< Return to Video

Cracking the Code Of Life ✪ PBS Nova Documentary HD

  • 0:00 - 0:04
    (Narrator) When I look at this, and these
    are 3 billion
  • 0:04 - 0:07
    chemical letters. Instructions for a human
  • 0:07 - 0:10
    being, my eyes glaze over,
  • 0:10 - 0:10
    (laughing)
  • 0:10 - 0:13
    but when scientist, Eric Lander looks at
  • 0:13 - 0:15
    this, he sees stories.
  • 0:16 - 0:18
    (Eric) The genome is a storybook
  • 0:18 - 0:20
    that's been edited for a couple billion
  • 0:20 - 0:24
    years, and you couldn't take it
  • 0:24 - 0:26
    to bed like a 1,001 Arabian nights and
  • 0:26 - 0:28
    read a different story in the genome
  • 0:28 - 0:30
    every night.
  • 0:30 - 0:32
    (Narrator) This is the story of one of
  • 0:32 - 0:35
    the greatest scientific adventures ever.
  • 0:35 - 0:38
    And at the heart of it, is a small, very
  • 0:38 - 0:42
    powerful molecule, DNA.
  • 0:44 - 0:46
    For the past ten years, scientists
  • 0:46 - 0:48
    all over the world have been painstakingly
  • 0:48 - 0:50
    trying to read the tiny instructions
  • 0:50 - 0:53
    buried inside our DNA.
  • 0:53 - 0:55
    And now, finally, the human genome has
  • 0:55 - 0:56
    been decoded.
  • 0:57 - 0:59
    (Craig) We're at the moment the scientists
  • 0:59 - 1:01
    wait for. This is what we wanted to do.
  • 1:01 - 1:03
    We're now examining
  • 1:03 - 1:07
    and interpreting the genetic code.
  • 1:07 - 1:09
    (Francis) This is the ultimate
  • 1:09 - 1:11
    imaginable thing that one could do,
  • 1:11 - 1:14
    scientifically, is to go look at our
  • 1:14 - 1:15
    own instruction book, and then try
  • 1:15 - 1:17
    to figure out what it's telling us.
  • 1:17 - 1:20
    (Narrator) What it's telling us is so
  • 1:20 - 1:23
    surprising, and so strange, and so
  • 1:23 - 1:24
    unexpected.
  • 1:24 - 1:28
    (Narrator) 50% of the genes in a banana
  • 1:28 - 1:30
    are in us.
    (Eric) How different are you from a banana
  • 1:30 - 1:32
    I feel like I can say this with
  • 1:32 - 1:33
    some authority, very different
  • 1:33 - 1:36
    from a banana.
    (Eric) You may feel different from a
  • 1:36 - 1:37
    banana.
  • 1:37 - 1:40
    All the machinery for replicating your
    DNA, all the machinery for controlling
  • 1:40 - 1:46
    the cell-cycle, the cell surface, for
    making nutrients, all that's the same.
  • 1:47 - 1:50
    (Narrator) So what does any of this
    information have to do
  • 1:50 - 1:53
    with you, or me? Perhaps more than we
  • 1:53 - 1:55
    could possibly imagine.
  • 1:55 - 1:59
    Which one of us will get cancer, or
    arthritis, or Alzheimer's.
  • 2:00 - 2:05
    Will there be cures? Will parents in the
    future, be able to determine their
  • 2:05 - 2:07
    children's genetic destinies.
  • 2:08 - 2:09
    (Eric) We've opened the box here
  • 2:09 - 2:13
    that's got a huge amount of valuable
    information.
  • 2:13 - 2:17
    It is the key to understanding disease
  • 2:17 - 2:20
    and, in the long run, to curing disease.
  • 2:20 - 2:23
    But having opened it... we're also gonna
  • 2:23 - 2:24
    be very uncomfortable with that info
  • 2:24 - 2:26
    for some time to come.
  • 2:26 - 2:28
    (Narrator) Yes, some of the info
  • 2:28 - 2:29
    you're about to see will make you
  • 2:29 - 2:32
    very uncomfortable, on the other hand,
    some of it, I
  • 2:32 - 2:34
    think you will find amazing and hopeful.
  • 2:34 - 2:36
    I'm Robert Krulwich, and tonight, we will
  • 2:36 - 2:38
    not only report the latest discoveries of
  • 2:38 - 2:40
    the Human Genome Project, you will meet
  • 2:40 - 2:42
    the people who made those discoveries
  • 2:42 - 2:45
    possible, and who competed furiously to
  • 2:45 - 2:48
    be first to be done. And as you watch
  • 2:48 - 2:49
    our program on the human genome,
  • 2:49 - 2:51
    We will be raising a number of issues,
  • 2:51 - 2:55
    genes and privacy, genes and corporate
    profits,
  • 2:55 - 2:58
    genes and the odd similarity between you
  • 2:58 - 3:00
    and yeast. And we'd like to have your
  • 3:00 - 3:02
    thoughts on all these subjects, so please,
  • 3:02 - 3:05
    if you will, login to Nova's website. It's
  • 3:05 - 3:08
    located at PBS.org. It'll be there
    after the
  • 3:08 - 3:10
    broadcast, so do it after the
    broadcast,
  • 3:10 - 3:13
    where you can take a survey,
    the results will
  • 3:13 - 3:15
    be immediately available and continually
  • 3:15 - 3:18
    updated. We'll be right back.
  • 3:18 - 3:38
    ♪ (Intro music playing) ♪
  • 3:38 - 3:40
    (Man) Major funding for Nova is provided
  • 3:40 - 3:44
    by the Park Foundation, dedicated
    to education
  • 3:44 - 3:48
    and quality television.
  • 3:48 - 3:50
    (Man) This program is funded, in-part, by
  • 3:50 - 3:53
    the Northwestern Mutual Foundation.
