Novel transcriptional networks regulated by CLOCK in human neurons

Miles R. Fontenot, Stefano Berto, Yuxiang Liu, Gordon Werthmann, Connor Douglas,

Noriyoshi Usui, Kelly Gleason, Carol A. Tamminga, Joseph S. Takahashi, and

Genevieve Konopka

Supplemental Material

Materials and Methods

Human brain tissue

Five samples each of BA10 and BA40 were obtained from the same five individuals

from the Dallas Brain Collection (Stan et al. 2006). These tissue samples featured

reasonably low post-mortem intervals (17 hours average, 21.5 hours maximum) and

were donated from individuals without a history of neurological or psychiatric disorders

as previously published (Ghose et al. 2009). See Supplemental Table S4 for detailed

demographic information.

CLOCK Chromatin-immunoprecipitation (ChIP) from adult human brain and

differentiated human neurons

ChIP experiments were carried out similar to (Koike et al. 2012) with the following

modifications. Human brain samples weighing roughly 1g were pulverized while on dry

ice. Samples were immediately homogenized by douncing in 4 mL of 1X PBS

containing 1% formaldehyde. Alternatively, ~5 × 107 differentiated human neurons were

gathered and mixed to a final concentration of 1% formaldehyde. Samples from both

human brain and in vitro neurons were treated the same way. The homogenates were

crosslinked for 12 minutes at room temperature, after which 250 µL of 2.5 M glycine

was added to quench the reaction and the mixture was moved to ice. Nuclei were

isolated by sucrose ultra-centrifugation. The homogenates were resuspended in 10 mL

of ice-cold 2.3 M sucrose containing 150 mM glycine, 10 mM HEPES pH 7.6, 15 mM

KCl, 2 mM EDTA, 0.15 mM spermine, 0.5 mM spermidine, 0.5 mM DTT and 0.5 mM

PMSF, and carefully layered on top of a 3 mL cushion of 1.85 M sucrose (containing the

same ingredients but with the addition of 10% glycerol). Samples were then centrifuged

for 1 hour at 24,000 rpm at 4°C in a Beckman SW32.1 rotor. The nuclei were washed in

1 mL of 10 mM Tris pH 7.5, 150 mM NaCl, 2 mM EDTA, transferred to a 1.5 mL

microfuge tube, and centrifuged for 5 minutes at 12,000g at 4°C. Samples were

immediately sonicated after aspirating the wash. Nuclei were resuspended in 3 mL of

ice cold 1% Triton-X sonication buffer (10 mM Tris pH 7.5, 300 mM NaCl, 1 mM EDTA,

1 mM EGTA, 1X protease inhibitor cocktail, 1 mM PMSF, 1% Triton-X, 0.1% sodium

deoxycholate). Chromatin was sheared in a 15 mL conical tube bathed in an ice bath

using a Misonix model CL-334 manual sonicator with a 417-A tip. Amplification was set

to 65, with 4 minutes of total “on” process time at 5 seconds on and 55 seconds resting

off to ensure that samples were kept ice-cold. Total applied energy was 8,000 –

12,000J. Sonicated chromatin was pre-cleared with 50 µL MagnaChIP protein A+G

magnetic beads (#16-663, Millipore) for 1 hour at 4°C. After taking 5% of sample as

input, pre-cleared chromatin was then split into 2 equal 1.3 mL aliquots and incubated

with antibody overnight at 4°C. 6 µg of rabbit αCLOCK (ab3517, Abcam, lot

#GR222742-4) was used for CLOCK ChIP and 6 µg of normal rabbit αIgG (#2729, Cell

Signaling) was used as a negative control. 40 µL of MagnaChIP protein A/G beads were

added to each IP and incubated with rotation for at least for 2 hours at 4°C. Beads were

then washed twice with IP buffer (10 mM Tris pH7.5, 150 mM NaCl, 1 mM EDTA, 1%

triton X-100, 0.1% Na-deoxycholate, 1 mM PMSF, 1X protease inhibitor), twice with

