Cell Reports
Supplemental Information

Mammalian Reverse Genetics without Crossing
Reveals Nr3a as a Short-Sleeper Gene
Genshiro A. Sunagawa, Kenta Sumiyama, Maki Ukai-Tadenuma, Dimitri Perrin, Hiroshi
Fujishima, Hideki Ukai, Osamu Nishimura, Shoi Shi, Rei-ichiro Ohno, Ryohei Narumi,
Yoshihiro Shimizu, Daisuke Tone, Koji L. Ode, Shigehiro Kuraku, and Hiroki R. Ueda

SUPPLEMENTAL EXPERIMENTAL PROCEDURES
A Computational Model to Estimate the Minimum Efficiency to Generate
Whole-body KO Mice by Multiple-target CRISPR Methods
A computational model estimated the efficiency of different CRISPR methods forgenerating whole-body knockout mice as follows. When sufficient Cas9 are provided,
the efficiency of the single-gene mono-allelic knockout P can be described as:
P  =

S
  K+S

, where total gRNA concentration and the dissociation constant between the gRNAand its target site are defined as S and K, respectively. This simple phenomenologicalmodel is adopted as the first-order approximation of the observed saturation in
CRISPR-mediated DNA cutting. It describes the net effect, which is therefore
intended to mechanistically include the known biochemical processes carried on
during DNA cutting (e.g. PAM site binding, as well as, Cas9 remaining bonded to thecut DNA site on both strands following a cut).
When a single allele is targeted by three different gRNAs at the concentration
S/3 with dissociation constants (K1, K2, and K3) between each gRNA and its target
site, the single-gene mono-allelic KO efficiency P can be described as:
P  =  1  -  K1K1 +S/3

K2
K2 +S/3

K3
K3 +S/3

The concentration of each gRNA is S/3 to keep total gRNA concentration as S.
Now, to include the recovery rate from single-site damage (α) is introduced
as the probability to recover of a single target site from a damaged state to an intact
1

state. When α is considered, the single-gene mono-allelic KO efficiency P for asingle-target method (Figure 1B) can be described as:
P  =  

S(1-α)
K+S

Similarly, the single-gene mono-allelic KO efficiency P for a triple-target CRISPR
method (Figure 1B) can be described as:
S K1 K2 +K2 K3 +K3 K1 1-α +S2 K1 +K2 +K3 1-α2 +S3 1-α3P  =  
K1 +S/3 K2 +S/3 K3 +S/3
When a single allele is targeted by N different sites, the dissociation constant
between each gRNA and its target site is defined as Ki (i=1⋯N). The single-genemono-allelic KO efficiency P for a N-tuple-target method (Figure S1A) can be thendescribed as:
P  =  S K1 ⋯KN-1 +⋯+K2 ⋯KN 1-α +⋯+SN-1 K1 +⋯+KN 1-αN-1 +SN 1-αNN
i=1 Ki +S

In our simulations, the single-gene mono-allelic KO efficiency P was calculated 1,000
times for each given α, N and S. In every time of calculation, one set of K (1, 3, and N
for single-, triple-, and N-tuple-target methods, respectively) were picked up randomly
from a log-normal distribution whose geometric mean and geometric standard
deviation as 1.0 and 2.5, respectively (Figure S1B). Without loss of generality, we
can use 1.0 for the geometric mean of the distribution of the dissociation constant K.
In order to estimate its geometric standard deviation, we first estimated three gRNA
dissociation constants from the SSA assay for three gRNAs for Tyr gene (K1 =2.32203, K2 = 1.16562 and K3 = 0.369466 for Tyr-1, Tyr-2 and Tyr-3, respectively,
Figure 1D). We note that K is estimated as an inverse-proportional value to the
DNA-cleavage efficiency measured from SSA assays. Since the geometric standard2

deviation of these estimated K values was 2.53097, we used 2.5 for the geometricstandard deviation. In order to estimate the efficiency of single-gene biallelic KO,
double-gene biallelic KO, and triple-gene biallelic KO, we calculated P2, P4 and P6,
respectively when α = 0.10 (Figure S1A). Since there is uncertainty for α and S invivo, we also calculated single-gene biallelic KO efficiency P2 with different values of
α and S (Figure S1C, α = 0.05, 0.10, 0.15, and S = 5, 10, 15, respectively). We notethat calculated KO efficiencies for multiple-target methods (N > 1) predict the“minimum” efficiency of KO since we did not include, for its simplicity, the probabilityof other type of mutations in the multiple-target CRISPR models (e.g. large deletions
induced by multiple-target CRISPR methods), which is difficult for a DNA repair
system to recover back to the intact state.
To estimate the realistic values for 𝛼  and S, we constructed three
single-target CRISPR models with different dissociation constants (K1 = 2.32203, K2= 1.16562 and K3 = 0.369466) estimated from the SSA assay results for Tyr-1, Tyr-2,
and Tyr-3, respectively. We compared that these models with the experimental
results of single-target CRISPR (36.0%, 54.2% and 64.7% for Tyr-1, Tyr-2 and Tyr-3,
respectively), and used a least squares method to estimate the realistic values for arecovery rate of mutation (α = 0.136428) and total gRNA concentration (S = 5.7248),
respectively. We then performed 1,000 simulations to calculate single-gene biallelic
KO efficiency (P2) for single-target (N = 1) and triple-target (N = 3) CRISPR models
with α = 0.136 and K, which are randomly picked up from the log-normal distribution
in Figure S1B. As a results, single-gene biallelic KO efficiency (P2) for single-target(N = 1) and triple-target (N = 3) CRISPR models at S = 5.7248 are 0.512 ± 0.138 and0.826 ± 0.098 (mean ± SD), respectively (Figure S1F), indicating that a triple-target
CRISPR method is much more efficient than a single-target CRISPR method when
we used the realistic parameter values for α and S. In addition, the triple-target3

strategy using the mixture of Cas9 mRNA and three gRNAs achieved almost perfect
efficiency (97.5%, Figures 1E and 1F), which is even more efficient than thepredicted minimum efficiency (82.6%) (Figure S1F).

Sleep Analysis by EEG/EMG
We anesthetized the animals and implanted telemetry transmitters (F20-EET, DSI)
into them for EEG/EMG recording. The detailed methods were described previously(Sunagawa et al., 2013). The recorded data were analyzed by the FASTER method
followed by a 16-second-rule filter. This rule only allows sleep or wake epochs lasting
longer than 16 seconds (two epochs in this study). If the automatically staged results
include sleep or wake episodes sized less than 16 seconds, these epochs will be
converted to the stage just before the episode started.

Equivalent Electrical Circuit Simulation of the SSS System.
Introducing the Laplace transform, the SSS system can be modeled as an electricalcircuit (Figure 3B). Reducing the physical properties of the SSS components to thoseof electrical circuit elements, the pressure difference detected by the sensor can bepredicted through simulation. Using these results, we optimized two variable
components in the SSS system, i.e., a resistance (R1) and a capacitance (C2).
The mathematical descriptions of the model are as follows. When the total
air flow is I, the flow can be divided into I1 and I2, which are the flow to the chamber
and the sensor (Figure 3B), respectively. How I is divided into I1 and I2 is determined4

by the ratio of conductance of the chamber and the sensor unit. This conductancecan be described in the Laplace domain as:
G1 s   =  

1
C1 s+

G2 (s)  =  R2 +

1
R1

1
C2 s

where G1 and G2 denote the conductance of the SSS chamber and the pressure
sensor unit, respectively. C1 is the capacitance of the SSS chamber, R1 is theresistance of the SSS chamber, R2 is the resistance of the pressure sensor, and C2 isthe capacitance of the sensor. Using G1 and G2, the air flow to the sensor can be
described as:
I2   =  I  -  I1   =  G1
IG1 +G2This equation can be interpreted as I2 is a value that I is amplified by
G1/(G1+G2). Now, R2 is the resistance of the sensor and C1 depends on the volume of
the chamber. Using a simple flow meter and a flow pump and the pressure sensor,
we estimated R2 and C1 as 152 Pa·s/ml and 0.018 ml/Pa, respectively. Because thiswhole circuit can be treated as a band-pass filter, our goal was to optimize R1 and C2
to maximize the gain of G1/(G1+G2) over the physiological respiration frequency ofmice. We ran computer simulations and selected 172 Pa·s/ml and 6.12 × 10-5 ml/Pa
as an optimal value for R1 and C2 in our environment (Figure 3C).

5

Fully-Automated Sleep/wake Phenotype Analysis by SSS Annotation Pipeline
The SSS annotation pipeline realizes fully automated sleep/wake phenotype analysis
from respiration data of mice. The basic staging strategy was based on the FASTER
method, which automatically stages the sleep state from EEG/EMG recordings(Sunagawa et al., 2013) (Figure S2B).
The SSS annotation pipeline is composed of three major steps (character
extraction, clustering, and annotation). In the character extraction step, the recorded
respiratory flow data are split into 8-second-length segments (called “epochs”). Eachepoch of respiratory-flow time-domain data are next converted by fast Fouriertransform (FFT) into frequency-domain data, the respiration power spectra. We used
the power up to the maximum frequency, called Nyquist frequency (i.e. half of thesampling frequency), which corresponds to 125 Hz in this study because therespiratory flow was recorded at 250 Hz. Characters of respiration power spectra arethen extracted by principal component analysis (PCA). The top three principal
components were used in the following analysis.
In the following clustering step, we used the nonparametric density estimation
clustering method (Azzalini and Torelli, 2007) to cluster the epochs. This clustering
method automatically selects the number of clusters, and therefore it is powerful tocluster certain datasets without a priori model or without information on the number ofclusters. A 6-day length data includes 64800 epochs. Entire epochs are divided into
12 group. Each group including 5400 epochs is used for a single-run of clustering.
There are two parameters used for this clustering. One is the smoothing factor hmult
which is used when the probability density is estimated from the given data. The otheris Ngrid, which affects the grid density for scanning the cluster core from the estimatedprobability density. We used 0.80 and 270 as hmult and Ngrid, respectively.

6

In the final annotation step, each cluster is annotated according to thestatistical value of characteristics for each cluster. During sleep the respiratory patternrepresents a slow and regular flow while during wake the flow become fast and highly
fluctuated (Figure S2A). This time-domain observation in sleep turns out to have a
peaky shape in power spectrum due to the regular respiration patterns. On the other
hand, during waking, the power spectrum of the respiratory flow tends to include wide
range of spectra, therefore showing relatively more uniform power spectrum due to its
fast and fluctuated respiration patterns. Focusing on these characteristic patterns in
the frequency domain, we classified “sleep” and “awake” states by using larger andsmaller skewness of the epoch’s power, respectively. “Sleep” state exhibits larger
skewness than awake state because of its higher power for a narrower range of
frequency and lower power for other range of frequency, which will make the
distribution of sleep-epoch’s power positively skewed. Therefore, we defined whenthe median of skewness of epoch’s power spectrum in one cluster is within the 49th
percentile (from the lowest value) of skewness of power spectrum of entire epochs,
the cluster is annotated as “awake,” otherwise as “sleep”. After each cluster isannotated by skewness of power spectrum, the two epoch rule (one state continues
at least two epochs) is applied to the results and the staging completes. All steps
were fully-automated and unsupervised, which allows a highly-objective andreproducible sleep/wake phenotype analysis. The source code of the SSS analysis isavailable at Supplemental Code S1 (https://goo.gl/teteJM).
The SSS technology is applied only to mice in this study. However, it might bepossible to extend this method to larger organisms such as primates and human.
SSS only uses a simple pressure sensor to detect the respiration pattern. Therefore,
the subject needs to be placed in a semi-closed area for monitoring. Applications
could be sleep monitors for babies in the incubator, or in the car for drivers.
7

