Dr. Leroy Hood Lecuture on P4 Medicine

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Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University …

Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University Medical Center and the Institute for Systems Biology. The partnership will be known as

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  • 1. Systems Biology and Systems Medicine: Catalyzing a Revolution from Reactive to Proactive (P4) MedicinePredictive, Personalized, Preventive and Participatory
    Lee Hood
    Institute for Systems Biology, Seattle
  • 2. I Participated in Four Paradigm Changes in Biology Leading to P4 Medicine
    Bringing engineering to biology (high throughput biology)
    The human genome project
    Cross-disciplinary biology
    Systems biology
    Predictive, Preventive, Personalized, and Participatory medicine (P4 Medicine)
    Each fundamentally changed how we think about biology and medicine.
    Each was met initially with enormous skepticism.
    Each new idea needed new organizational structure.
  • 3. Contemporary Systems Biology is Predicated on Viewing Biology is an Informational Science
  • 4. There are Two Types of Biological Information that
    Can Lead to Disease
    Thedigital informationof the genome
    • The environmental information that impinges upon and modifies the digital information
  • Two General Biological Structures that Handle Information
    Biological networks capture, transmit, process and pass on information
    • Simple and complex molecular machines execute biological functions
  • Left Index Fingerprints from Identical Twins
  • 5. ISB’s View of Systems Biology
  • 6. Agenda: Use biology to drive technology and computation. Need to create a cross-disciplinary culture.
    Biological Information
    BIOLOGY
    Cross-Disciplinary
    Culture
    Team Science
    TECHNOLOGY
    COMPUTATION
  • 12. Essentials of Systems Biology
    Hypothesis-driven
    Global data acquisition
    Integrate different types of data
    Delineate biological network dynamics
    Formulate models that are predictive and actionable.
  • 13. A Systems View of Disease
  • 14. dynamics of
    pathophysiology
    diagnosis
    therapy
    prevention
    A Systems View of Medicine Postulates that Disease Arises from Disease-
    Perturbed Networks
    Non-Diseased
    Diseased
  • 15. A Systems Approach to Prion Disease in Mice
  • 16. Prion disease example:Prion Protein Exists in Two Forms
    PrP Genetic Mutations
    PrPSc Infections
    Spontaneous conversion
    Cellular PrPC
    Infectious PrPSc
  • 17. Global TranscriptomeAnalysis—Differentially Expressed Genes (DEGs)
    Time-course array analysis:
    subtrative analyses to DEGs
    • C57BL/6J-RML: 12 time points
    • 18. FVB/NCr-RML: 11 time points
    • 19. BL6.I-301V: 9 time points
    • 20. FVB/B4053-RML: 8 time points
    Inoculate w/ Prions
    Prion strains:
    Mouse strains:
    Prion infected brain
    RNA
    from brain
    homogenate
    Almost 50 million
    Data points
    Mouse Genome array:
    45,000 probe sets
    ~22,000 mouse genes.
    Uninfected brain
    Carlson lab
    Inyoul Lee (Brianne Ogata, David Baxter)
    Bruz Marzolf (Microarray Facility)
  • 25. Prion Disease in Eight Mouse Strains: dealing with the signal to noise challenge
    employing subtractive biology
    Differentially Expressed Genes--DEGs--7400 to 333
  • 26. Neuropathology Identifies 4 Networks
    Microglia / Astrocyte
    activation
    PrP accumulation
    Synaptic Degeneration
    Nerve cell death
    Infected
    Normal
  • 27. Integration of Six Data Types for Prion Disease Studies in Mice
    Deep brain transcriptome analyses at 10 time points across disease onset in 8 mouse strains
    Correlate with protein interaction data from known (histopathology) disease-perturbed networks
    Correlation with dynamical histopathologicalstudies
    Correlation with clinical signs
    Distribution of infectious prion protein in the brains across disease progression
    Brain-specific blood protein concentration changes
  • 28. Dynamics of Prion Accumulation Network in the Brain: 6 weeks, 10 weeks and 20 weeks of 22 week course
  • 29. PrP accumulation and replication network—6 weeks
  • 30. PrP accumulation and replication network—10 weeks
  • 31. PrP accumulation and replication network—20 weeks
  • 32. Sequential Disease-Perturbation of the Four Networks of Prion Disease
    18~20 wk
    22 wk
    0 wk
    Clinical Signs
    Prion
    accumulation
    Glial
    Activation
    SynapticDegeneration
    Neuronal
    Cell Death
    Na+
    channels
    Reactive
    Astrocytes
    Cholesterol
    transport
    Caspases
    Sphingolipid
    synthesis
    Cargo
    transport
    Leukocyte
    extravasation
    Lysosome
    proteolysis
    *Arachidonate
    metab./Ca+ sig.