high salt wash buffer (20 mM Tris pH7.5, 500 mM NaCl, 2 mM EDTA, 1% triton X-100, 1

mM PMSF, 1X protease inhibitor), twice with LiCl wash buffer (20 mM Tris pH 7.5, 250

mM LiCl, 2 mM EDTA, 0.5% IGEPAL CA630, 1% Na-deoxycholate, 1 mM PMSF, 1X

protease inhibitor), and once with TE (10 mM Tris pH 8, 1 mM EDTA, 1 mM PMSF, 1X

protease inhibitor). Samples were eluted from the beads in 120 uL of elution buffer (20

mM Tris pH7.5, 5 mM EDTA, 0.5% SDS) per IP. Chromatin was reverse-crosslinked at

65°C overnight with constant rotation and beads were then removed. DNA was treated

with 10 µg of RNaseA for 1 hour at 37°C, after which 160 µg of proteinase K was added

for 1 hour at 55°C. Finally, DNA was purified using a Qiaquick PCR purification kit

(#28106, Qiagen).

CRISPR Knock-out (KO) of CLOCK in 293T cells

sgRNAs specific to the 8th exon of human CLOCK were generated according to the

guidelines published by Wang et. al. 2013 (Wang et al. 2013) and cloned into

pSpCas9(BB)-2A-GFP (Ran et al. 2013).

sgRNA: CACCGCTAGTGAAATTCGACAGGAC /

AAACGTCCTGTCGAATTTCACTAGC

293T cells were transfected with this plasmid using FuGENE 6 (#E2691, Promega),

incubated for 2 days, and then fluorescence activated cell sorting (Bonner et al. 1972)

was used to isolate individual cells into separate wells of a 96-well plate. Clonal

populations were grown over 2-3 weeks before being screened for CLOCK KO clones

by qRT-PCR. CLOCK KO clones used for ChIP-PCR do not express any detectable

CLOCK transcripts.

Human Neural Progenitors and Neurons

Human neural progenitors (hNPs) were purchased from Lonza and cultured as

previously described (Konopka et al. 2012b). Proliferating hNPs were cultured in

Neurobasal A medium (#10888-022, Invitrogen) supplemented with BIT (#9500,

Stemcell Technologies), antibiotic-antimycotic (#15240-062, Invitrogen), Glutamax

(#35050-061, Invitrogen), 10 ng/mL EGF (#100-15, PeproTech), and 10 ng/mL FGF

(#100-18B, PeproTech) on polyornithine- (#P3655, Sigma-Aldrich) and fibronectin-

(#F1141, Sigma-Aldrich) coated plates incubated at 37°C under 5% CO2. Cells were

half-fed every second day. hNPs were seeded at 1 million cells / well of a standard 6-

well plate for differentiation into human neurons. Differentiating cells were cultured in

Neurobasal A supplemented with B27 (#12587010, Invitrogen), antibiotic-antimycotic,

Glutamax, 10 ng/mL BDNF (#450-02, PeproTech), 10 ng/mL NT-3 (#450-03,

PeproTech), 10 ng/mL retinoic acid (#R2625, Sigma), 10 µM forskolin (#F6886, Sigma),

and 10 mM KCl (#7447-40, Fisher) on polyornithine- and laminin- (#23017-015,

Invitrogen) coated plates incubated at 37°C under 5% CO2. Cells were again half-fed

every second day for 4 weeks of differentiation.

Cloning tagged overexpression plasmid

CLOCK cDNA was originally cloned into pcDNA3.1B using restriction enzyme digestion

with HindIII and BamHI and ligation into the vector. 3X-FLAG N-terminal tag was added

through PCR insertion. The tagged CLOCK construct was then moved in pENTR/D-

TOPO using manufacturer’s Gateway Cloning System protocol (K240020, Thermo

Fisher). From there, the tagged CLOCK construct was recombined into the pLenti6.4

vector (V49610, Thermo Fisher), also following manufacturer’s protocol, with EF1α

driving overexpression of the cDNA. Transcriptional activity of this tagged CLOCK

construct was demonstrated through a luciferase assay driven by the PER1 promotor

(data not shown). All primers used in cloning are available upon request.

