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