Measurement of internal body timeby blood metabolomics
Yoichi Minamia,1, Takeya Kasukawab,1, Yuji Kakazuc,1, Masayuki Iigod, Masahiro Sugimotoc,e, Satsuki Ikedac,
Akira Yasuif, Gijsbertus T. J. van der Horstg, Tomoyoshi Sogac,2, and Hiroki R. Uedaa,b,2
aLaboratory for Systems Biology, and bFunctional Genomics Unit, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan; cInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami Kakuganji, Tsuruoka-shi, Yamagata 997-0052, Japan;
dDepartment of Applied Biochemistry, Utsunomiya University, 350 Mine-machi, Utsunomiya, Tochigi 321-8505, Japan; eDepartment of Bioinformatics,
Mitsubishi Space Software Co., Ltd., 5-4-36, Tsukaguchi-Honmachi, Amagasaki, Hyogo 661-0001, Japan; fDepartment of Molecular Genetics, Institute ofDevelopment, Aging and Cancer, Tohoku University, Seiryo-machi 4-1, Aobaku, Sendai, Miyagi 980-8575, Japan; and gMGC, Department of Genetics,
Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands

Detection of internal body time (BT) via a few-time-point assay has
been a longstanding challenge in medicine, because BT information can be exploited to maximize potency and minimize toxicity
during drug administration and thus will enable highly optimized
medication. To address this challenge, we previously developed
the concept, ‘‘molecular-timetable method,’’ which was originallyinspired by Linné’s flower clock. In Linné’s flower clock, one canestimate the time of the day by watching the opening and closing
pattern of various flowers. Similarly, in the molecular-timetable
method, one can measure the BT of the day by profiling the up anddown patterns of substances in the molecular timetable. To makethis method clinically feasible, we now performed blood metabolome analysis and here report the successful quantification of
hundreds of clock-controlled metabolites in mouse plasma. Basedon circadian blood metabolomics, we can detect individual BT
under various conditions, demonstrating its robustness against
genetic background, sex, age, and feeding differences. The powerof this method is also demonstrated by the sensitive and accurate
detection of circadian rhythm disorder in jet-lagged mice. These
results suggest the potential for metabolomics-based detection of
BT (‘‘metabolite-timetable method’’), which will lead to the realization of chronotherapy and personalized medicine.
chronotherapy 兩 circadian 兩 metabolome 兩 jet-lag 兩 LC-MSI

n the 18th century, the Swedish botanist Karl von Linné designeda ‘‘flower clock’’ comprising a series of plant species arranged
according to the respective time of the day their flowers open or
close. Watching this flower clock, one can estimate the time of the
day by noting the pattern of flower opening and closing. Since
Linné’s early times, it has been a well known fact that plants havean internal clock and thereby can open or close their flowers at a
precise time of the day. Similarly, animals possess an internal
molecular mechanism, a ‘‘circadian clock,’’ which underlies endogenous, self-sustained oscillations with a period of ⬇24 h manifest indiverse physiological and metabolic processes (1). In mammals,
several clock genes, including Clock, Bmal1, Per1, Per2, Cry1, Cry2,
RevErbA, Rora, Csnk1e, Csnk1d, and Fbxl3, regulate, at least in part,gene expression in central and/or peripheral clock tissues (2–4).
Reflecting the temporal changes in gene expression in central andperipheral clock tissues (5–8), the potency and/or toxicity ofadministered drugs depend on the individual’s present body time(BT) (9–13). It has been suggested that administrating a drug at a
specific BT improves the outcome of pharmacotherapy by maximizing its potency and minimizing its toxicity (14). In contrast,
administrating a drug at an inappropriate BT can result in severeside effects (15). Despite the importance of such BT-dependent
therapy (also known as ‘‘chronotherapy’’) (9–13), its application toclinical practice has been obstructed by a lack of clinically feasible
methods for measuring BT.
To overcome this problem, we previously developed the conceptof a ‘‘molecular-timetable method (16),’’ which was originallywww.pnas.org兾cgi兾doi兾10.1073兾pnas.0900617106