CLOCK KD and overexpression in human neurons

Lentiviral transduction was used to introduce 3X-FLAG-tagged CLOCK and untagged

ARNTL in pLenti6.4 for CLOCK overexpression (Fig. 1F and 2D) and pZIP shRNA for

CLOCK KD in four-week-differentiated human neurons. Neurons overexpressing FLAG-

tagged CLOCK were collected three days after viral transduction for FLAG ChIP-seq

experiments and were used with the same protocol detailed above for CLOCK ChIP-seq

in human brain, except using 6 µg anti-FLAG F3165 (Sigma) for the ChIP. Refer to Fig.

2A for a detailed timeline concerning RNA-seq experiments. pZIP shERWOOD shRNA

constructs were obtained from Transomics and included both scrambled non-targeting

shRNA as a negative control and the following shRNA (ULTRA-3415997) for CLOCK

KD (presented as scaffold-sense-loop-antisense-scaffold regions):

TGCTGTTGACAGTGAGCG-AAGAGATGACAGTAGTATTTTA-

TAGTGAAGCCACAGATGTA-TAAAATACTACTGTCATCTCTG-

TGCCTACTGCCTCGGA

and for NR1D1 KD:

TGCTGTTGACAGTGAGCG-CCACCTGGCAACTTCAATGCCA-

TAGTGAAGCCACAGATGTA-TGGCATTGAAGTTGCCAGGTGA-

TGCCTACTGCCTCGGA

Neurosphere migration assay

hNPs were transduced with pZIP shRNA. Two days later, hNPs were seeded in

proliferation media as described above at 30,000 cells/well in 200 µL on 96-well Corning

spheroid microplates (#CLS4520-10EA, Sigma). Neurosphere formation proceeded for

two days, after which proliferation media was replaced with differentiation media as

described above. Immediately after this media change (four days after viral

transduction), neurospheres were gently transferred onto polyornithine- and laminin-

coated coverslips using Axygen 200 µL wide-bore pipette tips (TF-205-WB-R-S,

Corning). After two days of migration at 37˚C on the coated coverslips, the

neurospheres were immunostained and analyzed for the neuronal migration distance

from the core. This timeframe overlapped with the experimental design used in the

RNA-seq experiments following CLOCK KD (Fig. 2A). A circle was drawn on the border

of the inner core of the neurosphere, then this circle was scaled by 200% using Adobe

Photoshop. Average migration distance was established using ImageJ by measuring

the distances between the center inner core and all cells beyond 200% of the inner core

size (350-500 cells per neurosphere, see Fig. 5D and 5F). Only GFP+ or RFP+ cells

(indicating transduction with the pZIP reporter) with no evidence of nuclei pyknosis or

cell membrane disruption were counted in the analysis.

Transwell migration assay

The transwell migration assay (protocol based on Giannelli et al. 1997) was set up to

closely mirror the neurosphere migration assay in experimental design to allow

comparison of the results. As above, hNPs were transduced with pZIP shRNA as

scrambled control (GFP+) or as CLOCK KD (RFP+). Two days later, hNPs infected with

either control or CLOCK KD constructs were mixed together in a 1:1 ratio and

transferred in proliferation media as described above at 12,500 cells/well with

polyornithine- and laminin-coated transwell membranes (#351152, Corning). After either

24 or 48 hours of migration at 37˚C, the cells were harvested. The proportion of hNPs

that migrated to the other side of the membrane was quantified by systematically

measuring 5 microscopic fields on each side of the membrane and comparing the two

sides.