Calculation of Sleep/wake Parameters
Four sleep/wake parameters are mainly used in this study. One is the “sleep time.”
This is the length of time that an animal’s status is staged as sleep over a given
period of time. The second is the “amplitude of sleep,” or simply the “amplitude.”
When an animal’s sleep time is recorded for D days and summed in the
B-minutes-bin, the sleep time of the kth bin is expressed as Tk , where
1  <  k  <  DLB
and
1440
B

LB   =  The average sleep time of the mth bin of the day  is expressed as1Tm   =  D

D

Tm+LB (i-1)
i=1When the mean of all bins is expressed as
1
ET   =  
LB

LB

Ti
i=1

the amplitude A is defined as
1
A  =  ET LB

LB

Ti -ET
i=1

82

The amplitude value differs with the bin “size” (i.e., duration) used to integrate
the sleep time. We used a 10-min bin size in this study for amplitude calculation.
Circadian and/or daily rhythmicity is expressed as a larger sleep/wake amplitude
value.
The third and fourth statistics are the transition probability between sleep andawake states. In this study, sleep staging is undergone every 8 seconds. This minimal
unit of sleep/wake state is called a segment (or an epoch). When we focus on two
consecutive epochs, there are four possible sets of state. They are sleep to awake,
awake to sleep, two sleep repeats and two wake repeats. When Nmn(X) is defined asa number of transitions from state m to n (m,n ⊆ {sleep, awake}) within the period X,
Pws   =  Nws
Nsw
, Psw   =  .  
Nws +Nww
Nss +Nsw

The two transition probabilities, Pws and Psw, which are independent of eachother, are indirect stabilization parameters that negatively reflect the stabilization of
the awake state (Pww) and sleep state (Pss), respectively (Pws = 1 - Pww, and Psw = 1 Pss). We think PWS and PSW are useful to experimentally test the sleep models which
try to provide sleep-wake temporal architecture (Diniz Behn and Booth, 2011)
because the transition probabilities can be quantified by SSS.
Pharmacological Administration
Methamphetamine (Philopon, Sumitomo Dainippon Pharma, Lot #006281) was
diluted with natural saline to be 3 mg/kg at 100 µl of solution. The vehicle was naturalsaline. Each mouse received an i.p. injection at ZT2.
Diazepam (Horizon, Maruishi Pharmaceutical, Lot #3Y140) was diluted withnatural saline to be 5 mg/kg at 300 µl of solution. The vehicle was natural saline. Eachmouse received an i.p. injection at ZT13 under dark conditions.
9Every animal used for pharmacological experiment was C57BL/6N male mice
(6 to 8-week-old). Mice were assigned arbitrarily to control or treatment groups except
the animals used in dual recording for EEG/EMG and SSS. In the dual recording, thecontrol animals used in DZP i.p. injection were used for MAP i.p. injection after 4 daysof recovery period.

Design of Target Sequences for gRNA
The target sequences for Tyr (Figure 1C) were designed using Jack Lin's on-line
CRISPR gRNA finder (http://spot.colorado.edu/~slin/cas9.html). The target
sequences for Bmal1, Cry1, Cry2, Per1, Per2, Hcrt (Figures S4A-C and S4K), Nr1,
Nr2a, Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), and Nr3b (Figures S5A-G and S6A)
were designed using the on-line CRISPR guide RNA Design tool
(http://cas9.cbi.pku.edu.cn/index.jsp)(Ma et al., 2013). Possible off-target sequences
within the mouse genome for each target sequence were checked using the CRISPR
Design Tool (http://tools.genome-engineering.org) (Ran et al., 2013).
The alternative target sequences for Tyr (set 2 and 3, Figure 7D) wereselected from the list resulting from the mm10 CRISPR/Cas9 database(http://www.crispr.riken.jp/).

Construction of the pGL3-SSA Plasmid for the Single-strand Annealing (SSA)Assay
Two partial fragments of the pGL3-control vector (Promega), containing the 5'- or 3'partial sequence of the Luciferase gene, were amplified using PCR with the following
10

primers: 1) forward 5'-GTAAAATCGATAAGGATCCGTCGAC-3', (Hokkaido System
Science), and 2) reverse:
5'-CAGCTGAAACTGCAGAAAGATATCAAAGAATTCTTAATCCAGATCCACAACCT
TCGCTTC-3' as primers amplifying part of the vector backbone and the 5' portion of
the Luciferase gene, and 3) forward
5'-GATATCTTTCTGCAGTTTCAGCTGCCAATCATCCAAAAAATTATTATCATGG-3',
and 4) reverse 5'-CATCGGTCGACGGATCCTTATCG-3' as primers amplifying part ofthe vector backbone and the 3' portion of the Luciferase gene. Both PCR products
were digested by PstI and BamHI, and mutually ligated. The resulting vector, whichcontained multiple cloning sequences(5'-TAAGAATTCTTTGATATCTTTCTGCAGTTTCAGCTG-3':
Stop-EcoRI-EcoRV-PstI-PvuII) between the 5'- and 3'-partial Luciferase sequences,
was designated as pGL3-SSA.

Construction of the pSSA-Tyr-1 and pSSA-Tyr-2/3 Plasmids.
We amplified 199-base and 200-base fragments containing the target sequences for
Tyrosinase (Tyr-1, and both Tyr-2 and Tyr-3, respectively, Figure S1D) from
C57BL/6 mouse genomic DNA by PCR. The PCR products were 5'-endphosphorylated with the Mighty Cloning kit (TaKaRa), and inserted into the EcoRV
sites of the pGL3-SSA plasmid (see above). The resulting vectors were designatedas pSSA-Tyr-1 and pSSA-Tyr-2/3, respectively.

11Oligonucleotide Sequences for the Target Sequences (Hokkaido System
Science)pSSA-Tyr-1:
Forward oligonucleotide: 5'-GGCACCTATGGCCAAATGAACAATGGG-3'
Reverse oligonucleotide: 5'-GTTCCCACAATAACAAGAAAAGTCTGTGCC-3'
pSSA-Tyr-2/3:
Forward oligonucleotide: 5'-TGGAACAAGCCAGTCGTATCTGGCC-3'
Reverse oligonucleotide: 5'-TCACAGATGGCTCTGATACAGCAAGCTG-3'

Construction of the pX330-Tyr-1, pX330-Tyr-2, pX330-Tyr-3, pX330-Bmal1-1,
pX330-Bmal1-2, and pX330-Bmal1-3 Plasmids.
Oligonucleotides (Hokkaido System Science) containing the target sequences for
Tyrosinase and Bmal1 (Tyr-1, Tyr-2, Tyr-3, Bmal1-1, Bmal1-2, and Bmal1-3,
respectively) were annealed and inserted into the BbsI sites downstream of the U6
promoter on the pX330 plasmid (Cong et al., 2013; Ran et al., 2013) [Addgene#42230]. The resulting vectors were designated as pX330-Tyr-1, pX330-Tyr-2,
pX330-Tyr-3, pX330-Bmal1-1, pX330-Bmal1-2, and pX330-Bmal1-3, respectively.

Oligonucleotide Sequences for the Target Sequences
Tyr-1:
Forward oligonucleotide: 5'-CACCGTGTCAAGGGACACACTGCT-3'
Reverse oligonucleotide: 5'-AAACAGCAGTGTGTCCCTTGACAC-3'

12

Tyr-2:
Forward oligonucleotide: 5'-CACCGTTATTGCTGCAGCTCTCTC-3'
Reverse oligonucleotide: 5'-AAACGAGAGAGCTGCAGCAATAAC-3'
Tyr-3:
Forward oligonucleotide: 5'-CACCGAAGAAGAAGCAACCCCAGG-3'
Reverse oligonucleotide: 5'-AAACCCTGGGGTTGCTTCTTCTTC-3'
Bmal1-1:
Forward oligonucleotide: 5'-CACCGTGTGGACTGCAATCGCAAG-3'
Reverse oligonucleotide: 5'-AAACCTTGCGATTGCAGTCCACAC-3'
Bmal1-2:
Forward oligonucleotide: 5'-CACCGTGTTCTGCATATTCTAACC-3'
Reverse oligonucleotide: 5'-AAACGGTTAGAATATGCAGAACAC-3'
Bmal1-3:
Forward oligonucleotide: 5'-CACCGTCAGATTGAAAAGAGGCGT-3'
Reverse oligonucleotide: 5'-AAACACGCCTCTTTTCAATCTGAC-3'

SSA Assay
293T cells were maintained in DMEM (Life Technologies) supplemented with 10%
FBS (JRH Biosciences) and antibiotics (100 U/ml penicillin and 100 µg/ml
streptomycin; Life Technologies). One day prior to transfection, the cells were plated
onto six-well plates at a density of 4×105 cells per well. The following day, the cells
were co-transfected using FuGene6 (Roche) with 1 µg of pSSA-Tyr-1 or
pSSA-Tyr-2/3 reporter plasmids in the presence of the following constructs, as
13

indicated in Figure S1E: 0 or 2 µg of pX330, pX330-Tyr-1, pX330-Tyr-2, or
pX330-Tyr-3 plasmid, according to the manufacturer's instructions. Empty vector wasused to bring the total amount of DNA to 3 µg per well. In addition, 50 ng of aphRL-CMV plasmid [Renilla luciferase (RLuc) reporter vector, Promega] was includedin each transfection as an internal control for transfection efficiency. Forty-eight hours
after transfection, the cells were harvested and assayed using the Dual-Luciferase
Reporter Assay System (Promega). The Luciferase activity was normalized to the
Rluc activity.

Cas9 mRNA Synthesis
p3s-Cas9HC plasmid(Cho et al., 2013) [Addgene #43945], which includes a T7
promoter-fused Cas9 coding region, was digested with XbaI (TaKaRa), and used as
the template for in vitro transcription using the mMESSAGE mMACHINE T7 kit (LifeTechnologies). The Cas9 mRNA was purified using the MEGAclear kit (LifeTechnologies).

gRNA Synthesis
The gRNA templates for Tyr (Figure 1C) and Bmal1 (Figure S4A) were
simultaneously fused to the T7 promoter and amplified from the pX330-Tyr and
pX330-Bmal1 vectors, respectively (see above) by PCR using the primers listed
below (Hokkaido System Science) (Wang et al., 2013). Alternatively, the gRNA
templates for Cry1, Cry2, Per1, Per2, Hcrt (Figures S4B, S4C and S4K), Nr1, Nr2a,
Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), Nr3b (Figures S5A-G and S6A), and Tyr(Figure 7D) were directly synthesized and fused to the T7 promoter by PCR. First,
14the partial fragments of the gRNA templates including each target sequence were
amplified from pX330 plasmids (Addgene #42230) using PCR with the Common
Reverse primer and Forward primer-1 (Hokkaido System Science) for each target
sequence (Table S3). Subsequently, the T7 promoter-fused gRNA templates were
amplified from the diluted PCR products using PCR with the Common Reverse primer
and Forward primer-2 (Hokkaido System Science) for each target sequence (Table
S3).
The T7 promoter-fused gRNA PCR fragments were used as the template forin vitro transcription using the MEGAshortscript T7 kit (Life Technologies). The
gRNAs were purified using the MEGAclear kit (Life Technologies).

Common Reverse Oligonucleotide for T7-gRNAs:
5'-AAAAGCACCGACTCGGTGCC-3' (Wang et al., 2013)

Oligonucleotide Sequences for T7-gRNAs of Tyr (set 1) and Bmal1
Tyr-1 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTCAAGGGACACACTGCT-3'
Tyr-2 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTTATTGCTGCAGCTCTCTC-3'
Tyr-3 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGAAGAAGAAGCAACCCCAGG-3'
Bmal1-1 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTGGACTGCAATCGCAAG-3'
15

Bmal1-2 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTTCTGCATATTCTAACC-3'
Bmal1-3 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTCAGATTGAAAAGAGGCGT-3'

Oligonucleotide Sequences for Other T7-gRNAs
Oligonucleotide sequences of triple-target gRNAs (T7-gRNAs) for Cry1, Cry2, Per1,
Per2, Hcrt, Nr1, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), Nr3b, and Tyr (set 2 and
3) genes are described in Table S3.