inspired by Linné’s flower clock. In Linné’s flower clock, one canestimate the time of the day by watching the opening and closing
pattern of various flowers. Similarly, in molecular-timetable
method, one can measure the BT of the day by profiling the up anddown pattern of substances in the molecular timetable. This concept was proven using the expression profile of clock-controlledgenes in a target organ (16). However, estimates of BT from theexpression profile of oscillating substances within a target organ (inthis case, the liver) are hard to apply directly to clinical situations.
To make the molecular-timetable method more clinically relevant,
we decided to determine BT from blood samples, which are moreavailable in clinical practice.
In the blood of mammals, several small chemical substances suchas metabolites and hormones have been reported to exhibit circadian oscillations. For example, the concentration of the steroid
hormone, corticosterone, is rhythmically controlled by circadianclock with a peak in the evening (17), and an amine-derived
hormone, melatonin, show circadian rhythm with a peak in the earlymorning in mice (18). In humans, several peptide hormone levels
show daily variations; growth hormone increases during sleep (19),leptin increases during the evening (20), and prolactin increases atnight (21). Concentrations of amino acids, including tryptophan,tyrosine, phenylalanine (22), methionine (23), cysteine, glutathione(24), and homocysteine (25), also exhibit daily variations in humanblood plasma. Despite these findings, comprehensive profiling ofcircadian dynamics of chemical substances in mammalian blood has
not yet been reported, and until now a comprehensive molecular
timetable of such chemical substances has not been constructed.
Metabolomics technology aims to comprehensively identify
and/or quantify the dynamic chemical substances present in biological samples. It is gaining interest in the fields of drug discovery,
disease diagnostics, and treatment (26–28). The present metabolomics technology was developed rapidly by coupling advanced
separation technology with highly sensitive and selective mass
spectrometry–gas chromatography mass spectrometry (GC/MS)(29–31), liquid chromatography mass spectrometry (LC-MS) (32–34), and capillary electrophoresis mass spectrometry (CE-MS) (35,36). To construct the molecular timetable from clinically available
samples, we have performed blood metabolome analysis in thisstudy. Using the LC-MS technique, we quantified hundreds of
Author contributions: Y.M., T.K., T.S., and H.R.U. designed research; Y.M., T.K., Y.K., M.I.,
and S.I. performed research; M.S., A.Y., and G.T.J.v.d.H. contributed new reagents/analytic
tools; T.K. analyzed data; and Y.M., T.K., T.S., and H.R.U. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1Y.M., T.K., and Y.K. contributed equally to this work.
2To whom correspondence may be addressed. E-mail: soga@sfc.keio.ac.jp or uedah-tky@umin.ac.jp.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0900617106/DCSupplemental.

PNAS Early Edition 兩 1 of 6

PHYSIOLOGY

Edited by Joseph S. Takahashi, Northwestern University, Evanston, IL, and approved April 21, 2009 (received for review January 22, 2009)

A

C 1.8
Nomalized quantity

Corticosterone (ng/ml)

100
75
50
25
0
0 4 8 12 16 20 0 4 8 12 16 20

Zeitgeber time (h)Circadian time (h)0.9
PT 6.1

PT 7.0

LysoPC (20:3)

LysoPC (20:4)

0
1.8

PT 7.6

PT 8.9

LysoPC (22:4)

LysoPC (18:3)

0

D 1.8
0
4

0.9
PT 5.8PT 5.9

LysoPC (22:5)

LysoPC (20:2)

PT 6.0PT 6.6

LysoPC (18:1)

LysoPC (22:5)

0
1.8

+2.5
8

0.9

12
16
SD

20
-2.5

24
4

Nomalized quantity

Molecular peak time (h)DD
LysoPC (20:2)

0.9
0 4 8 12 16 20 0 4 8 12 16 20

B

Molecular peak time (h)LD
LysoPC (18:1)

0
1.8

RT 18.30

0.9
PT 6.7

0

PT 6.8

RT 17.77

LysoPC (22:6)

1.8

LysoPC (20:3)

+2.5
0.9
8
SD

12
16

PT 7.30
1.8

PT 7.3

RT 18.17

LysoPC (20:3)

LysoPC (20:4)

0.9

20
-2.5

24
0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20
Zeitgeber time (h)Circadian time (h)0PT 7.4