Lumicycle analysis of circadian cycling

A luciferase driven by the ARNTL promoter (Nagoshi et al. 2004) in conjunction with a

custom Actimetrics Lumicycle was used to assay circadian rhythms following CLOCK

KD in differentiated human neurons. The same timeline as detailed for the RNA-seq

experiment (Fig. 2A) was used, except cells were placed in the lumicycle immediately

after dexamethasone synchronization (Balsalobre et al. 2000; Kamagata et al. 2017)

and addition of 100 nM luciferin, and recorded for five days. Five replicates each of

control shRNA and CLOCK KD shRNA were recorded. The data presented represents

ZT24-ZT72; this timing is equivalent to the period over which RNA-seq samples were

collected. Actimetrics MultiCycle software was used to analyze lumicycle data. Data

were detrended (first-order polynomial) and then best-fit to a sine wave estimated by a

Levenberg–Marquardt algorithm for measurement of period, phase, amplitude, and

damping rate (Chen et al. 2012).

RNA harvesting and real-time RT–PCR

Total RNA was purified from differentiated neurons using a miRNeasy kit (#217004,

Qiagen) following the manufacturer’s recommendations. qRT–PCR was performed as

previously described (Usui et al. 2017), using primers specific to 18S rRNA as a

reference for normalization. All primer sequences are available on request.

ChIP-PCR

ChIP-PCR was performed using the same reagents and equipment as qRT-PCR.

Enrichment of known CLOCK ChIP target region in the promoter region of PER1 was

calculated relative to amplification efficiency of a nearby genomic region with no

evidence of CLOCK ChIP binding.

PER1 CLOCK binding region positive primers:

CGTCTTCTCATTGGTCAGCA / CCCAGCCAATAAGAACCTCA

PER1 Negative primers:

CTTCCACCTCACTCCCTCAG / CCATGGGGAGAACAGAACAG

ChIP- and RNA-seq library construction and sequencing

ChIP- and RNA-seq libraries were prepared in-house as previously described

(Takahashi et al. 2015). For RNA-seq libraries, mRNA was isolated from randomized

samples using poly-A selection and strand-specific libraries were generated.

Sequencing was performed on randomly pooled samples by the McDermott Sequencing

Core at UT Southwestern on an Illumina NextSeq 500 sequencer. Single-end, 75-base-

pair (bp) reads were generated.

ATAC-seq protocol and library generation

Three replicates of each genotype of differentiated human neurons were collected 4

days after lentiviral infection. No dexamethasone synchronization was performed.

ATAC-seq libraries were generated per Buenrostro et. al. 2015 (Buenrostro et al.

2015a) with no additional modifications.

ChIP-seq data analyses

Reads were aligned to human hg38 reference genome using BWA (Li and Durbin

2010). Reads with MAPQ < 10 were discarded. Duplicates were removed using the

MarkDuplicates function implemented in Picard tools (Picard tools, Broad Institute).

MACS2 was used to calculate ChIP-seq enrichment (Zhang et al. 2008) with the

following parameters: “callpeak --bw 300 --tsize 76 --nomodel --extsize 200 -q 0.05 –

nolambda”. The CLOCK and IgG control IP for each sample was compared with the

relative input DNA. IgG negative control peaks were further removed from the CLOCK

peaks using bedtools (Quinlan and Hall 2010). The five replicates of each brain region

were compared in a regional consensus CLOCK peak list using bedtools (Supplemental

Table S1). Initially, irreproducibility discovery rate (IDR) scripts were used to compare

replicates (https://sites.google.com/site/anshulkundaje/projects/idr) (Li et al. 2011; Landt

et al. 2012). In addition, enrichment score and quality measures were further calculated

using PhantomPeakQualtools (https://github.com/kundajelab/phantompeakqualtools)

(Marinov et al. 2014). Due to the multiple biological replicates (n=5) and the limitation of

the pair-wise comparative approach of IDR (Yang et al. 2014), we defined the common

peakset using a consensus approach, consisting of peaks present in at least 4 of the 5

biological replicates for each brain region. We include the quality control measures in

Supplemental Table S1. Downstream analysis and visualizations of the consensus peak

lists were performed using the ChIPseeker package in R (Yu et al. 2015). Unsupervised

transcription factor motif enrichment for BA10 and BA40 CLOCK-bound regions was

performed using meme-chip from the MEME toolkit (Bailey et al. 2009) with the

following parameters: “-meme-minw 6 –meme-maxw 8 –meme-nmotifs 10 –meme-p 10

–centrimo”. Enrichment score was calculated using the 2kb upstream region of protein

coding genes (19787 genes) as sequence background.