Genotyping of KO Mice by Quantitative PCR (qPCR) and Sequencing
We first prepared the genomic DNA of wild type and Tyr KO mice from their brains,
scalps, and tails using the Wizard Genomic DNA Purification Kit (Promega, wild type,
Tyr KO mice, Figure S1H) according to the manufacturer's instructions, thenperformed qPCR analysis for genotyping of these mice using the ABI PRISM 7900
(Applied Biosystems)/ QuantStudio7 Real-Time PCR System (Life Technologies),
SYBR Premix Ex Taq GC (TaKaRa) and primers for qPCR (Table S4, Hokkaido
System Science) and confirmed that the quantitative qPCR results matched with eachother among the genomic DNAs extracted from brains, scalps and tails. Therefore,
we hereafter prepared the genomic DNA only from the tails using the DNeasy Blood& Tissue Kit (QIAGEN) for genotyping of other KO mice generated for sleep/wakephenotyping (Bmal1, Cry1/Cry2, Per1/Per2, Hcrt, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a, Nr3b
and Tyr KO mice). The absolute target site abundance was calculated using a
16

standard curve obtained from wild-type genomic DNA. The amount of Tbp(Tsujino etal., 2013) was quantified and used as an internal control.
When the amplified intact DNA by qPCR is less than 0.2% of wild-type
genome, we judged as the target DNA is not detectable. When either three targets ofthe gene were not detectable, we classified the animal as a knock-out. When all threetargets were detectable, genotyping by sequencing was performed. 0.6 ~ 1 kb aroundthe each target sequence was amplified by PCR with Ex Taq (TaKaRa) and theprimers (Table S4, Hokkaido System Science). The fragment was subcloned into
pGEM-T-easy vector (Promega), and sequenced. When every subcloned sequence
has deletion and/or insertion which is not an in-frame, the target was labeled asknock-out. When the target gene has at least one knocked-out target by sequence,
the animal was classified as KO. Otherwise the animal was classified as non-KO. For
double-KO animals, we classified the animal as KO when both genes were knocked
out by the criteria written above.

Protein Quantification
Absolute amount of TYR and NR3A proteins were quantified by selected reaction
monitoring (SRM) MS. Sample processing for the MS analysis was performed
according to a phase-transfer surfactant (PTS) protocol (Masuda et al.) with several
modifications. To analyze TYR abundance, ears from the mutant mice Tyr #3, Tyr #4,
and Nr3a (set 2) # 10 were used. To analyze NR3A abundance, brains from themutant mice Tyr #3, Tyr #4, Nr3a (set 1) # 1 - # 8, and Nr3a (set 2) # 1 - # 12 wereused. In brief, the tissues were homogenized by sonication in PTS buffer (12 mM
sodium deoxycholate, 12 mM sodium N-lauroylsarcosinate, and 50 mM NH4HCO3)
containing a phosphatase inhibitor cocktail (Nacalai Tesque) and a protease inhibitor
17

cocktail (Nacalai Tesque) and were clarified by centrifugation at 10,000xg for 10 min.
The resulting homogenates were frozen in liquid nitrogen and stored at -80°C until
use. Protein concentrations were determined by Quick Start Bradford Dye Reagent(Bio-Rad).
As internal standards, we added the synthetic peptides with the isotope
labeled lysine and arginine residue (13C6-Lys and 13C615N4-Arg, respectively). The
synthetic peptides were pre-quantified and digested to produce specific sequences
for the quantification of target proteins (DTLLGGSEIWR for the quantification of Tyr
protein and VVTLIEHPFVFTR for the quantification of NR3A protein) according to our
newly developed method described elsewhere. The proteins/standards mixture were
subjected to cysteine reduction and alkylation (10 mM TCEP at 37°C for 1 hr and 15
mM iodoacetamide at 37°C for 30 min in dark) followed by 5-fold dilution with 50 mM
NH4HCO3 solution. Then, enzymatic cleavage of proteins was performed by 8
hr-incubation with 1:50 (w/w) LysC followed by 16 hr-incubation with 1:50 (w/w)
trypsin at 37°C. The digestion was stopped by mixing an equal volume of ethyl
acetate in the presence of 0.5% TFA. The surfactants in the sample were removed bydiscarding the ethyl acetate phase. The left aqueous phase containing peptides wasdried with SpeedVac (Thermo Scientific). The dried peptide mixture was thendissolved in an analytical buffer (2% acetonitrile and 0.1% TFA). The resulting
peptide solution was desalted by using StageTip (Rappsilber et al.). Twenty µg of thepeptide mixture prepared from brain was dissolved in 20 µL of analytical buffer. 100µg of the peptide mixture prepared from ear was pre-fractionated into 6 fractions byStageTip-based fractionation (Wisniewski et al.), and then the fractions weredissolved in 14 - 30µL of analytical buffer.
The resulting peptide mixture corresponding to approximately 1µg of proteins
was analyzed by liquid chromatography (LC)-MS using a triple quadruple mass18

spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific) equippedwith a captive spray ionization source (Michrom Bioresources), a nano-Advance
UHPLC system (Bruker Daltonics), and an HTC-PAL autosampler (CTC Analytics)
with a trap column (0.3 x 5 mm, L-column, ODS, Chemicals Evaluation and ResearchInstitute, Japan). Analytical samples were separated by reversed phase
chromatography, in which we used a home-made capillary column (length of 200 mm
and inner diameter of 100 µm) packed with 2 µm C18 resin (L-column2, Chemicals
Evaluation and Research Institute, Japan), a gradient of acetonitrile from 4 to 36% in
0.5% acetic acid over 105 min at a flow late 300 nL/min. Elution was directly
electrosprayed (1.6 kV) into the MS.
To analyze TYR protein in ear samples, the heavy and light versions of
DTLLGGSEIWR were monitored by LC-SRM-MS by using the SRM transitions of m/z
623.8 to 804.4 (for the light version) and m/z 628.8 to 814.4 (for the heavy version).
To analyze NR3a protein in brain samples, the heavy and light versions of
VVTLIEHPFVFTR were monitored by LC-SRM-MS by using the SRM transitions ofm/z 520.0 to 766.4 (for the light version) and m/z 523.3 to 766.4 (for the heavy
version).

Library Preparation for Exome Sequencing
Exome libraries were constructed using SureSelectQXT Reagent Kit (Cat. No.
G9681A, Agilent Technologies) and SureSelectXT Mouse All Exon Kit V1 (Cat. No.
5190-4641, Agilent Technologies) according to the manufacturer’s instructions.
Fragmentation and adapter-tagging of purified genomic DNA samples were
performed by transposase-based reaction. After an amplification of adapter-tagged
DNA library, purification was performed with AMPure XP beads (Beckman Coulter).
19

The purified libraries were hybridized to the SureSelect capture library, and collectedhybridized DNA using streptavidin-coated beads. The enriched DNA libraries were
amplified with PCR using the appropriate pair of dual indexing primers to add index
tags for the Illumina TruSeq system. In each purification step, the length distribution
and concentration of DNA molecules in libraries were analyzed using the 2100
Bioanalyzer (Agilent Technologies).

Exome Sequencing and Indel Detection
The libraries were subjected to on-board cluster generation using TruSeq Rapid PE
Cluster Kit (Cat. No. PE-402-4001) and sequenced on Rapid Run Mode of Illumina
HiSeq 1500 (Illumina) to obtain paired-end reads with 126 cycles using three 50-cycle
SBS kits (Cat. No. FC-402-4002), taking advantage of surplus reagents asdemonstrated previously (Tatsumi K, 2015, in press.). 5% PhiX spike-in was added toeach lane as control. Image analysis and base calling were executed using thestandard Illumina software consisting of HiSeq Control Software (HCS) ver. 2.0.12.0
and Real-Time Analysis (RTA) ver. 1.17.21.3. Quality of the raw sequence data were
controlled by FastQC ver. 0.11.1(http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). Removal of adapter
sequences and low-quality reads was performed with Trim Galore ver. 0.3.3 with theparameters ‘-e 0.1 -q 30’(http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Additionally,reads with the average per-base quality score of no more than 30 were removed withan original script. The reads filtered as above were aligned with the genome
sequences GRCm38/mm10 by BWA ver. 0.7.10-r789 using the BWA-MEM algorithm(Li and Durbin, 2010). Potential PCR duplicates among the mapped paired reads
20were removed using the MarkDuplicates function of Picard Tools ver. 1.122(http://picard.sourceforge.net/). The sequencing statistics including the number ofreads, mapping rate and exome on-bait coverages were summarized by the
CalculateHsMetrics function of Picard Tools.
Local realignment of the mapped reads was performed by
RealignerTargetCreator and IndelRealigner of the Genome Analysis Toolkit (GATK)
package ver. 3.2-2 (McKenna et al., 2010), and recalibration based on per-basequality score were performed with the BaseRecalibrator and PrintReads functions.
For these processes, the mm9-based exome region data with 100 bp padding
provided by Agilent Technologies was converted to mm10 using the UCSC LiftOver
program (https://genome.ucsc.edu/util.html) (Rhead et al., 2010), and insertionand deletion mutations (indels) from NCBI dbSNP (build 137) were used as known
sites. The sample-specific indels were called using the HaplotypeCaller in GATK after
applying local realignment and recalibration performed above.
SnpEff package ver. 4.0E (Cingolani et al., 2012) was used to filter andannotate the indels causing high-impact coding variants. We defined as high-impact
mutations both the indels categorized in ‘HIGH’ by the SnpEff definition and thein-frame indels. The indels in both the samples and the control were removed, incomparison with the indels in the control. On- and off-target indels were visualized
and evaluated on the UCSC Integrative Genomics Viewer (IGV) ver. 2.3.36(Thorvaldsdottir et al., 2013).
The exome sequencing data have been deposited and are available in the
DNA Data Bank of Japan (DDBJ). The accession numbers for Tyr KO and Nr3a KO
data are DRA003478 and DRA003920 respectively.

21

CRISPR gRNA Database Development
The first step in automatically detecting gRNA sites was to extract candidate targets.
The process is described here for a single gene, and is repeated for all genes whenrunning the method. Using the genome annotation file and the genomic sequence, we
extracted the sequence of all exons shared by all known isoforms of the gene. This
was necessary to ensure that we targeted a gene, rather than only a few of its
isoforms. We then extracted all the sequences that matched the [G, C, or A]N20GG
pattern and those for which the reverse-complement matched this pattern. This list
represented all the possible candidates for the gene. The next steps were used tofilter this list and to keep only suitable targets. Each target needed to pass all thesteps, so the order did not have any impact on the selection. The steps were
therefore performed in the most computationally efficient order.
The candidate gRNAs needed to target a unique site. Using Bowtie2(Langmead and Salzberg, 2012), we eliminated all the candidates that had multiple
exact matches in the genome (irrespective of location). We also removed targets witha low AT percentage (below 45%), which have a risk of binding strongly to off-target
sites, and targets that contained TTTT, which tend to break the gRNA’s secondary
structure. Because the targets appeared in the forward primer during synthesis, we
also needed to make sure they were not too similar to the reverse primer (See above“Common Reverse oligonucleotide for T7-gRNAs”). This was achieved using the
Needleman-Wunsch algorithm (Needleman and Wunsch, 1970). We then used theVienna RNA-fold package (Lorenz et al., 2011) to compute the gRNA’s secondary
structure. We eliminated all candidates for which the stem loop structure for Cas9
recognition could not fold (Nishimasu et al., 2014), except if the folding energy wasabove -18 (indicating that the ‘wrong’ structure was very unstable). Finally, weevaluated the off-target risk using our own implementation of the Zhang tool (Ran et22

al., 2013). To ensure that all targets stored in the database were as safe as possible,
we used a very strict threshold and rejected candidates with a score below 75.
Candidates that passed all the filtering steps were saved and stored in the
database, which is accessible online at http://crispr.riken.jp.