RT 19.82
012

0

PT 9.10

120

Zeitgeber / Circadian Time (hr)clock-controlled metabolites in mouse plasma and successfully
constructed a molecular timetable of blood metabolites. This
metabolite timetable allowed us to measure individual BT undervarious conditions and was robust enough to be used in mice withdifferent genetic backgrounds, sex, age, and feeding conditions. It
was also sensitive and accurate enough to detect circadian rhythm
disorders in jet-lagged mice. Our preliminary results suggest that
other metabolomics techniques such as CE-MS can also be applied
to the molecular-timetable method, demonstrated by the quantification of hundreds of clock-controlled metabolites, the identification of substantial portion of these metabolites, and successful
measurement of BT from independent blood samples. Thus,
metabolomics-based measurement of BT will contribute to thepotential areas of chronotherapy and personalized medication
regimens.
Results
Construction of the Metabolite Timetable from Blood Plasma by
LC-MS. Samples of blood plasma were taken from young male

CBA/N mice every 4 hours over 2 days during light–dark (LD) or
constant darkness (DD) conditions. Plasma corticosterone wasused as a quality control because it exhibits a clear circadianoscillation when quantified by radio immunoassay (Fig. 1A). Smallchemical substances in the plasma were quantified by LC-MS
analysis to construct the metabolite timetable. LC-MS analysis
detected 695 negative ion and 938 positive ion peaks in the plasma.
Of these, 176 negative and 142 positive ion peaks exhibited significant circadian oscillations in LD and DD conditions [Fig. 1B; false
discovery rate (FDR) ⬍0.01; see also Materials and Methods]; thesepeaks accounted for the ⬇19.5% of the peaks detected in mouseplasma. These substances served as ‘‘time-indicating metabolites,’’
because they oscillate considerably even under constant environmental conditions (DD). For instance, at zeitgeber time 0 (ZT0; thebeginning of day) or circadian time 0 (CT0; the beginning of a
subjective day), concentrations of dawn-indicating metabolites,
which peak at approximately ZT0 or CT0 (Fig. 1B, green color bars
2 of 6 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0900617106

12

12Fig. 1. Circadian patterns of metabolites in mouseplasma. (A) Circadian changes in corticosterone levelsin the plasma of CBA/N mice under LD (Left) and DD,
Center) conditions. All values are mean ⫾ SEM. The
white bars above the graph indicate day, gray bars
indicate subjective day, and black bars indicate night/
subjective night. ZT0 is the time of light on, and CT0 is
the time the light used to be turned on. (B) Circadianoscillatory metabolites in the plasma of CBA/N mice[negative ions (up); positive ions (down)]. On the heatmaps, magenta tiles indicate a high quantity of substances and green tiles indicate a low quantity inplasma. Metabolites are sorted according to their molecular peak time (molecular peak times are indicatedas colors). (C and D) Identified oscillatory peaks measured by negative ion mode (C) and positive ion mode
(D). Mean value was set to 1.0.

in the molecular peak time), are high, whereas those of duskindicating metabolites, which peak at approximately ZT12 or CT12
(Fig. 1B, red color bars in the molecular peak time), are low.
Conversely, at ZT12 or CT12, concentrations of dawn-indicating
metabolites are low, whereas those of dusk-indicating metabolites
are high; this suggests that time-indicating metabolites can represent BT (BT), the endogenous state of circadian clock. In fact, theoscillations of these time-indicating metabolites are directly or
indirectly controlled by circadian clock, because the disruption offunctional molecular clock in Cry1⫺/⫺, Cry2⫺/⫺ mice (37) resultsin the alteration of circadian oscillations of these metabolites (Fig.
S1). We used these LC-MS data to construct the molecular
timetable of time-indicating metabolites (a ‘‘metabolite timetable’’)in mouse plasma (Table S1 online). We also note that, among thesetime-indicating metabolites (i.e., oscillatory peaks detected by
LC-MS), 14 oscillatory peaks were identified as various types oflysophosphatidylcholines with different unsaturated fatty acids(Fig. 1 C and D).
Measurement of BT from Independent Samples. To verify whether the

metabolite timetable was a good indicator of BT, we attempted toestimate the BT from the metabolite profiles of independently
sampled mice. We collected fresh blood plasma from individual
young male CBA/N mice every 4 h over 24 h under both LD or DD
conditions because of the possibility that sampling time and/or light
conditions would affect the accuracy of BT estimation. LC-MS
analysis was performed to profile the time-indicating metabolites in
the plasma samples (Fig. 2 A and B). After measured profile of thetime-indicating metabolites was normalized by using the metabolite
timetable, we filtered out outliers, fitted the normalized profile tocosine curve, and calculated the significance of its fitness (see also
Materials and Methods). This metabolite-timetable method could
successfully detect significant circadian rhythmicity in all metabolite
profiles of these samples (P ⬍ 0.01, Fig. 2 A and B). The estimatedBT closely matched with the environmental time when sampled(ZT under LD condition or CT under LD condition) with estimaMinami et al.

