Systems Biology of the Circadian Clock
Deborah Bell-Pedersen
Department of Biology
Gene
Expression
Issues in big data sets
Circadian
Clock
Is it rhythmic?
Phase?
Amplitude?
Clustering?
~ 50% of the
genome is
rhythmic
How to go from complex network interaction data to
predictive models?
TF network
Gene
Expression
Circadian
Clock
Phase?
Properties of Circadian Clocks
- Endogenous rhythm with a ‘free-running period’ of
about (circa) a day (diem)
- Can entrain to local time via environmental cues ( e.g. light)
Period = 25 h
midpoint
Phase
Period = 24 h
0
12
24
Light:Dark Entrained Rhythm
12
0
12
Free Running Rhythm
0
We are physiologically different depending on the
time of day
9:00 P.M.
Melatonin
Secretion Starts
12:00
Midnight
2:00 A.M.
Deepest Sleep
7:00 P.M.
Highest Body
Temperature
4:30 A.M.
Lowest Body
Temperature
6:30 P.M.
Highest Blood
Pressure
6 P.M.
6 A.M.
6:45 A.M.
Sharpest Blood
Pressure Rise
5:00 P.M.
Greatest Cardiovascular
Efficiency and Muscle
Strength
3:30 P.M.
Fastest Reaction
Time
2:30 P.M.
Best Coordination
7:30 A.M.
Melatonin
Secretion Stops
10:00 A.M.
Highest Alertness
12:00
Noon
Time of Day-Dependent Drug Effects
Targets HMG-CoA reductase
(night peak)
Detoxified by glutathione (day
peak)
Disruption of Synchrony/Phase
Shift Workers (20% of work force)
Coronary
Heart
Disease
Infections
Shift Work
Metabolic
Syndrome
Cancer
Accidents
Disruption of synchronization/phase
relationships
http://www.bbc.com/news/health-30297497
Alterations of circadian rhythms lead
to an increased risk of cancer
Mice with a defective clock have increased tumor production,
and cancer progression is accelerated in tumor bearing
animals.
Circadian
Clock
Cell Division
Chronotherapy:
Lung cancer
Colorectal Cancer
Breast Cancer
Liver Cancer
Kidney Cancer
The Melodie Pump
Ovarian Cancer
Oxaliplatin and colorectal cancer
Simple View of the Circadian Clock System
Input
Pathways
Light
Circadian
Oscillator
Output
Pathways
Overt rhythms
The negative feedback model of the circadian
oscillator
Light
Activating
Elements
degradation
Clock Genes
Inhibitory
Elements
Output Pathways
Overt rhythms
Time
Circadian clock output networks in
Neurospora crassa
~4000 ccgs (40%)
Peak at all phases
of the day
Output Networks
FRQ/FRH
WCC
?
Hurley et al., 2014
Development, stress responses, cell division, metabolism
Circadian Clock Output Pathways
Revealed by WCC ChIP-Seq in Neurospora
~200 targets
24
Transcription
Factors (TFs)
Hurley et al., 2014
Smith et al, 2010 Euk Cell
Simple model for controlling phase
WCC
Repressor
TF
Activator
Morning phase genes
TF
Mid-day phase genes
Evening phase genes
24 TF
RNA-seq
In WT versus TF mutant to
identify what genes are
regulated by the TF.
Direct or indirect
ChIP-seq
Identifies sites in the
genome bound by a
protein
ChIP Y
Y
GENE X
X
GENE Y
✔
RNA +/-Y
✔
✔
ChIP-seq of TFs Reveals a Network Hairball
Neurospora Genome Program Project Consortium NIH P01 GM068087
The Clock-Controlled TF ADV-1 is Necessary
for Rhythms in Development
WT
∆ADV-1
Matt Sachs, TAMU Biology, James Galagan, BU
NIH R01 GM113673
Regulatory Network Between the Clock and ADV-1
Revealed by RNA-seq: What controls the phase of
ADV-1 rhythms?
Can we develop a model
that will allow us to
predict
what
perturbations of the
genetic network will alter
the phase of the rhythm
of ADV-1, and eventually
any ccg, including those
involved in metabolism,
cell division?
Modeling approaches – use ADV-1 to
train a model
1. Construct a binding network
TF Con
target gene
WCC-targeted TFs
2. Identify which
genes are rhythmic
Matt Sachs, TAMU Biology, James Galagan, BU
We have great tools for model training
and validation
Wild type
TF
+
ADV-1
promoter
luc
ΔTF
Deletion of TF
+
ADV-1
promoter
luc
CSP-2 is required for ADV-1 protein
rhythms.
CSP-1 is required for the proper phase
of ADV-1 protein expression.
CLR-1 is not required for ADV-1
rhythmicity
Summary of the ADV-1 upstream network.
ADV-1 protein rhythmicity is
abolished in ΔWC-1, ΔWC-2
& ΔCSP-2
ADV-1 protein rhythms have
a phase delay
What are the rules?
Use model to make predictions of the
network for any TF
1. Construct a binding network
TF Con
target gene
WCC-targeted TFs
2. Extract circadian
features using
Fourier spectra
Matt Sachs, TAMU Biology, James Galagan, BU
NIH R01 GM113673
Downstream ADV-1 regulatory network interactions
12 additional TFs that
need to be ChiP’ed
ADV-1
csp2
vos-1
far-1
0275
cre1
7705
hsf-2
sah-1
Peak phase
ccgs
Neurospora Consortium
vad-1
sub-1
Modeling the downstream network
Chip-Seq: 24 TFs
Combine the two models into a dynamic
model to make predictions and identify
key nodes
Dynamic Model
Gene
Expression
ADV-1
Goal: Use models to predict key
nodes in the network that will
alter the phase of gene
expression
Metabolism
Need Help With
1. Time series analyses of genome-wide data sets
2. How to identify significant differences in phase and
amplitude between WT, mutants, and different
environmental conditions in genomic data sets
3. How to deal with the complexity of genetic networks
and predict function
4. How to determine how coupling among oscillators in
different tissues impact phase and amplitude
Gregg Allen – TAMHSC Neuroscience and Experimental Therapuetics
*Deb Bell-Pedersen – TAMU Biology
David Earnest - TAMHSC Neuroscience and Experimental Therapuetics
Richard Gomer – TAMU Biology
*Paul Hardin – TAMU Biology
Gladys Ko – TAMU Veterinary Integrative Biosciences
*Jerome Menet – TAMU Biology
*Christine Merlin – TAMU Biology
Weston Porter – TAMU Veterinary Integrative Biosciences
Terry Thomas – TAMU Biology
Gerard Toussaint - TAMHSC Neuroscience and Experimental Therapuetics
Chaodong Wu – TAMU Nutrition and Food Science
Mark Zoran – TAMU Biology
YOUR NAME HERE – TAMU Statistics, TAMU Math