  • 3:53 - 3:57
    Some people already know, Northwestern
    Mutual can help plan
  • 3:57 - 3:58
    for your children's education.
  • 3:58 - 4:00
    Are you there yet?
  • 4:00 - 4:03
    Northwestern Mutual Financial Network.
  • 4:06 - 4:09
    (Woman) Scientific achievement is fueled
  • 4:09 - 4:14
    by the simple desire to make things clear.
  • 4:15 - 4:18
    Sprint PCS is proud to support Nova.
  • 4:18 - 4:21
    (Man) Major funding for this program is
  • 4:21 - 4:25
    provided by, "The National Science
    Foundation."
  • 4:25 - 4:28
    - America's investment in the future.
  • 4:28 - 4:32
    (Man) And by the Corporation For Public
    Broadcasting
  • 4:32 - 4:34
    and by contributions to your PBS station
  • 4:34 - 4:36
    from viewers like you.
  • 4:36 - 4:40
    Thank you.
  • 4:58 - 5:03
    (Narrator) To begin, let's go back
    4-and-some billion years ago
  • 5:03 - 5:07
    to wherever it was that the first speck of
  • 5:07 - 5:10
    life appeared on Earth.
  • 5:10 - 5:14
    Maybe on the warm surface of a bubble.
  • 5:15 - 5:19
    That speck did something that's gone on,
    uninterrupted ever since.
  • 5:19 - 5:21
    It wrote a message. It was a chemical
  • 5:21 - 5:23
    message, that it passed to its children
  • 5:23 - 5:25
    and passed it on to its children, and
  • 5:25 - 5:28
    to its children, and so on.
  • 5:31 - 5:34
    The message has passed from the very first
  • 5:34 - 5:39
    organism, all the way down through time,
    to you and me.
  • 5:41 - 5:44
    Like a continuous thread, through all
  • 5:44 - 5:48
    living things.
  • 5:49 - 5:51
    It's more elaborate, now, of course, but
  • 5:51 - 5:54
    that message is, very simply, is the
  • 5:54 - 5:58
    secret of life.
  • 6:08 - 6:10
    And here is that message, contained in
  • 6:10 - 6:12
    this stunning little constellation of
  • 6:12 - 6:15
    chemicals, we call, "DNA".
  • 6:15 - 6:17
    You've seen it in this form, the classic
  • 6:17 - 6:19
    "Double Helix", but since we're gonna be
  • 6:19 - 6:21
    spending a lot of time talking about
  • 6:21 - 6:24
    DNA, I wondered what it looks like when
  • 6:24 - 6:26
    it's raw, ya know, in real life?
  • 6:26 - 6:29
    So, I asked an expert.
  • 6:29 - 6:31
    Eric Lander - DNA has a reputation for
  • 6:31 - 6:34
    being such a mystical, highfalutin sort of
  • 6:34 - 6:36
    molecule. All this information, your
  • 6:36 - 6:40
    future, your heredity, it's actually goop.
  • 6:40 - 6:42
    See, this here is DNA.
  • 6:42 - 6:44
    (Narrator) Professor Eric Lander is a
  • 6:44 - 6:47
    geneticist at MIT's Whitehead Institute.
  • 6:47 - 6:50
    (Eric) It's very very long strands of
  • 6:50 - 6:56
    molecules. These double helices of DNA,
    which, when you get them all
  • 6:56 - 6:59
    together, just look like little threads
    of cotton.
  • 6:59 - 7:01
    (Narrator) And these strands were
  • 7:01 - 7:05
    literally pulled from cells. Blood
    cells, maybe skin cells, of a human being.
  • 7:05 - 7:08
    Eric - Whoever contributed this DNA,
  • 7:08 - 7:10
    you can tell from this whether or not they
  • 7:10 - 7:12
    might be at early risk for Alzheimer's
  • 7:12 - 7:14
    disease. You can tell if they might be at
  • 7:14 - 7:16
    early risk for breast cancer.
  • 7:16 - 7:18
    And there's probably about 2,000 other
  • 7:18 - 7:20
    things you can tell, that we don't know
  • 7:20 - 7:21
    how to tell yet, but we'll be able
  • 7:21 - 7:24
    to tell. And it's, really, incredibly
    unlikely that you could tell
  • 7:24 - 7:25
    all that from this.
  • 7:25 - 7:28
    But, that's DNA for you.
  • 7:29 - 7:31
    That, apparently, is the secret of life,
  • 7:31 - 7:34
    just hanging off there on the tube.
  • 7:34 - 7:36
    (Narrator) And already, DNA has told
  • 7:36 - 7:40
    us things that no one, no one had
    expected.
  • 7:40 - 7:42
    It turns out that human beings have only
  • 7:42 - 7:46
    twice as many genes as a fruit fly.
  • 7:46 - 7:47
    Now how can that be?
  • 7:47 - 7:53
    We are such complex and magnificent
    creatures, and fruit flies, well, uh,
  • 7:53 - 7:55
    they're fruit flies.
  • 7:55 - 8:00
    DNA also tells us that we are more closely
    related to worms and to yeast than
  • 8:00 - 8:03
    most of us would ever have imagined.
  • 8:03 - 8:09
    But how do you read what's inside a
    molecule?
  • 8:09 - 8:12
    Well, if it's DNA, if you turn it so you
  • 8:12 - 8:15
    can look at from just the right angle,
  • 8:15 - 8:18
    you will see, in the middle, what
  • 8:18 - 8:21
    look like steps in a ladder.
  • 8:21 - 8:23
    Each step is made up of two chemicals,
  • 8:23 - 8:27
    "Cytosine" and "Guanine", or
    "Thymine" and "Adenine".
  • 8:27 - 8:35
    They come, always, in pairs called,
    "Base-Pairs" either C and G, or T and A,
  • 8:35 - 8:36
    for short.