A

Estimated BT

B

Environmental time

Environmental time

CT0

CT12

CT4

CT16

CT8

CT20

2
1
0
-1
-2

ZT16
Normalized quantity

ZT4
2
1
0
-1
-2

ZT8

2
1
0
-1
-2

ZT20

2
1
0
-1
-2

2
1
0
-1
-2
0

4

8

12

16

20

0
4
Time (h)

8

0

12

4

16

20

0

4

8
12
16
Molecular peak time (h)

8

12

16

20

0
4
Time (h)Differences in Genetic Backgrounds. In clinical situations, methods
for BT detection must apply to populations with heterogeneous
genetic backgrounds. To demonstrate the suitability of the metabolite-timetable method for individuals with different genetic backgrounds, we applied the method to other inbred mouse strain withgenetic backgrounds that differed from the original CBA/N strain.
We chose C57BL/6, because C57BL/6 and CBA/N are genetically
remote from each other and classified into 2 completely different
clusters among 55 mice strains according to SNP-based study (38).
We collected the blood plasma samples from individual young male
C57BL/6 mice every 4 h over 24 h under LD and DD conditions andquantified the time-indicating metabolites in the plasma by LC-MS(Fig. 3 A and B). The metabolite-timetable method detectedsignificant circadian rhythmicity (P ⬍ 0.01) in all metabolite profiles
both under LD (Fig. 3A) and DD conditions (Fig. 3B) even if we

Estimated BT

B

Environmental time

ZT0

16

20

used the metabolite timetable constructed from CBA/N mice. The
estimated BT closely matched with the environmental time with theestimation errors of 1.6 ⫾ 0.36 h for LD and 1.7 ⫾ 0.24 h for DD(mean ⫾ SD, Table S2). These results suggest that BT can be
accurately determined from the metabolite profiles of mice withheterogeneous genetic backgrounds.
Differences in Age and Sex. We constructed the metabolite timetablefrom young male mice only, so it was possible that age and sex
factors might affect the accuracy of the metabolite-timetable
method. To determine the influence of age and sex, we also applied
the metabolite-timetable method to aged male and young female
mice of the same strain. Blood plasma from individual aged maleor young female CBA/N mice was sampled at 2 time points, ZT0(the beginning of the day, i.e., time of light on) and ZT12 (the endof the day, i.e., time of the light off) under LD condition. These time
points were considered as 2 ‘‘noisiest’’ time points, because light
conditions were dramatically changed at these points. Timeindicating metabolites in the plasma were quantified by LC-MS(Fig. 4A) and significant circadian rhythmicity (P ⬍ 0.01) was
detected in all metabolite profiles of both the aged male mice andEnvironmental time

CT0

CT12

CT4

CT16

CT8

CT20

2
1
0
-1
-2

ZT16
Normalized quantity

ZT4
Normalized quantity

Estimated BT

ZT12

2
1
0
-1
-2

12

20

tion errors of 1.0 ⫾ 0.49 h for LD and 1.3 ⫾ 0.45 h for DD (mean ⫾SD, Table S2). Estimation error was here defined as time difference
between estimated BT and sampling time (environmental time).
These results suggest that BT can be accurately determined fromthe metabolite profiles of independently sampled mice.

A

8

Fig. 2. BT estimation. BT measurements of mice kept
under LD (A) and DD (B) conditions. Colors of the dots
indicate the molecular peak times of each substance(Table S1). Peak time of the red cosine curves indicatesestimated BT and peak time of the blue indicates thetime the sample was taken (‘‘environmental time’’).
The greater the degree of overlap of the red and blue
curves, the greater the accuracy of the measurement.
The dashed vertical lines show the BT (red) or environmental time (ZT/CT, blue). See Table S2 for statistics.

