Compass in the data ocean: Toward chronotherapy
Rikuhiro G. Yamadaa and Hiroki R. Uedaa,b,1

In the globalized modern society, the world is continuously moving 24 h a day 7 d a week. Prominent cities
on the earth can be seen brightly lit at night from outerspace. People on earth have daily (circadian) rhythms
based more on their social circumstances than on
natural cycles of day and night. This discrepancy ofrhythms poses new challenges to our bodies that havenever been met in evolutionary history. The circadianclock system regulates various aspects of physiology.
Even a healthy person may suffer once internal
rhythms break from environmental ones. The misalignment happens, for example, when a person
travels on an airplane across many time zones (jetlag), when a person oversleeps on the weekend andwakes up early to go to work on Monday (social jetlag), or when a person works during the night or earlymorning with an intermittent or rotating schedule(shiftwork). Several reports have shown that internal
body time varies by 5–6 h in healthy humans (1) and by
as much as 10–12 h in shift workers (2). Accumulating
evidence suggests that those misalignments may be alink to health risks, including obesity (3) and psychiatricdisorders (4). Also, the severity of some diseases may
be affected by the time of day, and some drugs are
known to have different potency or toxicity depending on their administration time. The advancement
achieved in the past two decades in the field of circadian biology has revealed that the core of the mammalian circadian clock is composed of about 20 transcription
factors (5). Recently, a research group reported that amajority of mammalian genes are under the clock regulation, and that markedly different genes show circadianoscillation in each tissue (6). Importantly, they reported
that a substantial number of top-selling drugs in theUnited States have circadian targets (6). Based on thosefindings, a convenient and precise molecular measurement of tissue molecular time is needed. The reportpublished in PNAS by Anafi et al. (7) from the same research group strives to achieve this precise molecularmeasurement of tissue molecular time.
The conventional method to monitor body time is totake blood samples periodically over 24 h, measuring
the level of hormones such as melatonin or cortisol,( , )(0 , 1)0( , )(-1, 0)18( , )( , )6( , )(1 , 0)12( , )
(0 ,-1)
Fig. 1. Simplified numerical representation of Linné’s
flower clock. A hypothetical flower A is represented withred petals, and flower B is represented with blue petals
around a 24-h clock. The openness of flowers is indicatedas −1 (closed), 0 (half-open), or 1 (open). The 2D vectorsthat contain elements of flower openness are displayed
below the corresponding flowers. The two thick blackarrows form a basis of the plane, and any arbitrary vectorof flower openness locates on the unit circle on theplane. A thin black vector is depicted as an example, andits phase (the time of day) is recapitulated as the angle
between the axis and the vector (double arc). The yellow
and black bands around the clock represent the day andnight times of a day. In an actual situation, the dimensionof the vector will be the number of biological molecules
used in the analysis, or the number of nodes in theprevious layer of the neural network.

which have robust circadian oscillation in the blood(1, 8). However, this method ignores the underlying
molecular machinery and differences between organs.
A straightforward approach to address this problem isto perform time series sampling from each organ. However, subjecting people to tissue biopsies with a constant time interval for a couple of days is a huge burden
on the people and is impractical. Choosing an easily
accessible tissue such as hair follicles can reduce theburden, but this approach precludes investigation ofinternal organs. Another way is to develop a molecular
timetable method that can estimate internal body timewith just one or a few samples (9).

aLaboratory for Synthetic Biology, RIKEN Quantitative Biology Center, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan; and bDepartment of Systems
Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Author contributions: R.G.Y. and H.R.U. wrote the paper.
The authors declare no conflict of interest.
See companion article on page 5312.
1To whom correspondence should be addressed. Email: uedah-tky@umin.ac.jp.