  • 8:36 - 8:39
    This is, step-by-step, a code.
  • 8:39 - 8:43
    Three-billion steps long; the formula for
  • 8:43 - 8:47
    a human being.
  • 8:47 - 8:50
    We're all familiar with this thing.
  • 8:50 - 8:52
    This shape is very familiar.
  • 8:52 - 8:54
    (Eric) Double Helix.
    (Narrator) Double Helix.
  • 8:54 - 8:56
    First of all, I want to... this is my
  • 8:56 - 8:57
    version of a DNA molecule.
  • 8:57 - 9:00
    Is this, by the way, what it looks like?
  • 9:00 - 9:02
    (Eric) Well, ehm, give or take... a
    cartoon version of it.
  • 9:02 - 9:05
    I mean, a little like that, yeah.
  • 9:05 - 9:09
    (Narrator) So there are, in almost every
    cell in your body, if you look deep
  • 9:09 - 9:12
    enough, you will find this chain here?
  • 9:12 - 9:14
    Eric - Oh, yes, stuck in the nucleus of
    your cell.
  • 9:14 - 9:16
    Narrator - Now how small is this?
  • 9:16 - 9:19
    In a real DNA molecule, the distance
  • 9:19 - 9:21
    between the two walls is how wide?
  • 9:21 - 9:23
    Eric - Oh, golly.
  • 9:23 - 9:25
    (Narrator) Look at this, he's asking for
    help.
  • 9:25 - 9:26
    (laughing)
  • 9:26 - 9:31
    Eric -This distance is about
    10 Angstroms.
  • 9:31 - 9:33
    (Narrator) That's one-billionth of a meter
  • 9:33 - 9:35
    when it's clumped up in a very particular
    way.
  • 9:35 - 9:39
    Eric - Well, no, it's curled up sort of
    like that, but, it's more than that.
  • 9:39 - 9:42
    You can't curl it up too much, bc
    these little negative charge things
  • 9:42 - 9:45
    will repel each other. So, you fold it on
    itself.
  • 9:45 - 9:46
    I'm gonna break your molecule..
  • 9:46 - 9:48
    Narrator - Yeah, don't break my molecule.
  • 9:48 - 9:51
    Eric - Ya know, ya got this
    and then it's folded up like this
  • 9:51 - 9:53
    and then those are folded up on top
  • 9:53 - 9:55
    of each other. And, so, in fact, if you
  • 9:55 - 9:56
    were to stretch out all of the DNA, it
  • 9:56 - 9:59
    would run, oh I don't know, thousands
  • 9:59 - 10:00
    and thousands of feet.
  • 10:00 - 10:01
    Narrator - Okay.
  • 10:01 - 10:02
    The main thing about this, is
  • 10:02 - 10:04
    the ladder, the steps of this ladder.
  • 10:04 - 10:08
    Narrator - If I knew it was "A" and "T"
    and "C" and "C" and "G" and "G" and "A"...
  • 10:08 - 10:12
    Eric - Oh, no, it's not "'G' and 'G', it's
    'G' and 'C'. It's the grammar...
  • 10:12 - 10:15
    Narrator - If I could read each of the
  • 10:15 - 10:17
    individual ladders, I might find the
  • 10:17 - 10:20
    picture of... what?
  • 10:20 - 10:21
    Eric - Of your children.
  • 10:21 - 10:23
    This is what you pass to your children.
  • 10:23 - 10:27
    Ya know, people have known for 2,000
    years that your kids look a lot
  • 10:27 - 10:29
    like you. Well, it's because you must pass
  • 10:29 - 10:31
    them something... Some instructions that
  • 10:31 - 10:33
    give them the eyes they have and hair
  • 10:33 - 10:36
    they have, and the nose shape they do.
  • 10:36 - 10:37
    The only way you pass it to them is in
  • 10:37 - 10:40
    these sentences. That's it.
  • 10:40 - 10:42
    (Narrator) And to show you the true power
  • 10:42 - 10:44
    of this molecule, we're gonna start with
  • 10:44 - 10:47
    one atom, deep inside.
  • 10:47 - 10:51
    We pull back and see it form its "A"'s and
    "T"'s and "C"'s and "G"'s and the
  • 10:51 - 10:56
    classic double spiral....
  • 11:00 - 11:02
    And then starts the mysterious process
  • 11:02 - 11:06
    that creates a healthy new baby.
  • 11:06 - 11:35
    ♪ (music playing) ♪
  • 11:46 - 11:49
    And the interesting thing is that every
  • 11:49 - 11:53
    human baby, every baby born, is 99.9%
  • 11:53 - 11:56
    identical in it's genetic code to every
  • 11:56 - 11:58
    other baby.
  • 11:58 - 12:02
    So, the tiniest differences in our genes
  • 12:02 - 12:04
    can be hugely important.
  • 12:04 - 12:06
    Can contribute to differences in height,
  • 12:06 - 12:09
    physique, maybe even talents, aptitudes,
  • 12:09 - 12:13
    and it also explain what can break...
  • 12:13 - 12:17
    What can make us sick.
  • 12:17 - 12:19
    Cracking the code of those minuscule
  • 12:19 - 12:22
    differences in DNA, that influence health
  • 12:22 - 12:26
    and illness, is what the Human Genome
    Project is all about.
  • 12:27 - 12:31
    Since 1990, scientists all over the world
  • 12:31 - 12:33
    in university and government labs have
  • 12:33 - 12:35
    been involved in a massive effort to read
  • 12:35 - 12:38
    all three-billion A's, T's, G's, and C's
  • 12:38 - 12:40
    of human DNA.