2
1
0
-1
-2

ZT8

2
1
0
-1
-2

ZT20
2
1
0
-1
-2

2
1
0
-1
-2
0

4

8

12

16

20

0
4
Time (h)80Minami et al.

12

4

16

20

0

8
12
16
Molecular peak time (h)

4

8

20

12

16

20

0
4
Time (h)

8

12

16

20

Fig. 3. Genetic background. BT measurement using
C57BL/6 mice plasma collected under LD (A) and DD (B)conditions. Colors of the dots indicate the molecularpeak times of each substance (Table S1). Peak time of
the red cosine curves indicate estimated BT and peaktime of the blue indicate the environmental time. The
dashed vertical lines show the BT (red) or environmental time (ZT/CT, blue). See Table S2 for statistics.
PNAS Early Edition 兩 3 of 6

PHYSIOLOGY

2
1
0
-1
-2

Normalized quantity

Estimated BT

ZT12

ZT0

A

Estimated BT

B

Environmental time

ZT0

aged male

2
1
0
-1
-2

young female

Normalized quantity

2
1
0
-1
-2

Environmental time

ZT0

ZT12

ZT4

ZT16

2
1
0
-1
-2

young male

2
1
0
-1
-2

Normalized quantity

Estimated BT

ZT12

2
1
0
-1
-2

ZT8

0

4

8

12

16

20

0
4
Time (h)

8

12

0

ZT20

2
1
0
-1
-2

16

20

0

4

8
12
16
Molecular peak time (h)

4

8

12

16

20

0
4
Time (h)Differences in Feeding Conditions. The circadian rhythmicity of foodintake is well known (39); therefore, feeding conditions may
severely affect the accuracy of the metabolite-timetable method. To
validate the use of the metabolite timetable in individuals withdifferent feeding conditions, we applied the metabolite-timetable
method to CBA/N mice deprived of food (food deprivation). This
feeding condition differed greatly from the original feeding condition where CBA/N mice were allowed ad lib feeding. We collected
the blood plasma from individual young, male, food-deprived
CBA/N mice every 4 hours over 24 h under LD condition. LC-MS
analysis was performed to quantify the plasma metabolites (Fig.
4B). The metabolite-timetable method detected significant circadian rhythmicity (P ⬍ 0.03) in all metabolite profiles. The estimatedBT matched with the environmental time with the estimation errors

ZT0

ZT12

ZT8

8hrs
ZT20

ZT24

C

4

8

12

16

16

20

Detection of Jet Lag. The final stage of the study was to evaluate theuse of the metabolite-timetable in the diagnosis of circadian rhythm
disorders. Jet lag is a common disorder of circadian rhythm, inwhich there is a difference between the internal BT and environmental time. To mimic jet lag, we kept the mice for 2 weeks undernormal LD conditions and then rapidly advanced the lighting
schedule by 8 h (40). Plasma samples were analyzed at 2 time points(ZT0 and ZT12 of the original LD cycle, termed as ‘‘Time 1’’ and‘‘Time 2’’) on 3 separate days: on day 1 (before entrainment to the
new cycle), day 5 (during entrainment), and day 14 (after entrainment) (Fig. 5 A and B). On day 1, the estimated BTs were 23.8 h(Time 1) and 11.8 h (Time 2), suggesting that the internal BTs still
follow the original LD cycle. By day 14, estimated BTs were 8.8 h(Time 1) and 20.8 h (Time 2), suggesting that the internal BTs had
shifted by ⬇8 h from the original LD cycle and had therefore
become entrained completely to the advanced cycle. Notably, on
day 5, estimated BTs were 3.5 h (Time 1) and 15.5 h (Time 2), a shift

Estimated BT
Environmental time (pre)Environmental time (post)

20

Time 1Day 0

12of 2.2 ⫾ 0.50 h (mean ⫾ SD, Table S2). These results suggest that
BT can be determined from the metabolite profiles of mice evenunder severe feeding conditions.

Molecular peak time (h)
0

8

20

the young female CBA/N mice (Fig. 4A). The estimated BT fromindividual mice sampled at ZT0 and ZT12 were BT23.0 and BT11.0
in aged male mice and BT1.2 and BT13.2 in young female mice
(Table S2). These results demonstrate that BT can be accurately
determined from the metabolite profiles of mice of different age
and sex.