RNA-seq data analyses

Three biological replicates of each genotype at each time point were collected. Reads

were aligned to human hg38 reference genome using STAR 2.5.2b (Dobin et al. 2013)

with the following parameters: “--outFilterMultimapNmax 10 --alignSJoverhangMin 10 --

alignSJDBoverhangMin 1 --outFilterMismatchNmax 3 --twopassMode Basic”. Gencode

annotation for hg38 (version 24) was used as reference to build STAR indexes and

alignment annotation (Harrow et al. 2012). For each sample, a BAM file including

mapped and unmapped with spanning splice junctions was produced. Secondary

alignment and multi-mapped reads where further removed using in-house scripts. Only

uniquely mapped reads were retained for further analysis. Overall quality control metrics

were performed using RseqQC using the UCSC hg38 gene model provided (Wang et

al. 2012). This includes: number of reads after multiple-step filtering, ribosomal RNA

reads depletion, and reads mapped to exons, UTRs, and intronic regions. Gene level

expression was calculated using HTseq version 0.6.0 using intersection-strict mode by

exonic regions (Anders et al. 2015). Counts were calculated based on protein-coding

genes annotation from hg38 Gencode gtf file (version 24). RPKM (reads per kilobase of

transcript per million reads mapped) values were calculated using edgeR (Robinson et

al. 2010). Length was curated using the protein-coding genes annotation from the hg38

Gencode gtf file. RPKM values were filtered for downstream for differential and co-

expression analyses using a “by time” RPKM cutoff. Briefly, a gene is considered

expressed if the RPKM ≥ 0.5 in all three biological replicates (either control or KD) in

any one experimental time point. We detected 12,183 protein-coding genes expressed

in our dataset using these criteria.

ATAC-seq data analyses

Reads were aligned to human hg38 using Bowtie 1 (Langmead et al. 2009) following

the ENCODE pipeline. Reads with MAPQ < 10 were discarded. Duplicates were

removed using the MarkDuplicates function implemented in Picard tools (Picard tools,

Broad Institute). MACS2 was used to calculate ATAC-seq enrichment (Zhang et al.

2008) with the following parameters: “callpeak --bw 300 --tsize 76 --extsize 300 --q 0.05

--nolambda”. Blacklisted regions (Buenrostro et al. 2015b) were translated in hg38

coordinates using liftOver tools (UCSC) and removed from the ATAC-seq called peaks

using bedtools (Quinlan and Hall 2010). IDR scripts

(https://sites.google.com/site/anshulkundaje/projects/idr) were used to compare

replicates (Li et al. 2011; Landt et al. 2012). Enrichment and quality measures were

further calculated using PhantomPeakQualtools

(https://github.com/kundajelab/phantompeakqualtools) (Marinov et al. 2014).

Downstream analyses and visualizations of the ATAC-seq peak lists were performed

using the ChIPseeker package in R (Yu et al. 2015). Differentially open chromatin

regions were annotated using the DiffBind package in R (Stark and Brown 2012).

Differential expression analyses

Differential expression between control and CLOCK KD samples was assessed using

the DESeq2 package in R (Anders and Huber 2010) (Supplemental Table S2). The

expression matrix contained the 12,183 protein-coding genes that passed the RPKM

cutoff as described above. Linear regression was performed using DESeq2 to remove

covariate variables: RIN value and library construction batch. All differentially expressed

genes with an FDR ≤ 0.05 and log2(fold change) ≥ |0.3| (Araujo et al. 2015) were

retained. A permutation test was also applied using 1000 permuted experiments. None

of these permuted analyses showed the same genes differentially expressed

(permutation p < 0.001).