  • 12:40 - 12:42
    They predicted it would take, at least
  • 12:42 - 12:48
    15 years. That was partly because, in the
    early days of the project,
  • 12:48 - 12:51
    a scientist could spend years, an entire
  • 12:51 - 12:54
    career, trying to readjust a handful of
  • 12:54 - 12:56
    letters in the human genome.
  • 12:56 - 12:59
    It took 10 years to find the one genetic
  • 12:59 - 13:02
    mistake that causes Cystic Fibrosis.
  • 13:02 - 13:05
    Another 10 years to find the gene for
  • 13:05 - 13:06
    Huntington's Disease.
  • 13:06 - 13:09
    15 years to find one of the genes that
  • 13:09 - 13:12
    increase the risk for breast cancer.
  • 13:12 - 13:17
    One letter at a time, painfully slowly,
  • 13:17 - 13:21
    frustratingly prone to mistakes, and
  • 13:21 - 13:24
    false leads.
  • 13:24 - 13:27
    We asked Robert Waterston, a pioneer in
  • 13:27 - 13:28
    mapping DNA, to show us the way it
  • 13:28 - 13:30
    used to be done.
  • 13:30 - 13:32
    (Robert) The original ladders for DNA
  • 13:32 - 13:36
    sequence, we actually read by putting a
  • 13:36 - 13:38
    little letter next to the band that we
  • 13:38 - 13:40
    were calling and then writing those
  • 13:40 - 13:42
    down on a piece of paper, or into the
  • 13:42 - 13:44
    computer after that.
  • 13:44 - 13:47
    Uh, it's horrendous.
  • 13:47 - 13:49
    (Narrator) And we haven't mentioned the
  • 13:49 - 13:53
    hardest part. This part here, magnified
    50 thousand times, is an actual
  • 13:53 - 13:57
    clump of DNA Chromosome -17.
  • 13:57 - 13:59
    If you look inside, you find, of course,
  • 13:59 - 14:03
    hundred of millions of A's, and C's, and
    T's, and G's, but it turns out
  • 14:03 - 14:07
    that only about 1% of them are active
  • 14:07 - 14:09
    and important. These are the genes
  • 14:09 - 14:11
    that scientists are searching for.
  • 14:11 - 14:13
    So, somewhere in this dense chemical
  • 14:13 - 14:15
    forest, are genes involved in deafness,
  • 14:15 - 14:19
    Alzheimer's, cancer, cataracts, but where?
  • 14:19 - 14:23
    This is such a maze, scientists need a
    map.
  • 14:23 - 14:26
    But at the old pace, that would take close
  • 14:26 - 14:29
    to forever.
  • 14:29 - 14:33
    And then came the revolution.
  • 14:34 - 14:36
    In the last 10 years the entire process
  • 14:36 - 14:40
    has been computerized, that costed
  • 14:40 - 14:44
    hundreds of millions of dollars.
  • 14:45 - 14:47
    But now, instead of decoding only a few
  • 14:47 - 14:50
    hundred letters by hand in a day...
  • 14:50 - 14:54
    together, these machines can do 1,000
    every second.
  • 14:55 - 14:59
    And that has made all the difference.
  • 14:59 - 15:01
    (Man) This is something that is gonna
  • 15:01 - 15:03
    go in the textbooks. Everybody knows
    that.
  • 15:03 - 15:05
    Everybody, when the Genome Project was
  • 15:05 - 15:09
    being born, was consciously aware of their
  • 15:09 - 15:11
    role in history.
  • 15:11 - 15:16
    (Narrator) Getting the letters out is, has
    been described as, finding the
  • 15:16 - 15:18
    blueprint of a human being, finding
  • 15:18 - 15:21
    the manual for a human being,
  • 15:21 - 15:24
    finding the code of a human being.
  • 15:24 - 15:26
    What's your metaphor?
  • 15:26 - 15:29
    Eric - Oh, golly gee. I mean, you can have
  • 15:29 - 15:31
    very highfalutin metaphors for this kind
  • 15:31 - 15:33
    of stuff. This is basically a parts list.
  • 15:33 - 15:35
    Blueprints and all these fancy names...
  • 15:35 - 15:37
    it's just a parts list.
  • 15:37 - 15:40
    It's just a parts list with a lot of
    parts.
  • 15:40 - 15:42
    If you take an airplane, a Boeing 777,
  • 15:42 - 15:44
    I think it has like a hundred-thousand
  • 15:44 - 15:46
    parts. If I gave you a parts list for the
  • 15:46 - 15:49
    Boeing 777, in one sense you'd know a
  • 15:49 - 15:51
    lot. You'd know a hundred-thousand
  • 15:51 - 15:52
    components that have gotta be there.
  • 15:52 - 15:54
    Screws and wires and rudders and things
  • 15:54 - 15:57
    like that. On the other hand, I bet you
  • 15:57 - 15:59
    wouldn't know how to put it together and
  • 15:59 - 16:01
    I bet you wouldn't know why it flies.
  • 16:01 - 16:02
    Well, we're in the same boat... We now
  • 16:02 - 16:06
    have a parts list. That's what The Human
    Genome Project is about.
  • 16:06 - 16:07
    It's about getting the parts list.
  • 16:07 - 16:09
    If you want to understand the plane,
  • 16:09 - 16:11
    you have to have the parts list, but
  • 16:11 - 16:13
    that's not enough to understand why
  • 16:13 - 16:14
    it flies. But, of course, you'd be crazy
  • 16:14 - 16:16
    not to start with the parts list.
  • 16:16 - 16:19
    And one reason it’s so important
    to understand all those parts is
  • 16:19 - 16:22
    to decode every letter of the genome,
  • 16:22 - 16:28
    Is because sometimes out of 3 billion base
    pairs in our DNA, just one single letter
  • 16:28 - 16:32
    can make a difference.