  • 33. 333 DEGs encode core prion disease
    231/333 DEGs encode known disease pathogenic networks
    102/333 DEGs encode novel pathogenic networks--the dark genes of prion disease
    Disease-perturbed networks sequentially activated
    Re-engineer disease-perturbed networks with drugs—new approach to drug target discovery
    Implications for systems diagnostics
    DEGs Encoding Known and Novel Prion Disease Phenotypes
  • 34. A Systems Approach to Blood Diagnostics
  • 35. Organ-Specific Blood FingerprintsMaking Blood A Window Distinguishing Health and Disease
    Blood Vessel
  • 36. Organ-specific Protein Blood Fingerprints—Disease Diagnostics
    Early detection
    Disease stratification
    Disease progression
    Follow therapy
    Assess reoccurances
    Integrated Diagnostics—platform company for P4 medicine
  • 37. Strategies and Technologies: Exploring New Dimensions of Data Space
  • 38. Whole Genome Sequencing of Families: New Genomic Strategy
  • 39. Whole Genome Sequencing of Family of Four
    Unaffected parents
    Children with craniofacial
    Malformation (Miller Syndrome)
    and lung disease (ciliary dyskinesia)
    • Noise reduction power of a family sequence permits:
    • 40. Identify 70% sequencing errors using principles of Mendelian genetics — ~1/100,000 error rate
    • 41. Discovery of ~230,000 novel (rare) SNPs—4.2 million SNPs in family
    • 42. Reduced disease gene candidates to 4
  • Microfluidic Protein Chip:Assay 2500 Organ-Specific Blood Proteinsfrom Millions of Patients Using a Drop of Blood
    Jim Heath--Caltech
  • 43. DEAL for In vitro molecular diagnostics:
    Integrated nanotech/microfluidics platform
    300 nanoliters of plasma
    cells out
    Assay region
    Organ 1
    Organ 2
    Tox response
    inflammation
    Dynamic range—106
    Sensitivity--high atmole
    5 minute measurement
    Jim Heath, et al
  • 44. Patient Assays that Explore New Dimensions of Data Space
  • 45. Individual Patient Information-Based Assays of the Present/ Future (I)
    Genomics
    Complete individual genome sequences—predictive health history—will be done sequencing families
    Complete individual cell genome sequences—cancer.
    Complete MHC chromosomal sequence in families—autoimmune disease and allegies
    200 Actionable single-nucleotide polymorphisms (SNPs)—pharmacogenetics-related and disease-related genes
    Sequence 1000 transcriptomessimultaneously in one DNA sequencing run from single cancer cells to identify quantized cells states and dissect cancer
    Analyze aging transcriptome profiles
    Proteomics
    2500 blood organ-specific blood proteins from 300 nanoliters of blood in 5 minutes—twice per year (50 proteins from 50 organs)—wellness assessment.
    Array of 13,000 human proteins—against autoimmune or allergic sera--stratify.
    Single molecule protein analyses—blood organ-specific proteins
  • 46. Individual Patient Information-Based Assays of the Present/ Future (II)
    Single cells
    Analyze 10,000 B cells and 10,000 T cells for the functional regions of their immune receptors—past and present immune responsiveness—follow vaccinations—identify autoimmune antibodies.
    Analyze individual blood macrophages—inflammation, etc.