Statistical Analyses
Statistical analyses were performed by Microsoft Excel or R version 3.1.0(2015).
For sleep phenotype analyses of CRISPR KO mice and conventional
circadian KO mice, normality was evaluated by Kolmogorov-Smirnov test at thesignificance level of 0.05. The homogeneity of variance for each group was evaluatedby Bartlett's test when all the member of the group was normal, or Levene's test whennot. In both tests, significance level was set to 0.05. The sleep phenotype of every KO
strain was compared to the control (C57BL/6N, male, n=108) by Dunnett’s test when
the group has homogenous variance and Dunnett's Modified Tukey-Kramer pairwise
multiple comparison test when not.
The histogram at the top of each sleep/wake parameter chart (Figures 4C, F,
5C, F, and S3M) shows the results of C57BL/6N male mice (n = 108). The error bar is
the SEM for each strain, and the black dashed vertical line and gray shaded area are
the mean and 1 SD range from the recordings of C57BL/6N male mice.
To estimate the sample size for phenotype evaluation, we compared thesleep time control dataset Xc (C57BL/6N, male, n=108) to a simulated dataset Xs
which have the same distribution except a mean of 1 SD (50.8 min) less or much thancontrol (Xs = Xc ± 50.8). We randomly sampled k (2 ≤ k ≤ 20) data from Xs andcompared with Xc by Dunnett’s test with the significance level of 0.05. This test was23repeated 1000 times for one k. We calculated the rate of p < 0.05 as sensitivity foreach k from 2 to 20. According to this calculation, we found that the sample size k ≥ 2provides more than 25% sensitivity whereas the sample size k ≥ 6 provides morethan 50% sensitivity. In this study, we selected the sample size k ≥ 8 to achieve morethan 75% sensitivity for the CRISPR knockout study of NMDA receptor family
members.
For pharmacological studies, means of two sets of data were evaluated. First,
the normality was tested by Shapiro-Wilk test at the significant level of 0.05. When thenormality is confirmed in both groups, the homogeneity of variance was tested by
F-test at the significance level of 0.05. When two groups were normal distributions
with equal variance, Student's t-test, when the groups are normal distributions without
equal variance, Welch's t-test, otherwise two-sample Wilcoxon test was applied.
For the sleep time evaluation of Nr3a KO mice by EEG/EMG, daily sleep time
was compared between Nr3a KO group (n=2) and wild type group (n=6). The
normality for wild type group was tested by Shapiro-Wilk test at the significance levelof 0.05 and turned out to be normal (p = 0.9195). Since there were only two micetested in the Nr3a KO group, they were estimated to be a normal distribution becauseof normality confirmed in other EEG recording done in this study (the Shapiro-Wilk
test for wild-type, DZP-control group, DZP-treated group, MAP-control group and
MAP-treated group were 0.6609, 0.1003, 0.4282, 0.4709 and 0.3382, respectively).
F-test at the significance level of 0.05 showed these groups do not have different
variance (p = 0.3014). Therefore, the mean sleep time was tested by Student's t-test(p = 0.0201, Figure S6E).
The i.p. injection was performed blindly without informing the injector thecontent of the drug. Randomization was not performed for processing data.
24

In this study, p < 0.05 was considered as significant (*p < 0.05, **p < 0.01,***p < 0.001 and n.s. for not significant evaluations).

25

SUPPLEMENTAL DATA
Figure S1. A triple-target CRISPR method for highly efficient production ofwhole-body biallelic KO mice, Related to Figure 1.
(A) Computational simulation predicting how efficiently one or more alleles are cut out
when different numbers of gRNAs are used for the same target gene. Knockout
efficiency was calculated for an increasing number of target alleles: single genemono-allelic (one allele), single gene biallelic (two alleles), double gene biallelic (fouralleles), and triple gene biallelic KO (six alleles). In each simulation, we comparedsingle- (one target per gene), dual- (two targets), triple- (three targets), sextuple- (sixtargets), and decuple- (ten targets) target methods. Each method uses the sameamount of total gRNA. A computational model of the different methods (except thesingle-target method) predicts the minimum efficiency.
(B) Distribution of the gRNA dissociation constant (K) used in the computational
simulation of CRISPR-based KO (Figure S1A). Distribution of the gRNA dissociation
constant (K) follows the log-normal distribution with its geometric mean and geometricstandard deviation as 1.0 and 2.5, respectively. Without loss of generality, we can use
1.0 for the geometric mean in the log-normal distribution of the gRNA dissociation
constant. We then estimated three gRNA dissociation constants from the SSA assay
for three gRNAs for Tyr gene (K1 = 2.32203, K2 = 1.16562 and K3 = 0.369466, Figure1D). Since the geometric standard deviation of these values was 2.53097, we used
2.5 for the geometric standard deviation in the log-normal distribution of the gRNA
dissociation constant.
(C) Computer simulation predicting the minimum efficiency (mean ± SD) of
single-gene biallelic KO with different recovery rates of mutations (α = 0.05, 0.10 and26

0.15) and total gRNA concentrations (S = 5, 10 and 15). We note that total gRNA
concentration (S) is a relative value to the geometric mean of gRNA dissociation
constants (1.0).
(D) Sequences of the inserted fragment for the SSA assay of gRNAs for the Tyr gene.
The fragment sequences for the SSA vector are shown with three target sequences(blue, orange, and green) for Tyr-1, Tyr-2 and Tyr-3 gRNAs, respectively. Mousegenomic sequence data were obtained from GRCm38/mm10 via the UCSC Genome
Browser (http://genome.ucsc.edu/) (Rhead et al., 2010).
(E) Schematic diagram of the single-strand annealing (SSA) assay. The SSA-reporter
vector contained 5’ and 3’ Luciferase gene fragments that shared 702 bp of direct
repeats. These fragments were separated by stop codon and gRNA target site. A
gRNA/Cas9-mediated double-strand break at the target site induced an SSA reaction
between the homologous regions, producing an active Luciferase gene.
(F) Computational simulation with estimated values for a recovery rate of mutation (α= 0.136) and total gRNA concentration (S = 5.72) predicting how efficiently two allelesof a gene are cut out when single or triple gRNAs are used for the same target gene.
We first constructed three single-target CRISPR models with three different
dissociation constants (K1 = 2.32203, K2 = 1.16562 and K3 = 0.369466) estimatedfrom the SSA assay results for Tyr-1, Tyr-2, and Tyr-3, respectively. We note that
estimated values of dissociation constants (K1, K2 and K3) are inverse proportional to
the DNA-cleavage efficiency in SSA assay, and geometric mean of dissociation
constants is set to be 1.0 without loss of generality. The single-target CRISPR
efficiencies of the experiments are shown in blue, orange and green solid circles for
Tyr-1 (36.0%), Tyr-2 (54.2%) and Tyr-3 (64.7%), respectively. We then compared thesingle-target CRISPR models and experiments to estimate the values for a recovery
27

rate of mutation (α = 0.136), total gRNA concentration (S = 5.72) by least squares
method. The triple-target CRISPR efficiencies of the experiment (97.5%) andsimulation (82.6%) are shown in solid and open circles (red), respectively.
(G) Coat color of the Tyr KO mice generated with three different single-target gRNAs.
See also Figure 1E.
(H) The genotyping of Tyr KO mice. The relative amount of intact DNA for each target
sequence was measured by quantitative PCR (qPCR). The genomic DNA was
purified from the brain, scalp and tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in one wild-type mouse
was defined as 100%.
(I) An MS analysis of Tyr protein in mice skin. Mass spectrometric signals obtained bymonitoring a tryptic peptide of Tyr protein (upper) in Nr3a (set 2) #10 (Figure S6B),
Tyr #3 and #4 (Figure S4D) in the presence of isotopically labeled internal standard(lower) are shown. The peptide was absolutely quantified using a triple quadruple
mass spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific). The
peptide concentration, which represents the Tyr protein concentration, wasdetermined to be 1.7 atmol/µg in Nr3a (set 2) #10, whereas the peak was notobserved in Tyr #3 and #4.

28

Figure S2. The Snappy Sleep Stager (SSS) enables non-invasive, fully
automated, high-performance sleep/wake phenotyping, Related to Figure 3.
(A) Representative wave patterns of EEG, EMG, and the pressure change in the SSS
chamber, reflecting the animal’s respiration flow, during NREM sleep, REM sleep,
and wake states in the mouse. During NREM sleep, the respiration rate was slow andits period was stable, whereas during waking, the respiration rate was faster and its
period and amplitude fluctuated more.
(B) Schematic diagram of the SSS annotation pipeline. After the mouse is set in thechamber and the respiration flow recording started, no subjective decision isnecessary until the sleep staging finishes. The recorded respiration flow is sampled at
250 Hz, then digitized, and stored in a computer. The recorded data are divided into
8-second epochs. Each epoch is transformed to a power spectrum, and its characters
extracted by principal component analysis. The top three principal components areused as characters, and time-series of the characters are automatically clustered byprobability density clustering. Finally, the clusters are annotated as sleep or awake
based on their characteristics. See Supplemental Experimental Procedures fordetail.
(C) Sleep/wake phenotyping using simultaneously recorded EEG/EMG andrespiration. Three animals were used to estimate the SSS performance. Top row
shows the principal components from the SSS analysis. Bottom row shows the timecourse of sleep time (per hour) determined by both EEG/EMG based staging (blackline) and SSS results (red line). Note that even in different individuals, the first andsecond principal components clearly formed two clusters. Choosing which cluster
was awake state and which was sleep state was simpler than judging all of theepoch’s states in conventional analyses.
29

(D) Accuracy of the SSS for different levels of wakefulness. Each awake epoch wascategorized as “quiet awake”, “normal awake”, or “active awake”, which were defined
as 0 to 33.3%, 33.3 to 66.6%, and 66.6 to 100% EMG power, respectively. Upper
panel shows representative “quiet awake” and “active awake” recordings. Lower
panel shows the accuracy of the SSS results for each level of wakefulness. Note thateven in “quiet awake” state the accuracy exceeded 90%.
(E) Distribution of sleep/wake parameters recorded in C57BL/6J male and female
mice and C57BL/6N female mice. The upper, middle and the lower rows show thedata of C57BL/6J male (n = 178), C57BL/6N female (n = 72) and C57BL/6N male (n =173) mice, respectively. The C57BL/6J male mice slept 699.3 ± 49.6 min (mean ± SD,
n = 178, 7.1% CV), which is 35 minutes less than the daily sleep time of C57BL/6N
mice. In females, C57BL/6N and C57BL/6J showed 636.4 ± 57.7 min and 613.2 ±50.1 min, respectively. They had shorter sleep time than males, which presumably isan effect of the estrus cycle. The pink and sky blue dashed lines show the mean andthe mean ± SD. See Table S1.