  • 16:36 - 16:40
    Alice and Tim Lord are parents of
    two-year-old Hayden,
  • 16:41 - 16:45
    Alice – good morning
    Tim – hey pumpkin
  • 16:48 - 16:52
    (Tim) – the two things that I think of the
    most about Hayden, which a lot of people .
  • 16:52 - 16:57
    got from him right from the beginning
    was that he was always very funny
  • 16:58 - 17:02
    Alice - Make your very very very serious
    face. I love you
  • 17:03 - 17:08
    (Tim) – he loved to smile and laugh,
    he used to guffaw this was later when he
  • 17:08 - 17:13
    was a year old he just found the funniest
    things hilarious he and I would just crack
  • 17:13 - 17:18
    each other up.
  • 17:20 - 17:25
    (Narrator) Hayden seemed to be developing
    normally but Allison began to notice
  • 17:25 - 17:32
    that some things were not quite right.
    Allison – I was very anxious all the time
  • 17:32 - 17:37
    with Hayden. I was certain things were
    not the same. I would see friends
  • 17:37 - 17:40
    changing the diaper of their child
    that was around the same age
  • 17:40 - 17:44
    and see the physical movement
    and the legs movement and
  • 17:44 - 17:47
    Hayden didn’t do that
  • 17:47 - 17:50
    ♪ (Singing “Happy Birthday”) ♪
  • 17:50 - 17:54
    Narrator – Doctors told them that Hayden
    was just developing a bit slowly, but
  • 17:54 - 17:58
    by the time he turned a year old, it was
    clear that something serious was wrong.
  • 18:03 - 18:07
    he never crawled, he never talked,
    never ate with his fingers,
  • 18:07 - 18:11
    and he seemed to be going backwards, not
    progressing.
  • 18:11 - 18:15
    (Tim) – I remember the last time he laughed.
  • 18:15 - 18:19
    I took a trip with him out to buy a suit
    to a wedding that night
  • 18:19 - 18:24
    and we came back and it was really windy,
    he just loves to fill the wind,
  • 18:24 - 18:30
    so we had a great time. We came back and
    I brought them up on the couch and sat
  • 18:30 - 18:34
    next to him and he just kind of threw his
    head back and laughed.
  • 18:34 - 18:36
    Like, oh, what a fun trip! Ya, know...
  • 18:36 - 18:43
    It was the last time he was able to
    laugh, it’s really hard.
  • 18:56 - 19:00
    (Narrator) – it turned out that Hayden
    had Tay-Sachs disease. A genetic
  • 19:00 - 19:03
    condition that slowly destroys
    a baby’s brain
  • 19:05 - 19:09
    (Kolodny) – What happens is a baby appears
    normal at birth,
  • 19:09 - 19:16
    and over the course of the first year,
    begins to miss developmental milestones.
  • 19:18 - 19:20
    So at six months a child should be turning
  • 19:20 - 19:26
    over but is unable to sit up, to stand, to
    walk, to talk.
  • 19:28 - 19:33
    (Narrator) – Tay-Sachs disease begins at
    one infinitesimal spot on the DNA ladder
  • 19:33 - 19:38
    if just one letter goes wrong, say this
    cluster of atoms is a picture of that
  • 19:38 - 19:43
    letter. A mistake here can come
    down to just four atoms, that’s it.
  • 19:44 - 19:49
    But since genes create proteins,
    that error creates a problem
  • 19:49 - 19:54
    in this protein, which is supposed to
    dissolve fat in the brain. And now
  • 19:54 - 19:59
    the protein doesn’t work, so fat
    builds up, swells the brain, and
  • 19:59 - 20:05
    destroys critical brain cells. And
    all of this is the result of one
  • 20:05 - 20:08
    bad letter in that baby’s DNA.
  • 20:10 - 20:14
    Kolodny – in most cases it’s a single
    base change as we say, a letter
  • 20:14 - 20:17
    difference.
  • 20:17 - 20:22
    (Narrator) – One defective letter out
    of 3 billion. And no way to fix it.
  • 20:22 - 20:29
    Tay-Sachs is a relentlessly progressive
    disease. In the years since his
  • 20:29 - 20:37
    diagnosis Hayden has gone blind, can’t
    eat solid food, it’s harder for him to
  • 20:37 - 20:43
    swallow, he can’t move on his own,
    and he has seizures as many as 10
  • 20:43 - 20:45
    times a day.
  • 20:45 - 20:49
    Kolodny – for children with classical
    Tay-Sachs disease, there is only one
  • 20:49 - 20:59
    outcome. Children die by age 5 to 7.
    Sometimes even before age 5.
  • 21:00 - 21:05
    (Narrator) – as it happens, Tim has an
    identical twin brother. When Hayden
  • 21:05 - 21:09
    was diagnosed, that brother, Charlie
    went to New York to be with him.
  • 21:12 - 21:18
    Charlie’s wife Blythe had had been
    Allison’s roommate in college and
  • 21:18 - 21:22
    her best friend.
  • 21:22 - 21:27
    Blythe – Charlie called me on the phone
    and told me to Hayden had Tay-Sachs
  • 21:27 - 21:32
    and explained I went to the computer and
    looked it up and then just couldn’t
  • 21:32 - 21:37
    believe what I read.
    (Blythe and Taylor talking)
  • 21:38 - 21:41
    (Narrator) – Blythe and Charlie had a
    three-year-old daughter Taylor and a
  • 21:41 - 21:45
    baby girl named Cameron. Cameron
    was happy and healthy except for one
  • 21:45 - 21:51
    small thing.
    Blythe – on the NTSAD website it talks
  • 21:51 - 21:54
    about that typically between six and
    eight months is when the signs start
  • 21:54 - 21:59
    coming. But one of the early signs is
    that they startle easily and Hayden
  • 21:59 - 22:04
    had always had a heavy startle
    response. We noticed that Cameron
  • 22:04 - 22:12
    had a comparable startle response.