A

Fig. 4. Age, sex differences and feeding condition.
(A) BT measurement of young male (Top), aged male
(Middle), and young female mice plasma (Bottom)
harvested at ZT0 (Left) and ZT12 (Right). (B) BT measurement of young male mice kept under fooddeprivation conditions. Colors of the dots indicate themolecular peak times of each substance (Table S1).
Peak time of the red cosine curves indicates estimatedBT, and peak time of the blue indicates the environmental time. The dashed vertical lines show the BT(red) or environmental time (ZT, blue). Results foryoung male mice (A) are replotted from Fig. 2 A forcomparison. See also Table S2 for statistics.

Time 2

2
1

Day 2

0

Day 3

-1

Day 1

Day 1

-2

Day 14

B

Time 22
1

Day 5

Time 1

Normalized quantity

Day 5

0
-1
-2

2

Day 14

1
0
-1
-2
0

4

8

12

16

20

24

0

Time (h)4 of 6 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0900617106

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8

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Fig. 5. Detection of jet lag. (A) Schematic view oflighting conditions. White bars indicate light on, andblack bars indicate light off. On day 1, the light was
turned off 8 h earlier. Samples were collected at 2 time
points on days 1, 5, and 14 after the LD shift (redtriangles). (B) The actogram of a single mouse, showing that it was experiencing ‘‘jet lag’’ induced by the LD
shift. Yellow shading indicates periods of light on, andgray shading indicates periods of light off. The red
triangles indicate days 1 (Top), 5 (Middle), and 14(Bottom). (C) BT measurement from mouse plasmacollected before (day 1, Top), during (day 5, Middle),
and after entrainment to the new LD cycle (day 14,
Lower). Colors of the dots indicate the molecular peaktime of each substance (Table S1). The red cosine curveis the estimation, the blue curve is the environmental
time (pre LD condition shift), and the brown cosine
curve is the environmental time (post shift). See also
Table S2 for statistics.

Minami et al.

General Applicability of the Metabolite-Timetable Method: CE-MSBased Method. As described above, the metabolite-timetable

method based on LC-MS analysis can accurately measure theindividual’s BT and sensitively diagnose circadian rhythm disorders
such as jet lag. The method depends entirely on the oscillations of
numerous chemical substances and time-indicating metabolites;
therefore, it can be applied to other metabolomics technologies
such as CE-MS analysis. With CE-MS analysis, it is possible toseparate charged compounds. So this is a complementary technology to LC-MS analysis (35). To demonstrate the general applicability of the metabolite-timetable method to other metabolomics
technology, blood plasma was sampled and pooled from young
male CBA/N mice every 4 hours under LD or DD conditions over
2 days. Small positively charged chemical substances in thesesamples were quantified by CE-MS, which detected 953 peaks. Of
these peaks, 153 exhibited significant circadian oscillations under
LD and DD conditions (Fig. S3A). We used these CE-MS data toconstruct the metabolite timetable in mouse plasma (Table S3).
Notably, 28 peaks (18.3% of the total) were identified as known
metabolites (Fig. S3B).
To confirm whether the CE-MS-based metabolite timetable wasa good indicator of an individual’s BT, we estimated BT from themetabolite profiles of independently sampled mice. Fresh plasma
from young male CBA/N mice was collected every 4 hours over 24 hboth under LD and DD conditions. CE-MS analysis was performed
to profile the time-indicating metabolites (Fig. S3 C and D). The
CE-MS-based metabolite-timetable method detected significant
circadian rhythmicity in all metabolite profiles of these samples(P ⬍ 0.01, Fig. S3 C and D). The estimated BT was close to theenvironmental time with estimation errors of 0.6 ⫾ 0.29 h for LD
and 0.6 ⫾ 0.54 h for DD (mean ⫾ SD, Table S4). These results
suggest the metabolite-timetable method is generally applicable toother metabolomics technologies such as CE-MS. See Fig. S3 E–H
and SI Text for the result at a more stringent criterion (FDR ⬍ 0.01).
Discussion
We identified 14 and 28 oscillatory peaks in mouse blood as known
metabolites in LC-MS and CE-MS analysis, respectively. For example, a different type of lysophsophatidylcolines exhibit significant
circadian oscillations in LC-MS analysis (Fig. 1 C and D). The genes
for key enzymes synthesizing lysophosphatidylcoline are Lcat
(lecitine:cholesterol acyltransferase), Lipc (hepatic lipase), and Lipg
(endothelial lipase). Among these genes, Lcat and Lipc are mainly
expressed in liver. We found that Lipc mRNA are rhythmically
expressed in the mouse liver with the peak time around peak time(PT) 5 (8), which slightly proceeds with the peak time of identified
oscillatory lysophosphatidylcolines (PT5.8–9.1). In CE-MS analysis, many amino acids exhibit significant circadian rhythmicity. For
example, glutamine (Gln), threonine (Thr), proline (Pro), valine(Val), phenylalanine (Phe), methionine (Met), isoleucine (Ile),leucine (Leu), and tryptophan (Trp) peak at around midnight(⬇PT18), whereas glycine (Gly) peaks in the evening (PT12.1) (Fig.
S4). In the urea cycle, metabolites such as ornithine (PT18.6),
citrulline (PT19.9), and 4-guanidino-butyrate (PT20.1) exhibit significant circadian rhythmicity (FDR ⬍0.1). Arginine (Arg), whichplays an important role in the urea cycle, also exhibits suggestive
circadian rhythmicity (FDR ⫽ 0.215; PT0.6) in our CE-MS data. It
is also noteworthy that the final product—Urea—is reported to varyover 24 h in the blood of certain species such as rabbits (41) and rats
(42). Interestingly, Reddy et al. (43) showed that 3 key enzymes
involved in the urea cycle, carbamoyl-phosphate synthetase 1
Minami et al.