30

Figure S3. SSS can detect various sleep/wake phenotypes, Related to Figure 4.
(A) SSS detected the sleep-time reduction induced by a single i.p. administration of
methamphetamine (MAP) at ZT2. Heatmaps show the sleep time (per hour) for all
mice used in this experiment. The MAP group (n = 6) received 3 mg/kg of MAP i.p. on
ZT2 of the second day, and the control group (n = 6) received vehicle i.p. at the sametime. The MAP group showed less sleep than the control group for the first 3 hr after
i.p. injection. Red bar at the top of the plot shows the period used for comparing theparameters in Figure S3B.
(B) MAP reduced the sleep time and Pws significantly for the following 3 hr (ZT2 to 4)
after the i.p. injection of MAP. (Left) Average sleep time. (Middle and right) Average
Pws and Psw. Error bars represent SEM (n = 6 for each group). Welch’s two sample
t-test.
(C) A single i.p. administration of MAP reduced sleep. Plot shows the average sleep
time for the MAP and control groups over time. Red bar at top indicates the 3-hr
period used for comparing the groups in Figure S3B.
(D) SSS detects the sleep-time increase caused by a single i.p. injection of diazepam(DZP) at ZT13. Heatmaps show the sleep time (per hour) of six mice from the DZP
group and the control group. The DZP group received 5 mg/kg of DZP i.p. on ZT13 of
the second day, while the control group received vehicle i.p. at the same time. The
DZP group showed increased sleep time for the first 5 hr after i.p. injection. Red barat the top of the plot shows the period used for comparing the parameters in Figure
S3E.
(E) DZP increased the sleep time and Pws significantly during the 5 hr (ZT13 to 17)
after i.p. administration. (Left) Total sleep time. (Middle and right) Average Pws and31

Psw. Error bars represent SEM (n = 6 for each group). Student's t-test or two-sample
Wilcoxon test.
(F) A single i.p. administration of DZP increased sleep. Plot shows the average sleep
time for the DZP and control groups. Red bar at top indicates the 5-hr period used forcomparing the groups in Figure S3E.
(G) After DZP increased the sleep time significantly during the first 5 hr (ZT13 to 17),
DZP conversely decreased the sleep time during the second 5 hr (ZT18 to 22) after
i.p. administration. (Left) Total sleep time (p < 0.05). (Middle and right) average Pws
and Psw. Error bars represent SEM (n = 6 for each group). Student's t-test, Welch's
t-test or two-sample Wilcoxon test.
(H) Performance of SSS during MAP induced sleep decrease. When 3 mg/kg of MAP
i.p. was given on ZT2, the accuracy, sleep sensitivity, sleep specificity, wakesensitivity and wake specificity against EEG derived sleep status were over 90%.
Error bars represent SEM (n = 4 for each group). The light and dark blue bars show
the saline and MAP administered group respectively. The dashed line denotes 90%.
(I) Performance of SSS during DZP induced sleep increase. When 5 mg/kg of DZP i.p.
was given on ZT13, the accuracy, sleep sensitivity, sleep specificity, wake sensitivity
and wake specificity against EEG derived sleep status were over 90%. Error bars
represent SEM (n = 4 for each group). The light and dark blue bars show the saline
and DZP administered group respectively. The dashed line denotes 90%.
(J) Sleep time of circadian-clock mutants evaluated by SSS. Altered sleep/wakephenotypes were detected in circadian-clock mutants. Heatmaps show the sleep time(per hour) for a week under light/dark conditions for C57BL/6N (n = 108), Bmal1 KO

32

mice (n = 34), Cry1/Cry2 double-KO mice (n = 27) and Per1/Per2 double-KO mice (n= 26), respectively.
(K) Sleep time (per hour) over 24 hr averaged over six days in circadian-clock
mutants. Red lines show the mean sleep time at each time of day for the mutants.
Gray line shows the data for C57BL/6N male mice (n = 108). The shaded area aroundthe line is the SEM for each time point.
(L) Averaged Pws and Psw over the course of one day in circadian-clock mutants. Redlines represent the mean transition probabilities at each time of day for the mutant
strains. Gray line shows the results for C57BL/6N male mice (n = 108).
(M) Distributions of sleep/wake parameters for circadian-clock mutants. Dunnett’s testor Dunnett's modified Tukey-Kramer pairwise multiple comparison test compared to
C57BL/6N male mice. See also Table S1.

33

Figure S4. Combined use of a triple-target CRISPR and SSS enables
sleep/wake phenotyping in single- and double-knockout mice, Related to
Figure 4.
(A-C) Target sequences of the gRNAs for knocking out clock genes. Bmal1 (A), Cry1
(B, left), Cry2 (B, right), Per1 (C, left), and Per2 (C, right); each gene had three target
sequences. Mouse genomic sequence data were obtained from GRCm38/mm10 via
the UCSC Genome Browser (http://genome.ucsc.edu/) (Rhead et al., 2010). The
colored letters (blue, orange, and green) show the 20-base target sequences. The
target sequences were designed on the sense (+) or the antisense (-) strand of
genomic DNA.
(D-G) The genotype of Tyr KO and clock KO mice. Tyr (D), Bmal1 (E), Cry1/Cry2 (f)and Per1/Per2 (G); each gene had three targets. The relative amount of intact DNA
for each target sequence was measured by quantitative PCR (qPCR). The genomic
DNA was purified from the tail of each mouse. The relative amount of intact DNA foreach target sequence was scaled so that the level in a wild-type mouse was definedas 100%. The numbers and wt below the bar plot denotes the mouse id and thewild-type mouse, respectively. Genotyping by sequencing was done for three Tyr KO
mouse (D) and Per2 gene of one Per1/Per2 KO mice (G). The sequences of mutant
alleles are shown. The target sites are colored and underlined. The mutations arelabeled in red. Red texts show the description of the sequence with the number ofdetected allele in parentheses.
(H) Average time course of the Pws and Psw for Tyr KO and circadian-clock mutants.
Red lines and red shaded area represent the mean and SEM of the transition
probabilities for each time of day for the mutants. Gray line shows the results of
C57BL/6N male mice (n = 108).
34

(I) Sleep time (per hour) phenotype under constant darkness for individual mice incircadian mutants produced by triple-target CRISPR. Sleep-time phenotype over six
days is shown for the three circadian-clock mutant mice, Bmal1 KO mice (n = 6),
Cry1/Cry2 (n = 5) and Per1/Per2 KO mice (n = 2).
(J) Sleep time (per hour) under constant darkness over 24 hr averaged over six daysin circadian mutants produced by triple-target CRISPR. Red lines and red shaded
area represent the mean and SEM of sleep time at each time of day for each strain.
The light gray and black bar written at the bottom of the panel denotes the subjective
time of the animal.
(K) Target sequences of the gRNAs for knocking out Hcrt gene. Mouse genomic
sequence data were obtained from GRCm38/mm10 via the UCSC Genome Browser(http://genome.ucsc.edu/) (Rhead et al., 2010). The colored letters (blue, orange,and green) show the 20-base target sequences. The target sequences were
designed on the sense (+) or the antisense (-) strand of genomic DNA.
(L) The genotype of Hcrt KO mice. The relative amount of intact DNA for each target
sequence was measured by qPCR. The genomic DNA was purified from the tail ofeach mouse. The relative amount of intact DNA for each target sequence was scaled
so that the level in a wild-type mouse was defined as 100%. The numbers and wt
below the bar plot denotes the mouse id and the wild-type mouse, respectively.
(M) Average time course of the Pws and Psw for Hcrt KO mice. Red lines and red
shaded area represent the mean and SEM of the transition probabilities for each timeof day for the mutants. Gray line shows the results of C57BL/6N male mice (n = 108).

35

Figure S5. Reverse genetics without crossing revealed Nr3a mutant to be ashort-sleeper, Related to Figure 5.
(A-G) Target sequences of the gRNAs for the knockout of NMDA receptor family. Nr1(A), Nr2a (B), Nr2b (C), Nr2c (D), Nr2d (E), Nr3a (F), and Nr3b (G); each had threetarget sequences. The mouse genomic sequence data were obtained from
GRCm38/mm10 via the UCSC Genome Browser (http://genome.ucsc.edu/) (Rhead
et al., 2010). The colored letters (blue, orange, and green) show the 20-base target
sequences. The target sequences were designed on the sense or antisense strand of
genomic DNA.
(H-L) The genotype of NMDA receptor family KO mice. Nr2a (H), Nr2c (I), Nr2d (J),
Nr3a (set 1) (K), and Nr3b (L); each had three targets. The relative amount of intact
DNA for each target sequence was measured by quantitative PCR (qPCR). The
genomic DNA was purified from the tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in a wild-type mouse wasdefined as 100%. The numbers and wt below the bar plot denotes the mouse id andthe wild-type mouse, respectively. Genotyping by sequencing was done for one Nr3a
KO mouse (K) and one Nr3b KO mice (L). The sequences of mutant alleles areshown. The target sites are colored and underlined. The mutations are labeled in red.
Red texts show the description of the sequence with the number of detected allele inparentheses.
(M) Average time course of the Pws and Psw of the NMDA receptor family systematic
KOs for one day. Red lines and red shaded area in each chart represent the mean
and SEM of the Pws or Psw for each time of day for the mutants. Gray line shows theresults of C57BL/6N male mice (n = 108).

36

Figure S6. Nr3a mutant is a short-sleeper in multiple KO strains and inconventional sleep analysis, Related to Figure 5.
(A) The second set of target sequences of the gRNAs for knocking out Nr3a gene.
Note that these sequences are distinct from set 1 sequences (Figure S5F). Mousegenomic sequence data were obtained from GRCm38/mm10 via the UCSC Genome
Browser (http://genome.ucsc.edu/) (Rhead et al., 2010). The colored letters (blue,
orange, and green) show the 20-base target sequences. The target sequences were
designed on the sense (+) or the antisense (-) strand of genomic DNA.
(B) The genotype of Nr3a (set 2) KO mice. The relative amount of intact DNA for eachtarget sequence was measured by quantitative PCR (qPCR). The genomic DNA was
purified from the tail of each mouse. The relative amount of intact DNA for each target
sequence was scaled so that the level in a wild-type mouse was defined as 100%.
The numbers and wt below the bar plot denotes the mouse id and the wild-type
mouse, respectively.
(C) Average time course of the Pws and Psw for Nr3a KO mice (set 2). Red lines andred shaded area represent the mean and SEM of the transition probabilities for eachtime of day for the mutants. Gray line shows the results of C57BL/6N male mice (n =108).
(D) The genotype of Nr3a (set 1) KO mice used for EEG/EMG analysis. The relative
amount of intact DNA for each target sequence was measured by qPCR. The
genomic DNA was purified from the tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in a wild-type mouse wasdefined as 100%. The numbers and wt below the bar plot denotes the mouse id andthe wild-type mouse, respectively.

37

(E) The sleep time of Nr3a (set 1) KO evaluated by EEG/EMG based sleep staging.
Nr3a KO group (n = 2) showed a significant short-sleeper phenotype (p = 0.02005,
Student's t-test) than the wild-type group (n = 6). Error bars represent SEM.
(F) Mass spectrometric signals obtained by monitoring a tryptic peptide of Nr3a
protein (upper) in each strain in the presence of isotopically labeled internal standard(lower) are shown. The peptide was absolutely quantified using a triple quadruple
mass spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific). The
peptide concentrations, which represents the Nr3a protein concentrations, were
determined to be 2.2 atmol/µg in Tyr KO #3 and 5.7 atmol/µg in Tyr KO #4, whereas
the peak was not observed in any Nr3a KO mice.

38

Figure S7. Exome analysis of whole-body biallelic Nr3a (set 2) KO mice
produced by triple-target CRISPR method, Related to Figure 6.
(A) The genomic regions targeted by the three gRNAs were shown for two mice in
Nr3a (set 2) KO group (Figure S6B). The upper part of each panel shows readcoverage, and the lower part shows read alignments (rectangles). Horizontal lines
between rectangles indicate read pairing. Different types of mutations occurring at thetarget sites are highlighted as follows: inter-exon deletion (red rectangles); intra-exon
deletion mutation (green rectangles); short deletion (red dots); and short insertion(blue dots).
(B) Enlarged View of Read Coverage of Exome Sequence of Nr3a KO. Diminisheddepth shows a deletion mutation with the most severely affected site pointed by a redarrow. A blue vertical bar indicates an insertion mutation whose proportion is shownby its length. Percentages in parentheses are the proportions of indel mutations thatcan cause loss of gene function.