Co-expression network analyses

To identify modules of co-expressed genes in the RNA-seq data, we carried out

weighted gene co-expression network analysis (WGCNA) (Langfelder and Horvath

2008) (Supplemental Table S3). RPKM values were filtered as described above, and

log2(RPKM+1) were used as input data. RIN value and library construction batch

covariates were removed using the ComBat function in the SVA package in R (Leek et

al. 2012). We generated a signed network by using the blockwiseModules function in

the WGCNA package. Beta was chosen as 14 so the network has a high scale free R

square (r2 = 0.79). For other parameters, we used corType = bicor, maxBlockSize =

13000, mergingThresh = 0.15, reassignThreshold = 1e-10, deepSplit = 2,

detectCutHeight = 0.999, and minModuleSize = 50. The modules were then determined

using the dynamic tree-cutting algorithm. Using these settings, 20 unique modules were

identified. We chose to focus on the three modules (CM1-CM3) that most correlated

(positively or negatively) with CLOCK KD. For network visualization, the top 250 edges

of the top 20 kWithin hub genes were plotted using Cytoscape 3.4.0 (Shannon et al.

2003). Gene node and font size is correlated with the kWithin value for each gene.

Functional annotation of DE and WGCNA genes

The functional annotation of differentially expressed and co-expressed genes was

performed using ToppGene (Chen et al. 2009). A Benjamini-Hochberg FDR (p < 0.05)

was applied for a multiple comparisons adjustment. Redundant gene ontology items

were merged using Revigo (Supek et al. 2011) for figure visualizations. Semantic

similarity analysis for GO terms was performed using GOSemSim (Schlicker et al. 2006)

based on Relevance method (Rel).

Geneset enrichment analyses

Enrichment analyses were performed using a hypergeometric test, followed by a

Benjamini-Hochberg correction. Hypergeometric values were recalculated 1000 times

and none of the overlaps resulted in a significant p-value. This method was used for

both the DEGs and the WGCNA module enrichment. Genes expressed in human brain

(Parikshak et al. 2013) are included as the background gene set for all enrichment

analyses. Because gene expression is relatively homogenous across the adult human

cortex (Miller et al. 2014; Hawrylycz et al. 2015), background gene sets were not refined

for regional expression. Developmental expression was not taken into account for

background gene sets for multiple reasons: 1) the disease and comparative datasets

are based upon adult tissues or cells in culture, 2) fetal human brain gene expression

datasets include data from only a few individuals, 3) it is not clear what time point in

fetal human brain development would be most comparable to the expression of

differentiated hNPs for a given background expression dataset, and 4) it is better to use

the same background gene set to compare data across analyses. The ASD-related and

high-confidence “scored” ASD genes were obtained from the Simons Foundation

Autism Research Initiative (Banerjee-Basu and Packer 2010). Intellectual disability

genes were collected from multiple independent sources (Inlow and Restifo 2004;

Ropers 2008; van Bokhoven 2011; Lubs et al. 2012; Iossifov et al. 2014). Genes related

to synapse were downloaded from the SynaptomeDB (Pirooznia et al. 2012). An

independent publication was used for FMRP gene targets (Darnell et al. 2011). Epilepsy

associated genes were curated by EpilepsyGene Database (Ran et al. 2015). Genes

with human-specific patterns of expression (comparing human to chimpanzee

expression) were curated from the overlap of multiple independent sources (Caceres et

al. 2003; Khaitovich et al. 2004; Khaitovich et al. 2005; Babbitt et al. 2010; Konopka et

al. 2012a; Liu et al. 2012). Even though the methods were not the same, these studies

on human-specific gene expression were integrated due to the limited number of

chimpanzee samples in each study.

Statistical analysis and code availability

Statistical analyses were performed using R. All code is available upon request from the

corresponding authors.

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Supplemental Figure Legends

Supplemental Figure S1. CLOCK plays an important regulatory role in neocortical

region BA40. CLOCK is co-expressed with more transcription factors in BA40 than any

other neocortical region in the BrainSpan dataset based on wTO analysis.

Supplemental Figure S2. CLOCK KO cells show no CLOCK ChIP-PCR enrichment.

ChIP-PCR enrichment of known CLOCK binding region in PER1 promoter is abolished

in 293T cells with knockout of endogenous CLOCK expression through CRISPR.