    Not as severe, but absolutely not
  • 22:12 - 22:16
    like Taylor had had.
    (Narrator) – as soon as she saw
  • 22:16 - 22:20
    that early warning sign on the Tay-Sachs
    website, Blythe went to get herself and
  • 22:20 - 22:25
    Cameron tested.
    (Charlie) – it was another week until we
  • 22:25 - 22:29
    got the final results on Cameron’s
    bloodwork. And then the Tuesday before
  • 22:29 - 22:33
    Thanksgiving, we went into our
    pediatrician’s office we had the results
  • 22:33 - 22:39
    that Blythe was a carrier and Cameron
    had Tay-Sachs
  • 22:39 - 22:51
    Blythe – all I said was “I’m sorry.”
    (Narrator) – Tay-Sachs is a very rare
  • 22:51 - 22:56
    disease that usually occurs in specific
    groups, like Ashkenazi Jews, but even
  • 22:56 - 23:02
    then the baby must inherit the gene
    from both parents. So even though
  • 23:02 - 23:06
    there is a Tay-Sachs test, the Lords had
    no reason to think they would be a risk.
  • 23:11 - 23:19
    And yet incredibly all four of them, Tim
    and Charlie and both their wives, were
  • 23:19 - 23:25
    carriers. That was an unbelievably bad
    role of the genetic dice.
  • 23:27 - 23:35
    Tim – Charlie and I are incredibly close
    and have been all our lives. And when I
  • 23:35 - 23:40
    think about him and Blythe having to go
    through this it seems really cruel
  • 23:44 - 23:51
    Charlie – I had already geared myself up
    to be my brother’s rock. And I couldn’t
  • 23:51 - 24:01
    imagine having to help him and go
    through it myself voice breaks
  • 24:05 - 24:10
    (Narrator) – for families like the Lords
    and for everybody, the Human Genome
  • 24:10 - 24:14
    Project offers the chance to find out
    early if we’re at risk for all kinds of
  • 24:14 - 24:21
    diseases.
    Tim Lord - I would like to see a really
  • 24:21 - 24:28
    aggressive push to develop a test for
    hundreds of genetic diseases, so that
  • 24:28 - 24:32
    parents could be
    informed before they start to have
  • 24:32 - 24:42
    children as to the dangers that faced
    them. I think it’s within our grasp, now
  • 24:42 - 24:46
    that we’ve mapped the human genenome,
    the information is there for people to
  • 24:46 - 24:56
    begin to sort through
    Tim – (talking to Hayden), “all right
  • 24:56 - 25:00
    pumpkin”
    (Tim) – they are horrible horrible
  • 25:00 - 25:05
    diseases. If there is any way you could
    be tested for a whole host of them and
  • 25:05 - 25:09
    not have them
    affect the child, I think it’s something
  • 25:09 - 25:13
    that we have to focus on
  • 25:20 - 25:26
    (Narrator) – Hayden Lord died a few months
    before his third birthday.
  • 25:28 - 25:30
    Narrator - what makes this story
  • 25:30 - 25:32
    especially hard to bear is we now know
  • 25:32 - 25:35
    that a loss that huge, and it was a
  • 25:35 - 25:37
    catastrophe by any measure, started
  • 25:37 - 25:41
    with a single error, a few atoms across,
  • 25:41 - 25:44
    buried inside a cell. Now, if something
  • 25:44 - 25:47
    so small could trigger such an enormous
  • 25:47 - 25:50
    result, is a perspective that is
  • 25:50 - 25:52
    incredibly frightening.
  • 25:52 - 25:54
    Except, that now geneticists have figured
  • 25:54 - 25:56
    out how to see many of these tiny errors
  • 25:56 - 25:59
    before they become catastrophes.
  • 25:59 - 26:01
    When you think about that, that's an
  • 26:01 - 26:04
    extraordinary thing, to spot a catastrophe
  • 26:04 - 26:07
    when it's still an insignificant dot in a
    cell.
  • 26:07 - 26:10
    Which is the promise of the Human Genome
    Project.
  • 26:10 - 26:13
    It is first and foremost, an early warning
  • 26:13 - 26:15
    system for a host of diseases, which
  • 26:15 - 26:19
    will give, hopefully, parents, doctors,
    and scientists an advantage
  • 26:19 - 26:21
    that we have never had before. When you
  • 26:21 - 26:24
    can see trouble coming way way before
  • 26:24 - 26:26
    it starts you have a chance to stop it
  • 26:26 - 26:30
    or treat it... eventually, you might cure
    it.
  • 26:31 - 26:33
    (Narrator) And that's why, when Congress
  • 26:33 - 26:36
    created the Human Genome Project in
    1990, the challenge was
  • 26:36 - 26:42
    to get a complete list of our A's, T's,
    C's, and G's as quickly as possible.
  • 26:42 - 26:44
    So, the business of making tests,
  • 26:44 - 26:48
    medicines, and cures could begin.
  • 26:48 - 26:51
    They figured it would take about 15 years
  • 26:51 - 26:53
    to decode a human being, and at the time,
  • 26:53 - 26:56
    that seemed reasonable.
  • 26:56 - 26:58
    Until this man: scientist, entrepreneur,
  • 26:58 - 27:01
    and speed-boat enthusiast, Craig Venter,
  • 27:01 - 27:05
    decided that he could do it faster, much
    faster.
  • 27:05 - 27:07
    (Craig) It's like sailing. Once you have
  • 27:07 - 27:09
    two sailboats on the water going
  • 27:09 - 27:11
    approximately the same direction,
  • 27:11 - 27:13
    they're racing. And science works very
  • 27:13 - 27:17
    much the same way. If you have two labs
  • 27:17 - 27:20
    Craig - remotely working on the same
    thing,
  • 27:20 - 27:24
    one tries to get there faster, better, or
  • 27:24 - 27:29
    higher quality... something different,
    in part, because our society
  • 27:29 - 27:32
    recognizes only "First Place".