39

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Cingolani, P., Platts, A., Wang le, L., Coon, M., Nguyen, T., Wang, L., Land, S.J., Lu,
X., and Ruden, D.M. (2012). A program for annotating and predicting the effects ofsingle nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophilamelanogaster strain w1118; iso-2; iso-3. Fly 6, 80-92.
Cong, L., Ran, F.A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P.D., Wu, X., Jiang,W., Marraffini, L.a., et al. (2013). Multiplex genome engineering using CRISPR/Cas
systems. Science (New York, NY) 339, 819-823.
Diniz Behn, C.G., and Booth, V. (2011). Modeling the temporal architecture of rat
sleep-wake behavior. Conference proceedings : Annual International Conference of
the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine
and Biology Society Conference 2011, 4713-4716.
Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2.
Nature methods 9, 357-359.
Li, H., and Durbin, R. (2010). Fast and accurate long-read alignment with
Burrows-Wheeler transform. Bioinformatics 26, 589-595.
Lorenz, R., Bernhart, S.H., Honer Zu Siederdissen, C., Tafer, H., Flamm, C., Stadler,
P.F., and Hofacker, I.L. (2011). ViennaRNA Package 2.0. Algorithms for molecularbiology : AMB 6, 26.
Masuda, T., Tomita, M., and Ishihama, Y. (2008). Phase transfer surfactant-aided
trypsin digestion for membrane proteome analysis. J Proteome Res 7, 731-740.
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A.,
Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010). The Genome Analysis

40

Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.
Genome research 20, 1297-1303.
Needleman, S.B., and Wunsch, C.D. (1970). A general method applicable to thesearch for similarities in the amino acid sequence of two proteins. Journal ofmolecular biology 48, 443-453.
Nishimasu, H., Ran, F.A., Hsu, P.D., Konermann, S., Shehata, S.I., Dohmae, N.,
Ishitani, R., Zhang, F., and Nureki, O. (2014). Crystal structure of Cas9 in complex
with guide RNA and target DNA. Cell 156, 935-949.
R core team (2015). R: A Language and Environment for Statistical Computing, R.C.
Team, ed. (Vienna, Austria: R Foundation for Statistical Computing).
Rappsilber, J., Mann, M., and Ishihama, Y. (2007). Protocol for micro-purification,enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.
Nature protocols 2, 1896-1906.
Tatsumi K, N.O., Itomi K, Tanegashima C, Kuraku S (2015, in press.). Optimizationand cost-saving in tagmentation-based mate-pair library preparation and sequencing.
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Thorvaldsdottir, H., Robinson, J.T., and Mesirov, J.P. (2013). Integrative Genomics
Viewer (IGV): high-performance genomics data visualization and exploration.
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Tsujino, K., Narumi, R., Masumoto, K.H., Susaki, E.A., Shinohara, Y., Abe, T., Iigo,
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Wisniewski, J.R., Zougman, A., and Mann, M. (2009). Combination of FASP andStageTip-based fractionation allows in-depth analysis of the hippocampal membraneproteome. J Proteome Res 8, 5674-5678.

41

A

B

1000

α = 0.10750Single-gene biallelic KO
Count

Predicted total knockout probability

Single-gene mono-allelic KO
1.0
0.5

# of
targets
10
6
3
2
1

0.0
1.0

Double-gene biallelic KO

Triple-gene biallelic KO

500
250
0

0.1 1.0 10.0
gRNA dissociation constant (K)C

Single-gene biallelic KO efficiency
Total gRNA concentration (S)
5
10
15

0.5
0.05
Recovery rate
0.10
of mutation (α)
0.15

0.0
0.0

2.5

5.0

7.5 10.0

0.0

2.5

5.0

7.5 10.0

0.85 ± 0.11

0.95 ± 0.05

0.97 ± 0.03

0.83 ± 0.11

0.93 ± 0.06

0.96 ± 0.03

0.79 ± 0.12

0.90 ± 0.07

0.93 ± 0.04

Total gRNA concentration (S)D
chr7 (-) 87,490,000-187,460,000

87,430,000

Tyr exon1

Tyr exon5

SSA vector-1

ggcacctatggccaaatgaacaatgggtcaacacccatgtttaatgatatcaacatctacgacctctttgtatggatgcattactatgtgtcaagggacacactgcttgggggctctgaaatatggagggacattgattttgcccatgaagcaccagggtttctgccttggcacagacttttcttgttattgtgggaac

Tyr target sequence-1
SSA vector-2/3
tggaacaagccagtcgtatctggccatggcttcttggggcagcactggtgggagct

gttattgctgcagctctctctgggcttagcagtaggctatgccttcagaagaagaagaagaagaagcaaccccaggaggaaaggcagccactcctcatggacaaagacgactaccacagcttgctgtatcagagccatctgtga

Tyr target sequence-2

E

Tyr target sequence-3

F

Luc (N) 1-1131

STOP

Luc (N) 1-1131

STOP

Target seq

Luc (C) 430-1653

seq

Luc (C) 430-1653

Target

Luc (

Luc (
SV40 promoter

C) 43

N) 1-

1131

0-16

α = 0.13643
Single-gene biallelic KO

Predicted total
knockout probability

Homologous sequence

SV40 promoter

53

1.0

0.975
0.826

Single-target
Tyr-1 Tyr-2 Tyr-3
Triple-target (Tyr-1,2,3)Experiment
Simulation

0.5
S = 5.7248
0.0 2.5 5.0 7.5 10.0
Total gRNA concentration (S)0.0# of targets
3 1

Luc 1-1653

G

Single target (Tyr-2)

Single target (Tyr-1)

H

Single target (Tyr-3)

I
Genome DNA / Tbp(Wild-type =100%)

125

Scalp

Target-1

75

Target-2

50

Target-3

25
Wildtype#1#2

Tyr knockout

Wildtype#1#2

Tyr knockout

Wildtype#1

Tyr #3

Tyr #4

300

100

0

Nr3a (set 2) #10

400

Tail

#2

Tyr knockout

Signal intensity

Brain

200
100
0
40000
30000
20000
10000
0

69

Figure S1 G.A. Sunagawa

70

71

72

69

70
71
Time (min)

72

69

70

71

72

A
NREM Sleep

Voltages (mV)
EMG
EEG

0.5

REM Sleep

Wake

0.0
-0.5
0.5
0.0

0.2
0.0

Non-invasive respiratory recording

C
5
PC2

2

Relative Power (A.U.)
0
80

10

4
Time (sec)Power spectrum analysis

0

0
PC1

Principal component analysis

Mouse #2

Mouse #3
Accuracy 95.6%

10

Sleep Sens. 93.5%
Spec. 97.7%

5

−20
SSS

0
PC1

Wake

−10

20

Sens. 98.3%
Spec. 93.1%−20

EEG/EMG

SSS

60

0
PC1

−5

20

EEG/EMG

30

0

0

D

0

Sens. 96.4%
Spec. 95.4%

20

male (n = 178)

Count

C57BL/6J

20
10
0

0.5

0.2
-0.2

0

C57BL/6J
female (n = 173)

20
10

10

10

0
10

0

0

10

10

0
30

0
20

0

0

30

20

20

20

10

10
0

0
0 500 1000
Sleep time (min)10

10
0.0 0.2 0.4 0.6
Amplitude

0
0.000

0.050
P ws

Figure S2 G.A. Sunagawa

4

8

0

Time (sec)

4

8

90

75
50
25
0

24 48 72 96 120 144
Time (hr)Countfemale (n = 72)

Active
awake

-0.5

10

Count

C57BL/6N

20

Sleep staging

100

EEG/EMG

Sleep
0
PC1

-0.5

E
30

Wake

0

Quiet
awake

0.5

8

e
ak
aw ke
e
tiv waAc al arm ake
No aw
iet
Qu

24 48 72 96 120 144
Time (hr)

SSS

60

30
0

6

−10
−20

20

−30 −20 −10 0 10 20
PC10

24 48 72 96 120 144
Time (hr)

Wake

−10

30
0

0
PC1

0

−5

Sens. 98.1%
Spec. 92.5%

4

10

Sleep Sens. 95.8%
Spec. 95.9%

5

0

Wake

2

Propability density clustering

Accuracy 95.3%
10

0

0

−10
−20

20

Mouse #1

060

8Accuracy 94.9%

−5
−10

6

10

−10
−20

30 60 90
Time (min)Sleep Sens. 93.3%
Spec. 97.6%

10

Sleep time (min)

0

20
15
10
5
0
0

8

PC2

Frequency (Hz)B

6

Accuracy (%)

4

Voltages(mV)
Resp.
(Pa) EMG EEG

2

PC2

-0.2
0

PC2

Resp. (Pa)

-0.5

0
0.000

0.050
P sw

B

D

Sleep time (min)

0

12

24

36

48

60

Time (hr)

60

0

0.00

0

5

0

0.025
0.000

5

0.000

5

0

0

0 5

0

Psw

0.25
0.00

3

0

Bmal1 (n = 34)

Cry1/Cry2 (n = 27)

Per1/Per2 (n = 26)

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Cry1/Cry2 (n = 27) Per1/Per2 (n = 26)

0

0

L

30
0

6

12 18 246

12 18 240

6

12 18 24

P ws

Bmal1 (n = 34)

0.2
0.1
0.0

P sw

60

0.2
0.1
0.0

Time (hr)Bmal1 (n = 34)

06

12 18 24

0

5

Bmal1

**
**

Per1/Per2
600
800
1000
Sleep time (min)

0.2

0.4
0.6
Amplitude

0.8

0.04
Pws

Figure S3 G.A. Sunagawa

0.06

72

Cry1/Cry2 (n = 27)

0

6 12 18 24
Time (hr)

0.03

0.04
Psw

Per1/Per2 (n = 26)

0

**
**
***
0.02

5

60

***
*
***
0.02

60

0 5
0
DZP (mg/kg)

M
Cry1/Cry2

48

Sleep time (min)B6N (n = 108)
Mouse
Sleep time (min)K

36Time (hr)

Sleep
Wake
Wake
Sleep
sensitivity specificity sensitivity specificity

5

JTime (Day)24

Accuracy

0.25
0DZP (mg/kg)
05

SSS statistics against EEG based stages

0.50

3

12

I

0.75

0 3
0
MAP (mg/kg)

72

0

0.50

3

60

20

1.00

0

48

40

0.75

3

36

Time (hr)

60

1.00

0

24

i.p.

5

SSS statistics against EEG based stages

0.00

12

F

0.04

0.00

20

0

0.02

0 5

40

3

0.06

0.050

60

Sleep
Wake
Wake
Sleep
Accuracy
sensitivity specificity sensitivity specificity

0.075

0

0

0.025

50

H

Psw

Pws

0
DZP (mg/kg)

3

0.075

100

0.050

0.02

0.00
0

150

n.s.

0.04

50

3

n.s.

0
DZP (mg/kg)

n.s.

100

0.05

0.00
0

0.06

150

0.02
0.01

0
MAP (mg/kg)

Sleep time (min)

0

30

0.10

E

5

DZP (mg/kg)
Second 5 hours after i.p.
Sleep time (min)

60

i.p.

0

G

48

0.03

60

MAP (mg/kg)
0
3

i.p.

Sleep time (min)36Time (hr)

0.15

Psw

24

0.04

90

Pws

12

First 5 hours after i.p.
Sleep time (min)

0

60

C

n.s.
120

First 3 hours after i.p.
Sleep time (min)Sleep time (min)03

MAP (mg/kg)

i.p.