Supplemental Figure S3. CLOCK ChIP targets in BA10 and BA40 overlap with

previously published CLOCK ChIP targets. Chow-Rusky plot showing significant

overlap of CLOCK ChIP-seq transcriptional targets from adult human brain with CLOCK

ChIP-seq data from Koike et. al. 2012, Perelis et. al. 2015, and Puram et. al. 2016

(hypergeometric test, FDR = 3.20 × 10-4, 1.94 × 10-13, and 1.22 × 10-9 respectively).

Supplemental Figure S4. More example CLOCK ChIP track files. A. Representative

track files of BA10 and BA40 both demonstrate enriched CLOCK binding upstream of

known canonical circadian targets (e.g. HLF and TEF) B. Representative CLOCK ChIP

tracks also show binding upstream of non-circadian targets (e.g. DNAJC16 and

MAP2K7).

Supplemental Figure S5: Heatmap representing semantic similarity scores

comparing BA10 and BA40 gene ontology terms. A high semantic similarity score

(simRel = 0.378; see Materials and Methods, Schlicker et. al. 2006) indicates strong

similarity in gene ontology terms of CLOCK ChIP-seq targets in the BA10 and BA40

datasets.

Supplemental Figure S6. Further characterization of CLOCK shRNA KD efficiency.

Note that “CLOCK shRNA 1” was used for all RNA-seq experiments in the main text

and “CLOCK shRNA 2” was used for further qRT-PCR experiments shown in

supplemental figure S8. A. Western blot of FLAG-tagged overexpressed CLOCK protein

in the context of shRNA KD demonstrates knockdown of CLOCK on the protein level

(n=3). B. Quantification of Western blot data from panel A (n=3). ** p < 0.01, **** p <

0.0001, Student’s T-test. C. qRT-PCR confirmation of CLOCK shRNA KD efficiency and

effect on example downstream target NR1D1 in differentiated human neurons (n=3). **

p < 0.01, **** p < 0.0001, Student’s T-test.

Supplemental Figure S7. Differentiated human neurons display weak cycling in

vitro. A. Neurons expressing luciferase driven by the ARNTL promoter show low raw

counts and statistically significant differences in period (Student’s t-test, p = 0.0028) and

amplitude (Student’s t-test, p = 0.0037) in CLOCK KD samples. B. Average RPKM

values for control samples of RNA-seq time course samples do not reveal detectable

rhythms over an equivalent period of time in three example canonical circadian genes.

Supplemental Figure S8. Differential gene expression of core circadian factors in

RNA-seq dataset. Heat map of differential expression values for core circadian genes

at each time point. Blue color represents downregulation and red color represents

upregulation of the gene with CLOCK KD relative to control. Upper value in each box is

the log2(fold change) and lower value in each box is the p-value for differential

expression.

Supplemental Figure S9. qRT-PCR control experiment for RNA-seq results. qRT-

PCR showing reproducibility of RNA-seq DE results with both an alternative shRNA to

CLOCK and the same shRNA in a different neuronal line. Plotted RNA-seq values

represent fold change relative to control. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p <

0.0001.

Supplemental Figure S10. Further characterization of CLOCK KD effect on

neuronal migration. Transwell assay recapitulates the phenotype seen in the

neurosphere assay in Figure 5. Neuronal cells transduced with lentivirus knocking down

CLOCK migrated further than control cells at both a 24 hour (A) and 48 hour (B) time

point (n=3 transwell membranes for each time point).

Supplemental Figure S11. Knockdown of direct CLOCK transcriptional target

NR1D1 results in a similar change in neuronal migration compared to CLOCK KD.

Human neuronal cells in the neurosphere assay migrate further from the core following

NR1D1 KD, relative to within-neurosphere control cells.

Supplemental Table S1. ChIP-seq database, gene set intersections, gene

ontology, and IDR statistics.

Supplemental Table S2. RNA-seq database with differentially expressed genes,

gene set intersections, and gene ontology.

Supplemental Table S3. WGCNA database and gene ontology of CM1-3.

Supplemental Table S4. Demographics of human brain samples used in ChIP-seq

experiments.