and lifestyle factors [34–56].

Aside from age, there are many more successful examples of phenotypes or biological conditions for which predictors have been built using DNA methylation. These include many types of cancer [5], coronary heart disease [57], type 2 diabetes [58], BMI [59] smoking exposure [60], alcohol consumption [61], postpartum depression [62] or gestational age [63, 64]. The potential in the medical fi eld is enormous, however the focus of Chronomics is on prevention and wellbeing.

The power of these predictors is that they behave like updated risk scores. For example, the incidence of obesity-associated cancers could be predicted using an obesityassociated DNA methylation signature that measures the adiposity history of the individual, irrespective of the actual BMI of the individual at the time of assessment [5]. Furthermore, lung cancer risk prediction can be improved when including some DNA methylation markers associated with smoking exposure [65], even after adjusting for current smoking status and duration [66].

DNA methylation has all the ideal characteristics to build robust predictors from machine learning:

It is a biologically stable and technically reproducible marker that can integrate genetic and non-genetic effects. Currently, most risk models only include epidemiological factors and they normally do not enable differentiation of individuals with good and poor prognosis. DNA methylation effectively captures an individual interaction at the molecular level with environmental factors such as stress, nutrition, smoking and/or absence of physical exercise, which are key components of multivariable risk algorithms.

Chronomics’ actionable epigenetic indicatorsThe focus of Chronomics is on health, wellbeing, and prevention of age and lifestyle related diseases by monitoring and reducing risk factors. We are non-diagnostic, but we show our users how to avoid illness with actionable indicators for health and environmental impact that can be improved by positive life changes.

Certain lifestyle interventions, such as exercise [67, 68], quitting smoking [69, 70] or drinking [71, yoga classes [72], dietary changes [73, 74] or weight loss [75], can reverse specifi c sets of epigenetic markers associated with ill health.

We are pioneering a new era of DNA testing powered by epigenetic predictors, becoming the fi rst company in the world to translate the predictive power of DNA methylation into benefi ts for the health and wellbeing of our society.

What is DNA methylation? DNA methylation is the addition of a methyl (CH3) group to DNA by DNA methyltransferase (DNMT) enzymes. In humans, this mostly occurs on cytosine bases where the cytosine is followed by a guanine base (commonly referred to as a CpG site), of which there are more than 28 million in the human genome [7]. In a given DNA strand of a given cell, the methylation readout of each one of these CpG sites is a binary variable: the site is methylated or not. However, when a sample is taken from a tissue many cells are analysed at the same time. DNA methylation at a CpG site is therefore normally expressed as a percentage, which approximately refl ects how many cells in that tissue are methylated at that genomic location [8, 9]. DNA methylation has been shown to be infl uenced by both genetics, the environment, and health [10, 12].


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of Sciences, 111(43):15538–15543, 2014.




34. Steve Horvath, Wiebke Erhart, Mario Brosch, Ole Ammerpohl, Witigo von Schönfels, Markus Ahrens, Nils Heits, Jordana T Bell, 34. Steve Horvath, Wiebke Erhart, Mario Brosch, Ole Ammerpohl, Witigo von Schönfels, Markus Ahrens, Nils Heits, Jordana T Bell,





34. Steve Horvath, Wiebke Erhart, Mario Brosch, Ole Ammerpohl, Witigo von Schönfels, Markus Ahrens, Nils Heits, Jordana T Bell, 34. Steve Horvath, Wiebke Erhart, Mario Brosch, Ole Ammerpohl, Witigo von Schönfels, Markus Ahrens, Nils Heits, Jordana T Bell,


63. Jerry Guintivano, Michal Arad, Todd D Gould, Jennifer L Payne, and ZA Kaminsky. Antenatal prediction of postpartum depression with 63. Jerry Guintivano, Michal Arad, Todd D Gould, Jennifer L Payne, and ZA Kaminsky. Antenatal prediction of postpartum depression with




61. Yan Zhang, Ben Schöttker, Ines Florath, Christian Stock, Katja Butterbach, Bernd Holleczek, Ute Mons, and Hermann Brenner.

Smoking-associated DNA methylation biomarkers and their predictive value for all-cause and cardiovascular mortality.

Environmental health perspectives, 124(1):67, 2016.

62. C Liu, RE Marioni, Å K Hedman, L Pfeiffer, PC Tsai, LM Reynolds, AC Just, Q Duan, CG Boer, T Tanaka, et al. A DNA methylation







Epigenetic aging and immune senescence in women with insomnia symptoms: fi ndings from the women’s health initiative study. 63. Jerry Guintivano, Michal Arad, Todd D Gould, Jennifer L Payne, and ZA Kaminsky. Antenatal prediction of postpartum depression with


















48. Judith E Carroll, Michael R Irwin, Morgan Levine, Teresa E Seeman, Devin Absher, Themistocles Assimes, and Steve Horvath.

The Science Behind Chronomics

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