Pws

A

0.05

6

12 18 24

0

5

A

Bmal1
chr7(+)

113,250,000

113,300,000
Bmal1
Bmal1

gtgtggactgcaatcgcaag (+)
Bmal1 target sequence-1

B

Cry1

chr10(-)

gtgttctgcatattctaacc (-)
Bmal1 target sequence-2

gtcagattgaaaagaggcgt (+)
Bmal1 target sequence-3

85,160,000

85,140,000

85,180,000

Cry292,270,000

chr2(-)

92,260,000

92,250,000
Cry2
Cry2

Cry1

gaaatatggcgttccttccc (+)
Cry1 target sequence-1

C

gtcaaagcgtgggttgtttg (-)
Cry1 target sequence-2

gtcctcctaccaaaacccac (-)
Cry1 target sequence-3

69,1050,000

69,110,000
Per1
Per1

gtcatattcaaaggtcaaac (-)
Cry2 target sequence-1

Per1

gtctctgcatgattgacgat (-)
Cry2 target sequence-3

Per2

chr11(+) 69,100,000

gacatgagtggtcccctaga (+) ggacatgtctacttacaccc (+)
Per1 target sequence-1
Per1 target sequence-2

D

chr1(-)

ggtagcgaatctcattctcg (-)
Per2 target sequence-1

gtctatatttcggagcaggc (+)
Per1 target sequence-3

Target-1
Target-2
Target-3
wt

3

4

5

Tyr #46

7

8

9

Target-1
Target-3

Genome DNA / Tbp(Wild-type =100%)

Target-2Reference: GGTGGGAGCTGTTATTGCTGCAGCTCTCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCC
AGGAGGAAAGGCAGCC
Mouse #4: GGTGGGAGCTGTTATTGCTGCAGCTCCCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAA---------------AGGCAGCC 1 bp mutation, -16 bp (5)GGTGGGAGCTGTTATTGCT-----------------------------ATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCCTAAAGGAGGAAAGGCAGCC -29 bp, +4 bp (10)

Tyr #5

Target-1

Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT
GCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #5: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT
GCTTGGGGGCTCTGAAATATGGAGGGACATTG wild type (3)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACA--CTTGGGGGCTCTGAAATATGGAGGGACATTG -3 bp [TLL→TL](1)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCA---------GA
GCTTGGGGGCTCTGAAATATGGAGGGACATTG -11 bp+2 bp [VSRDTLLG→VSELG] (8)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGA------(+60base)GCTTGGGGGCTCTGAAATATGGAGGGACATTG -6 bp+60 bp [including stop codon] (3)Target-2

Target-3

Genome DNA / Tbp(Wild-type =100%)

Target-1
Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT GCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #4: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT GCTTGGGGGCTCTGAAATATGGAGGGACATTG wild type (3)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACTTGCTTGGGGGCTCTGAAATATGGAGGGACATTG +1 bp (3)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGA------ ---------CTCTGAAATATGGAGGGACATTG -15 bp [∆ TLLGG] (7)Target-2

Target-3

Reference: GGTGGGAGCTGTTATTGCTGCAGCTCT CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCC
AGGAGGAAAGGCAGCCACTCCTCATGG
Mouse #6: GGTGGGAGCTGTTATTGCTGCAGCTCTTCTCTGGGCTTAGCAGTAGGCTACGCCTTCAGAAGAAGAAGAAGAAG--------------------CAACCACTCCTCATGG +1 bp, -24 bp+3 bp (4)
GGTGGGAGCTGTTATTGCTGCAGCTCTTCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAG-----------------------CAACCACTCCTCATGG +1 bp, -27 bp+3 bp(1)
GGTGGGAGCTGTTATTGCT-------- ---------------------ATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCCTAAAGGAGGAAAGGCAGCCACTCCTCATGG -29 bp, +4 bp (1)
GGTGGGAGCTGTTATTGCTGCAG---- --------------------------------------------------------GAAAGGCAGGAAAGGCAGCCACTCCTCATGG -63 bp+7 bp (2)
GGTGGGAGCTGTTATTGCTGCAGCTCT C------------------------------------------------------AGGAGGAAAGGCAGCCACTCCTCATGG -55 bp (6)

P ws

0.2
0.1
0.0

P sw

H
0.2
0.1
0.0

0

wt

1

2

120
100
80
60
40
20
0

3

4

5

wt

1

2

3

4

5

wt

1

5

Target-1-6 bp (5)
+1 bp (4)Target-2

wt

1

2

wt

1

Reference: GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCCCG TTGCGGGTGCGGAATCGAATGGGAGAATAATC
Mouse #2: GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCC-- ---CGGGTGCGGAATCGAATGGGAGAATAATC -5 bp (2)
GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCCCGTTTGCGGGTGCGGAATCGAATGGGAGAATAATC +1 bp (6)Per1/Per2 (n = 2)

6

0

0

0

6

12 18 24

-5 bp (15)Target-3

2

Cry1/Cry2 (n = 5)

12 18 24

4

Reference: GGAGCTGCACGAGCACAGGCGTCTCGATCAGATCCTGAGGTAGATAGCCCAGGAGGGGGACCGCCCTGCGAGGCAAGGG
Mouse #2: GGAGCTGCACGAGCACAGGCGTCTCGATCAGATCCTGAGGTAGAT-----AGGAGGGGGACCGCCCTGCGAGGCAAGGG

Bmal1 (n = 6)63

Reference: TTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATCTCATTC TCGTGGTGTTTCCCAACACTGTGGAGAGAG
Mouse #2: TTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATC------ TCGTGGTGTTTCCCAACACTGTGGAGAGAGTTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATCTCATTCATCGTGGTGTTTCCCAACACTGTGGAGAGAG

Tyr (n = 7)

12 18 24

2

Per2 #2

Per2

Per1

6

Cry2

Cry1

G

Reference: GGTGGGAGCTGTTATTGCTGCAGCTCT CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCAGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGAMouse #5: GGTGGGAGCTGTTATTGCTGCAGCTCTACTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCC-----------------ACTCCTCATGGACAAAGACGA +1 bp, -17 bp (6)
GGTGGGAGCTGTTATTGCTGCAGCTCT --------------------------------------------------------AGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGA -56 bp (4)
GGTGGGAGCTGTTATTGCTGC--TAAG CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGA---------AGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGA -6 bp+4 bp, -9 bp (8)Tyr #6

120
100
80
60
40
20
0

gtgtgatgtactccccgttg (-)
Per2 target sequence-3

Bmal1

120
100
80
60
40
20
0

F

Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACAC TGCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #2: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT TGCTTGGGGGCTCTGAAATATGGAGGGACATTG +1 bp (5)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACTGTGCTTGGGGGCTCTGAAATATGGAGGGACATTG +2 bp (9)91,420,000

gatcctgaggtagatagccc (-)
Per2 target sequence-2

E

Tyr

91,435,000

91,450,000

Per2Genome DNA / Tbp(Wild-type =100%)

120
100
80
60
40
20
0

gcgagaattcttctacacag (+)
Cry2 target sequence-2

6

12 18 24

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7Time (Day)K Hcrtchr11(-)

J60

0100,762,500

100,762,000
Hcrt

catgaactttccttctacaa (+)
Hcrt target sequence-1

caacagttcgtagagacggc (-)
Hcrt target sequence-2

aaagcggtggtagttacggt (-)
Hcrt target sequence-3

0

0

6

12 18 24

1

Per1/Per2 (n = 2)

0

0

2

Figure S4 G.A. Sunagawa

3

4

6

12 18 24

Time (Hour)

M

Hcrt

wt

Cry1/Cry2 (n = 5)

30

L
120
100
80
60
40
20
0

Bmal1 (n = 6)

60

5

6

7

P ws

Per1/Per2 (n = 2)

0.2
0.1
0.0

P sw

Cry1/Cry2 (n = 5)

Mouse

Bmal1 (n = 6)

Sleep time (min)I

Sleep time (min)Time (Hour)

0.2
0.1
0.0

6

12 18 24

Hcrt (n = 7)06

12 18 24

Time (Hour)A Nr1chr2(-)

25,305,000

25,315,000

B Nr2a25,295,000

chr16(-)

Nr1
Nr1
Nr1os
Nr1
gaccccaagattgtcaacat (+) gtgggagtgaagtggtcgtt (-)
Nr1 target sequence-1
Nr1 target sequence-2

C Nr2bchr6(-)

9,600,000
Nr2agtgaagtggtcgttgggagt (-)
Nr1 target sequence-3

gaaattctcctacattcccg (+)
Nr2a target sequence-1

ggtcatgctgaagatcatgc (+)
Nr2a target sequence-2

D Nr2c135,800,000

136,100,000

9,800,000

9,950,000

chr11(-) 115,265,000

115,260,000

gatcttgacaaacttccgac (-)
Nr2a target sequence-3

115,255,000
Nr2c
Nr2c

Nr2b

ggccaacaccaaccagaact (-) gatggaagtcatctttctcg (-)
Nr2b target sequence-1 Nr2b target sequence-2

E Nr2dchr7(-) 45,865,000

gttcacgaagtcctggtagc (-)
Nr2b target sequence-3

45,850,000

gcatcctcacccaaatctgt (+)
Nr2c target sequence-1

F Nr3a (set 1)
chr4(-)

45,835,000

gttctcctacgacttgtacc (+)
Nr2c target sequence-2

gtggaatggtatgatcggtg (+)
Nr2c target sequence-3

49,750,000

49,800,000

49,700,000
Nr3a
Nr3a
Pp3r2

Nr2d

Nr3b

chr10(+)

79,974,000

79,972,000H

79,977,000
Nr3bgctacggctattgcagttcc (+)
Nr3b target sequence-1

gagattcatgaggaagcgtc (-)
Nr3b target sequence-3

2

160
140
120
100
80
60
40
20
0

3

4

5

6

7

8

9

Genome DNA / Tbp(Wild-type =100%)

1

10 11 12 13 14 15

L

Nr3a (set 1)

wt

1

2

Nr3a (set 1) #6

3

4

5

6

7

8

gatagactcgaggtggaact (-) gctaaacaggctatccagta (-)
Nr3a target sequence-2 Nr3a target sequence-3

Nr2a
Target-1
Target-2
Target-3
wt

120
100
80
60
40
20
0

1

2

3

4

5

6

7

8

Nr2d

wt

200
180
160
140
120
100
80
60
40
20
0

1

2

3

4

5

6

7

8

9

10 11 12 13

Nr3b

wt

1

2

3

4

5

6

7

8

9

10 11

Target-1

Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #6: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (3)CCTACAACCTGTCTTTGGAAGTAGTGATGG--- -------------------CCTGCCACTTATGCCTT -22 bp (4)Reference: CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAGTTCCTGGCAGCTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
Mouse #7: CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAG----------CTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGC----------------CACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAG-TCCTGGCAGCTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG

Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGA ACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #6: TAGCAGTGATGTTGATGATAGACTCGAGGTGGACACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG +1 bp (4)TAGCAGTGATGTTGATGATAGACTCGAGG---- ACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG -4 bp (3)Target-2

Target-3

0.2
0.1
0.0
0.2
0.1
0.0

Target-3+1 bp mutation (5)-5 bp (2)Nr2a (n = 8)0

6

-10 bp (3)-16 bp (4)-1 bp (3)Reference: ATGACTCGGCCAGGCTGGATGCGCTCTTCACTGCATTGGAGACTACCGGACGCTTCCTCATGAATCTCTGGGCAATCTTCTGCCTG
Mouse #7: ATGACTCGGCCAGGCTGGATGCGCTCTTCACT------------(Δ547base)------------TCCTCATGAATCTCTGGGCAATCTTCTGCCTG -547 bp (16)≈Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #6: TACTGCTATGTAGGCTGCTAAACAGGCTATCCTGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGATACTGCTATGTAGGCTGCTAAACAGGC-----AGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA

