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Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology

Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology. James J. Collins Center for BioDynamics and Department of Biomedical Engineering Boston University. Human Balance Control and Vibrating Insoles. Directed Evolution of Academic Interests. Charles Cantor

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Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology

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  1. Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology James J. Collins Center for BioDynamics andDepartment of Biomedical EngineeringBoston University

  2. Human Balance Control and Vibrating Insoles

  3. Directed Evolution of Academic Interests Charles Cantor Boston University and Sequenom

  4. Off On # of Cells Gene Expression 1 2 3 4 Synthetic Biology: Engineered Gene Networks Encode into DNA plasmid Transfer to cell Test network dynamics Design & model network

  5. Schematic Design of Genetic Toggle Switch Inducer 1 Reporter Repressor 2 Repressor 1 Inducer 2 TS Gardner et al., Nature, 2000

  6. 0 0 Toggle Model Identifies the Minimal Conditions for Bistability Nonlinear ODE model: reduced rate equations for transcription and translation

  7. Toggle Model Identifies Minimal Conditions for Bistability

  8. Genetic Toggle Switch: Plasmid Design

  9. Experimental Demonstration of Bistability

  10. 4 3 2 Results: Switching Threshold

  11. Results: Switching Time Switching ON Switching OFF

  12. Programmable Cells Interfacing natural and engineered gene networks

  13. Programmable Cells: DNA Damage Sensor H Kobayashi et al., PNAS, 2004

  14. DNA Damage Sensor with a Biofilm Readout

  15. Programmable Cells: Crowd Sensor

  16. Enter and Destroy the Biofilm Matrix

  17. Results: Engineered Enzymatically Active Bacteriophage

  18. RNA-based Synthetic Biology

  19. RNA Switches: Engineered Riboregulators FJ Isaacs et al., Nature Biotechnology, 2004

  20. Engineered Riboregulator: Cis-Repression Intermediate transcription Predicted Mfold structures High transcription Only sequences with one Mfold predicted structure were pursued Shown in green is the start codon, in blue the ribosome binding site, and in red the cis-repressive sequence

  21. Engineered Riboregulator: Trans-Activation taR12-crR12 interaction

  22. Engineered Riboregulator: System Performance Steady-State Response Specificity Transient Response FJ Isaacs et al., Nature Biotechnology, 2004

  23. Engineered Mammalian Gene Switch: RNAi and Repressor Proteins

  24. Engineered Mammalian Gene Switch: Performance Characteristics

  25. Biosensors Applications of Synthetic Gene Networks Cell therapy, stem cells Engineered gene circuits Functional genomics

  26. Systems Biology: Reverse Engineering Gene Networks Gene Circuit Control Toolbox Complex Systems Toolbox Reconstructed Gene Circuitry

  27. Gene 5 Network Inference via Gene Perturbations & Expression Profiling Reverse engineer regulatory network Overexpress each gene in network Obtain expression profiles for each compound Process expression data with NIR algorithm 1. Gene 1 2. Gene 2 NIR 3. Gene 3 4. Gene 4 5. MKS Yeung et al., PNAS, 2002 J Tegner et al., PNAS, 2003

  28. E. coli SOS Pathway (DNA-Damage Repair Pathway) SOS pathway involves over 100 genes Validation study examined nine-gene subnetwork TS Gardner et al., Science, 2003

  29. dRn n SOS Network Analysis: Experimental-Computational Overview Profile mRNA Expression Apply NIR Algorithm Recover & Apply Network Perturb mRNA Expression  • 7-9 genes perturbed • Wild-type E. coli MG1655 (K-12) cell strain • Episomal, SC101-based perturbation vector • Arabinose-inducible expression system • Estimate perturbation from luciferase control • Assay 9 mRNA species • Quantitative real-time PCR • SYBR Green protocol w/ 16S RNA normalization • 8 sample replicates, duplicate PCR rxns • Statistical filtering for noise reduction Network Identification by multiple Regression (NIR) Algorithm • Identify critical nodes • Profile drug interactions • Optimize lead compounds

  30. NIR algorithm First-order approximation Model Structure Fit Criterion Minimum least squares Solution Search Strategy Constraint: k < N inputs/gene; Search: exhaustive or heuristic Data Design & Collection Steady-state; Small perturbations Linear model w/ confidence estimates Estimated Model NIR Algorithm for Inferring Genetic Networks General system ID framework

  31. recA umuDC lexA Connection strengths rpoD ssb rpoS recF rpoH dinI recA lexA ssb recF dinI umuD rpoD rpoH rpoS 0.40 -0.18 -0.01 0 0.10 0 -0.01 0 0 recA lexA ssb recF dinI umuDC rpoD rpoH rpoS 0.39 -0.67 -0.01 0 0.09 -0.07 0 0 0 0.04 -1.19 -0.28 0 0.05 0 0.03 0 0 0 0 0 0 0 0 0 0 0 0.28 0 0 0 -1.09 0.16 -0.04 0.01 0 0.11 -0.40 -0.02 0 0.20 -0.15 0 0 0 -0.17 0 -0.02 0 0.03 0 -0.51 0.02 0 0.10 0 0 0 0.01 -0.03 0 0.52 0 0.22 0 0 -1.68 0.67 0 0.08 0 -2.92 SOS Subnetwork Model Identified by NIR Graphical model Quantitative regulatory model Majority of previously observed influences discovered despite high noise (68% N/S)