    Use molecular-pore technology to separate epithelial cells from blood cells--cancer
    iPS (stem) cells
    Analyze individual stem (iPS) cells from each individual differentiated to relevant tissues to get important phenotypic information—molecular, imaging and higher level phenotypic measurements.
  • 47. 35
    Predictive, Personalized, Preventive and Participatory (P4) Medicine
    Driven by systems approaches to disease, new measurement (nanotechnology) and visualization technologies and powerful new computational tools, P4 medicine will emerge over the next 10-20 years
  • 48. P4 Medicine
    • Predictive:
    • 49. Probabilistic health history--DNA sequence
    • 50. Biannual multi-parameter blood protein measurements
    • 51. In vivo molecular imaging
  • P4 Medicine
    • Personalized:
    • 52. Unique individual human genetic variation mandates individual treatment
    • 53. Patient is his or her own control—longitudinal data
    • 54. Billions of data points on each individual
    • 55. 100s millions patients
    with billions data points
  • 56. P4 Medicine
    • Preventive:
    • 57. Design of therapeutic and preventive drugs
    via systems approaches
    • Systems approaches to vaccines will transform prevention of infectious diseases
    • 58. Transition to wellness assessment
  • P4 Medicine
    • Participatory:
    • 59. Patient understands and participates in medical choices
    • 60. Physicians trained before P4 will have to understand it
    • 61. Medical community—interconnected and educated
    • 62. Create IT for healthcare to handle billions of patients, each with billions of data points
  • Digitalization of Biology and Medicine Will Transform Medicine
    Analysis of single molecules, single cells, single organs and single individuals
    A revolution that will transform medicine even more than digitalization transformed information technologies and communications
    Digitization of medicine will lead to dramatically lower healthcare costs
    Single individual
    Single cell
    Single molecule
  • 63. ISB Strategic Partnerships for P4 Medicine
  • 64. ISB’s Two-Fold Strategy for P4 Medicine and Strategic Partnerships
    Inventing strategies, technologies and computational tools—ISB/Luxembourg
    Creating the P4 Medicine Institute to be an innovative advocate in bringing P4 medicine to patients--ISB/OSU
  • 65. The P4 Medicine Institute (ISB/OSU)
    Goal is to catalyze the P4 medical transformation of healthcare by:
    • Accelerating translation of systems science to clinical practice
    • 66. Executing tangible & pragmatic demonstration projects—lung cancer and wellness
    • 67. Developing public-private industry collaboration
    • 68. Addressing technical, strategic, operational, policy, economic, & sociologic issues
    Science & Technology
    +
    Policy & Industry
  • 69. Six Assertions About P4 Medicine
    P4 medicine is medicine of the present/near future
    Proactive P4 medicine is revolutionary rather than incremental or evolutionary—medicine is becoming an information science. Generate billions of data points on each individual.
    P4 medicine will transform the healthcare industry.
    P4 medicine will be effective and inexpensive.
    Pilot projects with informational assays in patient groups will be necessary to convince skeptics.
    The national healthcare debate in the future should be reframed around P4 medicine rather than the old reactive medicine.
  • 70. Acknowledgements
    Prion--Institute for Systems Biology
    Daehee Hwang
    Inyoul Lee
    HyuntaeYoo
    Eugene Yi (proteomics core facility)
    BruzMarzolf (Affymetrix core facility)
    Nanotechnology—protein chips, protein-capture agents--Jim Heath, Caltech
    MRM protein assays—R Moritz, R Aebersold
    Single-cell analyses—Leslie Chen and QiangTian
    Luxemburg Strategic Partnership—David Galas, Diane Isonaka, Rudi Balling (Lux)
    Prion--McLaughlin Research Institute
    Great Falls, Montana
    RanjitGiri
    Douglas Spicer
    Rajeev Kumar
    Rose Pitstick
    Rebecca Young
    George A. Carlson
    Family genome project—ISB/UW/Utah/Complete Genomics—David Galas
    P4MI Institute—Fred Lee, Clay Marsh (OSU)
    Single protein analysis—Chris Laustead