M

Target-1

Nr3b #7

Target-2

P ws

wt

120
100
80
60
40
20
0

J

Nr2c

P sw

K

120
100
80
60
40
20
0

Genome DNA / Tbp(Wild-type =100%)

Genome DNA / Tbp(Wild-type =100%)I

ggatgcgctcttcactgcat (+)
Nr3b target sequence-2

Genome DNA / Tbp(Wild-type =100%)G

ggaagtagtgatggccatcg (+)
Nr3a target sequence-1

gactactccttcaatgagga(+) ggcgtctggaatggcatgat(+)
Nr2d target sequence-2 Nr2d target sequence-3

Genome DNA / Tbp(Wild-type =100%)

ggatctgcgcactgacacta(-)
Nr2d target sequence-1

Nr2c (n = 15)

12 18 24 0

6

12 18 24 0

Nr2d (n = 13) Nr3a (set 1) (n = 8) Nr3b (n = 11)6

12 18 24 0

6Time (Hour)

Figure S5 G.A. Sunagawa

12 18 24 0

6

12 18 24

A
Nr3a (set 2)
49,800,000

gaaaacctgaaccgtgtgga (+)
Nr3a target sequence-4

Nr3a (set 1)

ggccacatgaaggctccaat (-)
Nr3a target sequence-6

C

Nr3a (set 2)

120
100
80
60
40
20
0

Target-1
Target-2
Target-3
wt

1

Nr3a (set 1) #9
120
100
80
60
40
20
0

gactaatgactcttccatac (+)
Nr3a target sequence-5

2

3

4

5

6

7

8

9

0.2
0.1
0.0

Nr3a (set 2)(n = 12)0

6

12 18 24

Time (Hour)E

Nr3a (set 1) #10

Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #9: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (16)Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #10: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (14)
CCTACAACCTGTCTTTGGAAGTAGTGAT----- --------------GGCGACCTGCCACTTATGCCTT -19 bp (2)Target-2Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #10: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGGTAGCAGTGATGTTGATGA --------------AACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG

wild type (16)Target-3

wild type (6)-14 bp (10)Target-3

Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #9: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA

EEG based sleep timecomparison

Target-1

Target-2

9 10

0.2
0.1
0.0

10 11 12Target-1

Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #9: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG

wt

Nr3a
Nr3a
Pp3r2

Sleep time (min)D

49,700,000

P ws

Genome DNA / Tbp(Wild-type =100%)

B

49,750,000

P sw

chr4(-)

Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCA GTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #10: TACTGCTATGTAGGCTGCTAAACAGGCTATCCA GTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA wild type (7)TACTGCTATGTAGGCTGCTAAACAGGCTATCCAAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA +1 bp (8)wild type (15)

800
600
400
200
0
WT

F

Tyr

Nr3a (set 1)

3

4

1

1500

1500

1000

1000

500

500

Signal intensity

Signal intensity

#

0
30000
67.46

20000

67.50

3

67.28

67.09

4

5

67.62

66.21

68

66

6

7

8

0
30000

67.20

66.87

20000

67.19
66.31

10000

10000
0

2

66

68

66

Time (min)

0

68

66

68 66

68

66

68

Time (min)

66

68

66

66

68

Nr3a (set 2)
#

1

2

3

67.40

66.65

66.42

4

5

6

7

8

9

66.31

67.22

10

11

12

1500

Signal intensity

1000
500
0
30000

66.06

66.56

20000

67.35

67.46

66.24

66.50
65.50

10000
0

66

68

66

68

66

66

66

68

66

66

66

Time (min)Figure S6 G.A. Sunagawa

68 66

68

66

68

64

66

66

68

Nr3a

A

Nr3a exon1
Chr4 (-)

Nr3a exon3
49,844 kb

49,846 kb

49,773 kb

49,842 kb

Target-4

49,771 kb

49,769 kb

49,767 kb

Target-5 Target-6

Wild-typeNr3a (set 2) #1

Intra-exon deletion
and inversion

Short insertion

Nr3a (set 2) #7

Intra-exon deletion
and inversion

Inter-exon deletion and inversion

long deletion

Short deletion

Short deletion
Short deletion
Short insertion

B

Nr3a exon1

Chr4 (-)

Nr3a exon2

49,844,600 bp

49,844,650 bp

49,792,850 bp

Target-1

Nr3a exon3
49,771,100 bp

49,792,800 bp

Target-2

49,771,050 bp

Target-3

Wild-type
350

400

200

Nr3a (set 1) #6
Coverage: 247 (97.6% affected)

400

200

Coverage: 129 (99.2% affected)

Deletion: 136

Deletion: 71

Insertion: 105

Insertion: 59

300

Coverage: 146 (58.9% affected)
Deletion: 86

Nr3a (set 1) #8
Coverage: 200 (97.0% affected)

300

80

Coverage: 46 (95.7% affected)

300

Deletion: 194

Coverage: 153 (100.0% affected)
Deletion: 153

Insertion: 44

Nr3a exon1
Chr4 (-)

49,844,700 bp

Nr3a exon3
49,771,150 bp

49,844,650 bp

Target-4

Nr3a exon3

49,771,100 bp

49,770,800 bp

Target-5

49,770,750 bp

Target-6

Wild-type
350

350

350

Nr3a (set 2) #1
300

Coverage: 187 (20.1% affected)

200

Coverage: 100 (100.0% affected)

200

Coverage: 64 (95.3% affected)

Deletion: 20
Deletion: 100

Deletion: 61

Insertion: 19

Nr3a (set 2) #7
300

Coverage: 140 (41.4% affected)
Deletion: 38

100

Coverage: 56 (100.0% affected)
Deletion: 56

Insertion: 20

Figure S7 G.A. Sunagawa

100

Coverage: 71 (91.5% affected)
Deletion: 65

Table S1. Sleep/wake phenotype, Related to Figure 3, 4 and 5. Every mouse phenotyped in SSS under LD
conditions. The values are mean ± SD for wild-type and mean ± SEM for KOs.
Amplitude

Pws

Psw

108

Daily sleep
time (min)735.7 ± 50.8

0.4462 ± 0.0530

0.0379 ± 0.0068

0.0363 ± 0.0067

C57BL/6N female 72

636.4 ± 57.7

0.5764 ± 0.0823

0.0282 ± 0.0066

0.0353 ± 0.0064

C57BL/6J male 178
C57BL/6J female 173
Bmal1
34
Conventional
Cry1/Cry227
KO
Per1/Per2
26
Tyr
7
Bmal1
6
Cry1/Cry2
5
Per1/Per2
2
Hcrt
7
Triple-target
Nr2a
8
CRISPR KO
Nr2c
15
Nr2d
13
Nr3a (set 1)
8
Nr3a (set 2)
12
Nr3b
11

699.3 ± 49.6
613.2 ± 50.1
719.1 ± 15.0
748.8 ± 11.5
742.9 ± 14.7
734.5 ± 17.2
758.5 ± 14.6
725.8 ± 14.5
716.2 ± 2.3
720.1 ± 24.1
711.9 ± 29.5
715.0 ± 11.6
755.8 ± 10.6
639.0 ± 17.5
627.1 ± 11.2
683.7 ± 11.3

0.4948 ± 0.0610
0.5716 ± 0.0815
0.3871 ± 0.0132
0.3586 ± 0.0119
0.4308 ± 0.0119
0.4249 ± 0.0097
0.3735 ± 0.0189
0.3489 ± 0.0092
0.3699 ± 0.0253
0.3925 ± 0.0278
0.5328 ± 0.0290
0.4557 ± 0.0188
0.4052 ± 0.0131
0.5826 ± 0.0342
0.5737 ± 0.0169
0.5004 ± 0.0149

0.0387 ± 0.0064
0.0300 ± 0.0050
0.0312 ± 0.0014
0.0331 ± 0.0012
0.0456 ± 0.0021
0.0368 ± 0.0023
0.0307 ± 0.0022
0.0388 ± 0.0018
0.0364 ± 0.0009
0.0548 ± 0.0022
0.0407 ± 0.0035
0.0343 ± 0.0012
0.0408 ± 0.0011
0.0250 ± 0.0015
0.0254 ± 0.0009
0.0310 ± 0.0015

0.0411 ± 0.0076
0.0406 ± 0.0075
0.0310 ± 0.0012
0.0307 ± 0.0014
0.0431 ± 0.0021
0.0350 ± 0.0012
0.0274 ± 0.0015
0.0381 ± 0.0013
0.0368 ± 0.0007
0.0556 ± 0.0048
0.0410 ± 0.0026
0.0348 ± 0.0013
0.0369 ± 0.0010
0.0310 ± 0.0009
0.0363 ± 0.0006
0.0342 ± 0.0015

Wild-type

Strain/gene#

C57BL/6N male

Table S2. KO mice production by triple-target CRISPR method, Related to Figure 1, 4 and 5. Cas9 mRNAand gRNAs targeting Tyr gene and circadian/sleep related genes (Bmal1, Cry1/Cry2, Per1/Per2 and Hcrt) and
NMDA receptor family (Nr1, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a, and Nr3b) were injected into fertilized C57BL/6N
eggs. Male mice, which surveived at least the age of 6 weeks, were used for SSS analysis. The table shows thenumber of transferred embryos, number of offsprings and the results of genotyping for all mice used in SSS
analysis.
Non-KO
NotTransferred
Offspring
Genotyped KO confirmed
Gene
confirmed determinedmale micemale miceembryos
Male
Femalemale micemale mice
Tyr
150
9
11
7
7
0
0
Bmal1
111
6
8
6
6
0
0
Cry1/Cry2
105
6
8
6
5
0
1
Per1/Per2
169
3
5
3
2
0
1
Hcrt
96
11
8
10
7
0
3
Nr1
205
0
0
0
0
0
0
Nr2a
131
8
12
8
8
0
0
Nr2b
236
1
0
1
0
1
0
Nr2c
143
16
8
15
15
0
0
Nr2d
140
13
20
13
13
0
0
Nr3a (set 1)
135
8
9
8
8
0
0
Nr3a (set 2)
124
13
7
13
12
0
1
Nr3b
205
12
9
12
11
0
1
Total
1950
106
105
102
94
1
7
100.0%
92.2%
1.0%
6.9%

Table S5. Exome sequencing statistics for Tyr and Nr3a KO mice, Related to Figure 2 and 6. See
Methods for the detail of library preparation and sequencing. 'Target bases' are those in bait regions designed
for capturing exons.

Proportionof on-baitbases inuniquely
mappedreads (%)

Targetbases
achieving
10X orgreatercoverage(%)

Sample namein DDBJ

LibraryTotal
number of
reads

Number of
uniquely
mappedreads

Wildtype used in
Tyr KO analysis

50,520,378

46,336,283

91.7

3,032,197,583

53.7

95.0

tyr-wild-type

Tyr #1

53,635,574

49,124,972

91.6

3,196,839,130

53.5

95.6

tyr-01

Tyr #250,975,220

47,102,814

92.4

2,258,383,004

39.4

91.6

tyr-02

Wildtype used in
Nr3a KO analysis

237,830,634

217,074,710

91.3

13,453,459,028

51.0

99.0

nr3a-wild-type

Nr3a (set 1) #6

182,374,702

172,485,979

94.6

14,927,922,341

69.9

98.9

nr3a-set1-06

Nr3a (set 1) #8

194,956,048

184,558,103

94.7

15,510,369,211

67.8

99.0

nr3a-set1-08

Nr3a (set 2) #1

175,181,378

167,133,655

95.4

14,427,136,154

69.7

98.5

nr3a-set2-01

Nr3a (set 2) #7

187,343,854

177,780,377

94.9

15,194,075,796

69.0

98.8

nr3a-set2-07

Uniquely
Number of
mappedreads (%) on-bait bases

Data S1. The Source Code of the SSS Analysis, Written in R Language
The code can be downloaded from https://goo.gl/teteJM.