  32. recA recA Influence strengths umuDC lexA lexA - -0.18 -0.01 0 0.10 0 -0.01 0 0 recA lexA ssb recF dinI umuDC rpoD rpoH rpoS 0.39 - -0.01 0 0.09 -0.07 0 0 0 rpoD ssb 0.04 -1.19 - 0 0.05 0 0.03 0 0 0 0 0 - 0 0 0 0 0 0.28 0 0 0 - 0.16 -0.04 0.01 0 0.11 -0.40 -0.02 0 0.20 - 0 0 0 -0.17 0 -0.02 0 0.03 0 - 0.02 0 0.10 0 0 0 0.01 -0.03 0 - 0 rpoS recF 0.22 0 0 -1.68 0.67 0 0.08 0 - rpoH dinI 16 14 recA lexA ssb recF dinI umuD rpoD rpoH rpoS 12 10 8 6 4 2 0 recA lexA ssb recF dinI umu rpoD rpoH rpoS NIR Model Correctly Identifies Major SOS Network Regulators Mean influence on other genes recA and lexA identified as major regulators in the SOS subnetwork Mean Respone (%)

  33. drug drug Solved Using NIR Identified Network Can Be Used to Profile Drug Targets Treat cells with drug compound Obtain expression profile Filter profile using identified network ID direct genetic targets of drug

  34. 3 2 lexA rpoD recA ssb recF dinI umu rpoH rpoS 1 0 -1 2.5 -2 2 1.5 1 0.5 0 -0.5 recA lexA ssb recF dinI umu rpoD rpoH rpoS NIR Validation: recA/lexA Double Perturbation Expression changes Following recA/lexA double perturbation Cannot distinguish affected genes using just expression data Predicted mediators: lexA and recA identified as perturbed genes by network model Correct mediators of expression profile identified using NIR approach

  35. 4 3.5 3 2.5 2 1.5 1 0.5 0 recA lexA ssb recF dinI umu rpoD rpoH rpoS 2.5 2 1.5 1 0.5 0 -0.5 recA lexA ssb recF dinI umu rpoD rpoH rpoS NIR Validation: MMC Mode of Action in E. coli Expression changes Following mitomycin C perturbation Predicted mediators recA and umuDC identified as mediators

  36. recA lexA ssb recF dinI umu rpoD rpoH rpoS Network Model Identifies Mode of Action of Additional Stressors Predicted mediators Mitomycin C DNA-damaging agents UV radiation Pefloxacin Does not damage DNA Novobiocin

  37. E. Coli Network Reconstruction on a Genome Scale

  38. Quinolones Induce an Oxidative Damage Cellular Death Pathway

  39. Bactericidal Antibiotics: Stimulate Hydroxyl Radical Formation

  40. Bacteriostatic Antibiotics: Hydroxyl Radicals Are Not Produced

  41. Disabling the SOS Response Potentiates Bactericidal Antibiotics

  42. MNI enables use of compounds, knockouts, mutations, etc. to identify network MNI 1. Drug MNI Algorithm 2. KO 3. Gene 1 Extending to Higher Organisms and Diverse Data Sets NIR 1. Gene 1 NIR Algorithm 2. Gene 2 D di Bernardo et al., Nature Biotechnology, 2005

  43. Tested MNI on Yeast Data Set of 515 Expression Profiles Measure 6000+ RNAs 515 Diverse Treatments • Data from: • TR Hughes, et al., Cell, 2000 (300 expression profiles) • S Mnaimneh, et al., Cell, 2004 (215 expression profiles)

  44. MNI Identifies Target of Itraconazole Itraconazole treatment: a known target is ERG11 Filter through MNI-inferred network model Expression Change MNI Predictions Erg11 Erg11

  45. MNI Identifies Target Pathways/Genes for Multiple Compounds D di Bernardo et al., Nature Biotechnology, 2005

  46. Identified Novel Anticancer Compound via Chemical Screen • PTSB inhibits growth in yeast and tumor cell lines In collaboration with Schaus and Elliot laboratories Dept. of Chemistry, Boston University Center for Methodology and Library Development (CMLD), Boston U.

  47. Identification and Validation of PTSB Targets TRR1/TRX2 activity inhibited in presence of PTSB MNI identifies thioredoxin (TRX2) and thioredoxin reductase (TRR1) 0 uM PTSB • Biochemical Assay: • Thioredoxin reduction of dithio(bis)nitrobenzoic acid (DTNB) • Product of reaction = thiolate anion, measured via A412 1 uM PTSB 5 uM PTSB 50 uM PTSB

  48. A Network Biology Approach to Prostate Cancer

  49. Key Enriched Pathways and Associated Genetic Mediators

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