Preventing and Treating Chronic Disease - Francis Collins

3,609 views

Published on

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
3,609
On SlideShare
0
From Embeds
0
Number of Embeds
496
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • DO NOT DELETE THIS SLIDE OR ADD A TITLE
  • DO NOT DELETE THIS SLIDE OR ADD A TITLE
  • Readout from state-of-the-art, high-throughput genotyping (Illumina 5085, blueclusters) technologies Example of a typical readout from a genome-wide association (GWAS) study, which utilize data generated by high-throughput, discovery initiatives like the HapMap and 1000 Genomes Projects Readout from state-of-the-art, high-throughput genotyping (Illumina 5085) technologies Genotyping chip (Illumina SNP Chip) 2X zoom readout from state-of-the-art, high-throughput sequencing (454Data) technologies
  • Readout from state-of-the-art, high-throughput genotyping (Illumina 5085, blueclusters) technologies Example of a typical readout from a genome-wide association (GWAS) study, which utilize data generated by high-throughput, discovery initiatives like the HapMap and 1000 Genomes Projects Readout from state-of-the-art, high-throughput genotyping (Illumina 5085) technologies Genotyping chip (Illumina SNP Chip) 2X zoom readout from state-of-the-art, high-throughput sequencing (454Data) technologies
  • Aleksei Aksimentiev, Assistant Professor of Physics, University of Illinois Transport across cell membranes The ability of membrane channels to sort single molecules is of great interest in bioengineering and, as a result, membrane channels like alpha-hemolysin have been adopted for in vitro devices or used as an inspiration for manufacturing artificial channels. Thus, suspended in a lipid bilayer, an alpha-hemolysin channel becomes a stochastic sensor when a molecular adapter is placed inside its genetically re-engineered transmembrane pore, reporting via modulation of the transmembrane ionic current the type and the concentration of analytes entering the channel. The transmembrane pore of alpha-hemolysin can conduct not only small solutes, but also rather big (tens of kDa) linear macromolecules, including DNA and RNA strands. Using alpha-hemolysin as a prototypical beta-barrel membrane channel, Professor Aksimentiev is investigating transport of ions, nucleic acids and proteins across the cells boundaries. In collaboration with leading experimental groups, he has been conducting large scale molecular dynamics simulation of nucleic acids transport. In the future, he will apply this computational methodology to investigate translocation of proteins through membrane pores. http://physics.illinois.edu/people/profile.asp?aksiment
  • This negative-stained transmission electron micrograph (TEM) depicts the ultrastructural details of an influenza virus particle, or “virion”. A member of the taxonomic family Orthomyxoviridae, the influenza virus is a single-stranded RNA organism. http://phil.cdc.gov/phil/details.asp 3D graphical representation of a generic influenza virion’s ultrastructure. A portion of the virion’s outer protein coat has been cut away, which reveals the virus’ contents. Exterior: Light blue on a stem: Hemagglutinin Dark blue on a stem: Neuraminidase Light blue donuts: M2 Ion Channel Interior: Coiled structure: RNP http://phil.cdc.gov/phil/details.asp
  • Influenza viral spike (HA): Molecular model of the influenza virus spike and the site of the highly conserved region of vulnerability in the stem region. The spike, composed of a trimeric HA, consists of an upper (head) and lower (stem) region that mediates attachment and entry of influenza into cells of the respiratory tract. The sites of amino acid variability among influenza strains (red, <98% conserved) and the location of the highly conserved stem antibody site (yellow) are highlighted. (Nabel and Fauci, Nature Medicine , 12/2010)
  • HIV Tiles: HIV virions budding and releasing from an infected cell. Credit: NIAID, NIH HIV virus (CDC) HIV culture (CDC) ? Center: Scanning electron micrograph of HIV particles infecting a human T cell. Credit: NIAID, NIH
  • iPrEx images from study “infographic” (on website)
  • iPrEx images from study “infographic” (on website)
  • Arrow modified by speeches to reflect adherence statistics
  • NicVAX image from NABI Biopharmaceuticals
  • from the WHO Tobacco Atlas
  • Update from Leslie Cook (to RK, 12/23/10): “The answer is yes – Dr. Collins may use me as an example of the fact that NicVax is a great tool to help people quit once and for all.  I look forward to the day when it is widely available to the general public in the hopes that others like me will finally be able to rid themselves of a lethal addiction.”
  • DO NOT DELETE THIS SLIDE OR ADD A TITLE
  • Preventing and Treating Chronic Disease - Francis Collins

    1. www.genome.gov/gwastudies/
    2. DNA Sequencing
    3. DNA Sequencing
    4. Elevated RiskName Confidence Your Risk Avg. Risk Compared to AverageAtrial Fibrillation 33.9% 27.2% 1.25xType 2 Diabetes 29.9% 23.7% 1.26xProstate Cancer 22.6% 17.8% 1.27xAge-related Macular Degeneration 11.3% 7.0% 1.61xRestless Legs Syndrome 2.5% 2.0% 1.25xExfoliation Glaucoma 2.2% 0.7% 2.90xMultiple Sclerosis 0.5% 0.3% 1.37xEsophageal Squamous Cell 0.4% 0.4% 1.21xCarcinoma (ESCC)Stomach Cancer (Gastric Cardia 0.3% 0.2% 1.22xAdenocarcinoma)Bipolar Disorder 0.2% 0.1% 1.44x
    5. Elevated RiskName Confidence Your Risk Avg. Risk Compared to AverageAtrial Fibrillation 33.9% 27.2% 1.25xType 2 Diabetes 29.9% 23.7% 1.26xProstate Cancer 22.6% 17.8% 1.27xAge-related Macular Degeneration 11.3% 7.0% 1.61xRestless Legs Syndrome 2.5% 2.0% 1.25xExfoliation Glaucoma 2.2% 0.7% 2.90xMultiple Sclerosis 0.5% 0.3% 1.37xEsophageal Squamous Cell 0.4% 0.4% 1.21xCarcinoma (ESCC)Stomach Cancer (Gastric Cardia 0.3% 0.2% 1.22xAdenocarcinoma)Bipolar Disorder 0.2% 0.1% 1.44x
    6. Specific genetic risk factors for diabetes
    7. Universal Flu Vaccine
    8. HIVPrevention
    9. NicotineVaccine
    10. Deaths from Tobacco Use as percentage of total deaths among men and women over 35 2000 regional estimates Over 25% 10%–14% 20%–24% 5%–9% 15%–19% Under 5% MenWomen
    11. Clinical trial participantLeslie Cook, after 4 years:“I havent smoked a cigarette since. I dont want one.”

    ×