  • 27:32 - 27:34
    (Narrator) Back in 1990, Vinter was one
  • 27:34 - 27:38
    of many government scientists
    painstakingly decoding
  • 27:38 - 27:41
    proteins and genes, his focus was one
  • 27:41 - 27:43
    protein in the brain.
  • 27:43 - 27:46
    Vinter - I took 10 years to get the
    protein, and it took a whole year
  • 27:46 - 27:48
    to get 1,000 letters of genetic code.
  • 27:48 - 27:51
    (Narrator) For Venter, that was way too
    slow.
  • 27:51 - 27:54
    Narrator - So, you're sitting there
    thinking there must be
  • 27:54 - 27:55
    a better way when you were gazing out
  • 27:55 - 27:56
    the window.
  • 27:56 - 27:58
    Vinter - There HAD to be a better way.
  • 27:58 - 28:00
    (Narrator) And that's when he learned that
  • 28:00 - 28:04
    someone had invented a new machine
    that could identify C's and T's and A's
  • 28:04 - 28:05
    and G's with remarkable speed.
  • 28:05 - 28:09
    And Craig Venter just loves machines that
  • 28:09 - 28:10
    go fast.
  • 28:10 - 28:12
    Vinter - I immediately contacted the
  • 28:12 - 28:15
    company to see if I could get one of the
    first machines.
  • 28:15 - 28:17
    (Narrator) - And here's how they work:
  • 28:17 - 28:20
    Human DNA is chopped by robots into tiny
  • 28:20 - 28:23
    pieces. These pieces are copied over and
  • 28:23 - 28:26
    over again in bacteria and then tagged
  • 28:26 - 28:30
    with colored dyes.
  • 28:30 - 28:34
    A laser bounces light off of each snip of
  • 28:34 - 28:37
    DNA and the colors that it sees represent
  • 28:37 - 28:40
    individual letters in the genetic code.
  • 28:40 - 28:43
    And these computers can do this 24 hours
  • 28:43 - 28:46
    a day, everyday.
  • 28:46 - 28:49
    Venter - See, now you can see clearly the
    peaks.
  • 28:49 - 28:51
    So, there's just a blue on coming up so
  • 28:51 - 28:53
    that's a "C" coming up. You could read
  • 28:53 - 28:56
    this and you could write this all down.
  • 28:56 - 28:59
    (Narrator) So, blue, yellow, red, red,
    yellow...
  • 28:59 - 29:03
    Venter - So, that's C, G, T, T, A.
  • 29:03 - 29:05
    (Narrator) And, somehow all these little
  • 29:05 - 29:10
    pieces have to be put together again in
    the right order. Venter's dream was to
  • 29:10 - 29:12
    have hundreds of new machines at his
  • 29:12 - 29:15
    fingertips, so he quit is government job
  • 29:15 - 29:17
    and formed a company he called,
  • 29:17 - 29:20
    "Celera Genomics". "Celera", from the
  • 29:20 - 29:23
    Latin word, "Celerity", meaning, "Speed".
  • 29:23 - 29:26
    And this is what he built.
  • 29:26 - 29:29
    Narrator - Oh my Lord. And you know what's
  • 29:29 - 29:31
    interesting is there's almost nobody here.
  • 29:31 - 29:35
    Vinter - Yeah. It's all automated.
  • 29:35 - 29:37
    (Narrator) So, who is this guy and why's
  • 29:37 - 29:40
    he such a bulldog for speed?
  • 29:40 - 29:43
    Craig Venter grew up in California,
  • 29:43 - 29:45
    left high school and spent a year as a
  • 29:45 - 29:48
    surfing bum on the beach by day and
  • 29:48 - 29:51
    a stock-boy at Sears by night. He was
  • 29:51 - 29:53
    inevitably drafted, went to Vietnam with
  • 29:53 - 29:56
    the Navy. That's him way over there on
  • 29:56 - 29:58
    the left. He was eventually assigned to a
  • 29:58 - 30:00
    Naval Hospital in Denang during the
  • 30:00 - 30:02
    Tet Offensive, when the Americans were
  • 30:02 - 30:04
    taking very heavy casualties.
  • 30:04 - 30:06
    At 21, he was in the triage unit, where
  • 30:06 - 30:10
    they decide who will live, and who will
    die.
  • 30:10 - 30:11
    When you're young and you
  • 30:11 - 30:13
    Narrator - see a lot of people die, and
  • 30:13 - 30:15
    they could all be you, do feel like you
  • 30:15 - 30:20
    sort of owe them cures, cures that they'll
  • 30:20 - 30:23
    never get? Or am I over-romanticizing...
  • 30:23 - 30:24
    Vinter - Well, the motivation's become
  • 30:24 - 30:28
    complex, but that's certainly apart of it.
  • 30:28 - 30:33
    Also, I think surviving the year there
    was...
  • 30:33 - 30:37
    (emotional pause)
  • 30:44 - 30:46
    So, it puts things in perspective that
  • 30:46 - 30:49
    I think, if you're not in that situation,
  • 30:49 - 30:53
    you could never truly have that
    perspective.
  • 30:53 - 30:56
    Narrator - So, you hear ticking?
  • 30:56 - 31:00
    Vinter - Yeah... but also, I feel, that
  • 31:00 - 31:02
    I've had this tremendous gift for all
  • 31:02 - 31:05
    these years since I got back in 1968, and
  • 31:05 - 31:07
    I wanted to make sure I did something
  • 31:07 - 31:10
    with it.
  • 31:11 - 31:13
    (Narrator) In the spring of 1998, Venter
  • 31:13 - 31:15
    announced that he and his company were
    gonna
  • 31:15 - 31:17
    sequence all three billion letters of the
  • 31:17 - 31:20
    human genome in two years.