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COMMENTARYCOMMENTARYThe molecular timetable method was inspired by the Linné’s
flower clock, which is a conceptual clock named after Carl vonLinné, the father of modern taxonomy. In the 18th century, Linné
realized that several flowers open or close at a particular time ofday, and proposed a list of flowers so that the combination of theflowers indicates the time of day. By substituting the flowers withgood time-indicating molecules, we can construct a molecular
timetable with which to infer internal body time. Molecules canbe, but are not limited to, mRNA (9–12), metabolites (13–15), andprotein (16). The fundamental step of the method is to find goodtime-indicating molecules. Because good time-indicating molecules
are oscillatory with the circadian period, analytical techniques developed for searching for oscillatory molecules can be used, including the Pearson correlation between variation of molecular levels
and the sinusoidal curve (9, 13, 14, 16), a nonparametric correlation
test such as JTK_CYCLE (17), and singular value decomposition or
its relatives (18, 19). However, the notable caveat of those analysis
techniques is that the experiment should be well designed andcontrolled with the intention of detecting oscillatory molecules.
Therefore, those analysis tools usually require a set of new experiments when the subject changes. The recently emerging machinelearning approaches may mitigate this problem by training a modelthat can be applied to datasets taken from different organs (20).
However, samples with known circadian time are required for thetraining. As Anafi et al. (7) point out, the Gene Expression Omnibus,
a public gene expression database maintained by the National
Center for Biotechnology Information, has a collection of more than
1 million human gene expression data, but most data lack thesampling time information. Hence, they embarked on a challenge
to develop a method that could make use of the large-scale data by
developing an unsupervised learning method, CYCLOPS (CYCLic
Ordering by Periodic Structure).
First, they focused on genes that were detected as oscillatory
in the mouse in the previous study (6). Then, they relied on amathematical concept of oscillation. The basic concept they usedin the work can be envisaged with the metaphor of Linné’s flowerclock. For simplicity, let us assume we have two imaginary flowers
A and B, and that (i) “flower A” opens at 0600 hours and closes at
1800 hours, (ii) “flower B” opens at 0000 hours (midnight) andcloses at 1200 hours (noon), and (iii) flowers A and B open or close
gradually; that is, flower A is half-open at midnight and noon, andflower B is half-open at 0600 and 1800 hours (Fig. 1). Notice that it
would be difficult to know the time of day by looking at just flower
A or just flower B under this assumption. For example, when you
see that flower A is half-open, you may need to sit for hours to see
if it is opening (midnight) or closing (noon); that is, you need timeseries observation. Instead, by looking at both flowers, you would
be able to know immediately that it is noon if flower B is also open.
If you have more flowers with different opening or closing times, theclock becomes more precise. This simple setting can show anotherinteresting property of oscillation. When the degree of openness of
a flower is numerically represented (e.g., 1 for open, −1 for closed,
0 for half-open), we can form two vectors for time-indicating flowers. Each vector contains elements representing the openness at adifferent time of day (Fig. 1). It should be noted that two orthogonal
vectors form a plane on which vectors of arbitrary time of day distribute on the unit circle. The angle between the vector and an axis

The results of Anafi et al. may spark the dawnof the “Age of Discovery” in the data ocean of
public databases, manifesting CYCLOPS as a
useful compass to enhance the translation ofknowledge in circadian biology to medicine.
indicates the time of day of the vector. This observation also holds
when the vector is n-dimensional; that is, there are n > 2 flowers ina vector. This property of oscillation means that the openness of ntime-indicating flowers at an arbitrary time of day can be encoded
in a circle on 2D space. So, the problem of finding time-indicating
molecules (flowers) can be rephrased as “What selection of molecules can make a plane on which projected vectors form a circle,
or more generally, an ellipse?” Anafi et al. (6) addressed thisproblem by using unsupervised machine learning, where a neuralnetwork model was optimized so that the gene expression profiles
were efficiently encoded on an ellipse on a plane. Because their
method is an unsupervised approach, it was expected to workwith datasets of unknown sampling time. Indeed, they successfully demonstrated that their method could detect aberrant circadian function in human hepatocellular carcinoma samples thatwere originally sampled without the intention of circadian analysis. Furthermore, they confirmed the circadian expression of a
glucose transporter gene in human liver samples. In addition,
based on this finding, they succeeded in providing a proof ofconcept, where the toxicity of a drug that interferes with thetransporter could be reduced by controlling administration timing. The results of Anafi et al. may spark the dawn of the “Age ofDiscovery” in the data ocean of public databases, manifestingCYCLOPS as a useful compass to enhance the translation ofknowledge in circadian biology to medicine.

Acknowledgments
We thank the laboratory members at RIKEN and Quantitative Biology Center,
especially A. Millius for his critical reading and editing of the manuscript. This
research was supported by grants from the strategic programs for R&D (President’s Discretionary Fund) of RIKEN (to H.R.U.), an intramural grant-in-aid fromthe RIKEN Quantitative Biology Center (to H.R.U.), and a grant-in-aid for Challenging Exploratory Research (JSPS KAKENHI 26640021, to R.G.Y.).

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