(CPS1), argininosuccinate synthetase 1 (ASS1), and arginase 1(ARG1), show circadian rhythms in the liver, the center of the urea
cycle and urea formation (43). In the creatine pathway and neighboring glycine and threonine metabolism, metabolites such as
guanidoacetate (PT6.2), Creatine (PT14.7), creatinine (PT18.7),
sarcosine (PT18.0), and dimethylglycine (PT16.5) exhibit significant circadian rhythmicity (FDR ⬍0.1). Arginine (PT0.6) first
converts to guanidoacetate. Guanidoacetatete (CT6.2) then converts to creatine. Creatine (PT14.7) finally converts to creatinine(PT18.7) or sarcosine (PT18.0), which is also converted fromdimethylglycine (PT16.5). The differences in the peak times of thesemetabolites may reflect successive processing throughout the day inthe creatine pathway and neighboring glycine and threonine
metabolism (Fig. S4).
Our results suggest that metabolite-timetable method can detectcircadian rhythm disorders in vivo. In a normal situation, patientslive under the zeitgeber (e.g., light). Notably, our method successfully diagnoses the jet-lag state under LD conditions (Fig. 5 and Fig.
S2), and this strongly suggests that endogenous abnormal clockstate can be diagnosed by our method, even if there is external time
information such as light. Circadian rhythm disorders are caused byenvironmental factors (such as jet lag) and/or inherited factors (asin familial advanced sleep-phase syndrome). Brown et al. (44, 45)
reported detecting circadian rhythm disorders by characterizing thefeature of molecular circadian clocks in the isolated cells. Theycollected skin samples from human subjects, cultured the cells, andtransfected clock-controlled reporter into the cells. The features ofthe molecular circadian clock in the isolated cells correlated withthe chronotypes (i.e., the feature of organismal circadian clock)of the subjects, suggesting that the method should also allow
detection of inherited circadian rhythm disorders. Our method can
detect both inherited and acquired circadian rhythm disorders but
cannot distinguish between them, whereas Brown et al.’s (44, 45)
method can detect inherited but not acquired disorders. These 2
methods are therefore complementary for detecting circadianrhythm disorders.
Although our results suggest the metabolite-timetable method
can successfully estimate BT, keeping MS and hiring a specialized
operator in each hospital seems difficult. Establishing a special
center for ‘‘detecting BT’’ performing MS analysis is 1 possibility tosolve this. Another possibility is detecting time-indicating metabolites in a specific way (e.g., making an ELISA kit for detecting BT
using a specific antibody for target time-indicating metabolites). To
achieve the latter possibility, the assignment of oscillatory peaks to
known metabolites is important, and we already identified 14(LC-MS) and 28 (CE-MS) oscillatory peaks as known metabolites(Figs. 1 and S3). We also examined the effect of peak numbers on
BT estimation. Fig. S5 shows the accuracy of the BT estimation
using the different number of oscillatory metabolites. If we set thestatistical error rate P ⬍ 0.05 and estimation error between environmental and estimated time ⬍2 h, the minimum number oftime-indicating metabolites was ⬇20. In addition, the effect of
feeding is an important issue, especially in humans, because humans
eat different amounts of food at entirely different times. Further
analysis on food intake conditions would be a great help for
applying this method in clinical situations.
In this study, we showed that a metabolite-timetable method
based on LC–MS analysis is able to estimate individuals’ BTs witha high degree of accuracy throughout the time of the day, under
different lighting conditions (LD and DD), and in individuals withdifferent genetic backgrounds (CBA/N and C57BL/6 mice) (Figs. 2
and 3). We also found that the LC–MS-derived metabolite timetable is robust despite differences in age, sex, and feeding (Fig. 4);
in addition, it is a sensitive and accurate detector of disorderedcircadian rhythm in jet-lagged mice. Our preliminary results suggestthat the metabolite-timetable method can be also applied to othermetabolomics techniques such as CE–MS; it allowed quantificationof hundreds of clock-controlled metabolites, of which many could
PNAS Early Edition 兩 5 of 6

PHYSIOLOGY

of 3.5 h from the original LD cycle, indicating incomplete entrainment to the advanced cycle, i.e., jet lag (Fig. 5C and Table S2).
These results suggest that the metabolite-timetable method canaccurately detect circadian rhythm disorders. Another set of BT
estimation data for jet-lagged mice supports this conclusion (Fig. S2and Table S2).

be identified, enabling successful measurement of BT from independent blood samples. The next step is to construct a metabolite
timetable for human blood plasma, which will help measurement of
BTs for humans and diagnosis of circadian rhythm disorders andfacilitate the development of chronotherapy and tailored medication regimens.

{(Asi ⫺ Msi)/Sti}, and we predicted b as BT of the target sample. To estimate theP value of the prediction, we applied a permutation test to the maximum
correlations.

Materials and Methods

Supporting Information. More Materials and Methods information is available in
SI Text.

BT Measurement. Metabolomics-based measurement of BT is performed as
described for expression-based measurement of BT (16), except that 2 samples areused for an estimation of BT. In the metabolite-timetable method, we used 2
samples with 12-h sampling time interval (e.g., ZT0 and ZT12 are used for 1
measurement of BT) to calibrate measurement-to-measurement experimental
fluctuations of detection sensitivity, which usually differs among metabolites. We
define the area in a certain sample as Asi and the mean areas of 2 samples (of 12-htime interval) as Msi for metabolite i. We also define the mean, standard deviation, and peak time of metabolite i in the timetable as Mti, Sti, and pti, respectively.
For estimation of BT, we did not use outlying metabolites that do not satisfy thecondition (兩(Mti ⫺ Msi)/Sti兩 ⬍ 2公2). By changing b to 0, 0.1, . . . , 23.9, we searched
for b with a maximum Pearson’s correlation between {公2 cos(2␲(Pti ⫺b)/24)} and1. Dunlap JC, Loros JJ, DeCoursey PJ, eds (2004) Chronobiology: Biological Timekeeping(Sinauer, Sunderland, MS).
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Ethics. All experiments were performed with the permission of Kobe Animal
Experiment Supervisory Panel (permission IDs are AH15-10 and AH18-01).

ACKNOWLEDGMENTS. We thank Sachie Satoh and Yuki Ueno for technicalsupport. We also thank Shigenobu Shibata, Masamitsu Iino, Kenichiro Uno,
Rikuhiro Yamada, Hideki Ukai, Guojun Sheng, Yasushi Isojima, and Sato Honma
for helpful discussions and Erik Kuld for proofreading of the manuscript. The
Laboratory for Animal Resources and Genetic Engineering in the Center for
Developmental Biology is noted for assistance with mouse work. This work is
partly supported by New Energy Developing Organization (NEDO) (H.R.U.), a
grant on Priority Aeas ‘‘Ensuring Chemical Hazard’’ and ‘‘Biomarker Discovery for
Drug Development’’ from the Health and Labor Sciences Research Grants (T.S.),and a grant ‘‘Functional Genomics on Fundamental Brain Functions’’ from Utsunomiya University (M.I.).

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