  • 31:20 - 31:21
    Remember, the government said it
  • 31:21 - 31:23
    was gonna take 15.
  • 31:23 - 31:25
    (Venter) There was a lot of arrogance
  • 31:25 - 31:28
    that went with that program
  • 31:28 - 31:30
    Vinter - they were gonna do it at their
  • 31:30 - 31:33
    pace, and a lot of the scientists, ya
    know, if they were really
  • 31:33 - 31:35
    being honest with you, would tell you that
  • 31:35 - 31:37
    they planned to retire doing this program.
  • 31:37 - 31:40
    You know, that's not what we think is the
  • 31:40 - 31:42
    right way to do science, especially
  • 31:42 - 31:44
    science that affects so many peoples'
    lives.
  • 31:44 - 31:47
    Robert - Craig's a high-testosterone male.
  • 31:47 - 31:51
    Who has, who loves to be an iconoclast,
    right?
  • 31:51 - 31:54
    He loves rattling peoples' cages. And he's
  • 31:54 - 31:57
    done that consistently in the genome
    project.
  • 31:58 - 32:00
    (Narrator) Craig Venter's announcement
  • 32:00 - 32:01
    that his team would finish the
  • 32:01 - 32:05
    entire genome in just two years,
    galvanized everybody working
  • 32:05 - 32:08
    on the public project. Now, they were
  • 32:08 - 32:10
    scrambling to keep up.
  • 32:10 - 32:12
    Man - There are some limitations,
  • 32:12 - 32:15
    we don't think we can this thing to go
    any faster at the moment without
  • 32:15 - 32:19
    throwing a lot more robotics at it.
    The arm physically takes 20 seconds
  • 32:19 - 32:19
    to move...
  • 32:19 - 32:23
    (Narrator) Francis Collins, the head of
    the Human Genome Project, was
  • 32:23 - 32:26
    determined that Celera was not gonna
    beat his teams to the prize. He made a
  • 32:26 - 32:31
    dramatic decision to try to cut five full
    years off of the original plan.
  • 32:31 - 32:33
    Eric - The okay way to do it...
  • 32:33 - 32:38
    (Francis) When the major genome
    centers met, and agreed to go for broke
  • 32:38 - 32:41
    here, I don't think there was anybody in
  • 32:41 - 32:43
    the room that was very confident we could
  • 32:43 - 32:45
    do that. I mean, you could sit down with
  • 32:45 - 32:47
    a piece of paper, and make projections,
  • 32:47 - 32:49
    if everything went really well, that might
  • 32:49 - 32:51
    get you there, but there were so many ways
  • 32:51 - 32:55
    this could have just run completely
    off the track.
  • 32:55 - 32:57
    (Narrator) At MIT, they decided to try to
  • 32:57 - 33:00
    scale up their effort 15-fold.
  • 33:00 - 33:02
    And that meant a major change in their
  • 33:02 - 33:04
    usual academic pace.
  • 33:04 - 33:06
    (Woman) We basically had a goal since
  • 33:06 - 33:10
    Woman - march to get to a plate emitted
    operation from womb-to-tomb all the
  • 33:10 - 33:11
    way through.
  • 33:11 - 33:14
    (Narrator) In the fall of 1999,
    representatives from the five
  • 33:14 - 33:18
    major labs come to check out Eric
    Lander's operation.
  • 33:18 - 33:21
    All the big honchos in the Human
    Genome Project are here.
  • 33:21 - 33:23
    Scientists from Washington University
  • 33:23 - 33:26
    in St. Louis, Baylor College of Medicine
  • 33:26 - 33:29
    in Texas, the Department of Energy,
  • 33:29 - 33:32
    she's from the Sanger Center in England.
  • 33:32 - 33:34
    If they want to finish the genome before
  • 33:34 - 33:36
    Craig Venter, these folks have to figure
  • 33:36 - 33:38
    out how to outfit their labs with a lot of
  • 33:38 - 33:42
    new, and fancy, and unfamiliar equipment,
  • 33:42 - 33:45
    and they've got to do it fast.
  • 33:45 - 33:47
    Woman - So, we'll have to run some kind
  • 33:47 - 33:48
    of a conduit...
  • 33:48 - 33:50
    (Narrator) At MIT, a different crate is
  • 33:50 - 33:52
    arriving almost daily.
  • 33:52 - 33:55
    Man - It's like Christmas, everyone
    unwrap something.
  • 33:55 - 33:57
    (Narrator) Just like a bad Christmas
  • 33:57 - 33:59
    present, assembly is required, and the
  • 33:59 - 34:04
    instruction are, of course, not always
    clear.
  • 34:04 - 34:06
    (Man) Oh no, the magnet-plates stick
    to each other?
  • 34:06 - 34:10
    Man - This is about, plus or minus three
    feet.
  • 34:10 - 34:10
    (laughing)
  • 34:10 - 34:14
    (Eric Landers) Since one's on the cutting-
    edge, I guess that they always call
  • 34:14 - 34:16
    Eric - it the "bleeding edge", right?
    Nothing,
  • 34:16 - 34:20
    really, is working as you would expect.
    All the stuff we're doing will be working
  • 34:20 - 34:22
    perfectly as soon as we're ready to junk
    it.
  • 34:22 - 34:25
    (Man) Right, right.
    (Narrator) The MIT crew is particularly
  • 34:25 - 34:27
    excited about their brand-new,
  • 34:27 - 34:33
    $300,000, state-of-the-art, DNA purifying
    machine.
  • 34:33 - 34:38
    Man - Alright, maiden voyage, it didn't
    ask me for a password, that's good.
  • 34:39 - 34:41
    On it goes.
  • 34:41 - 34:43
    Other man - Got the yellow light right
    away.
  • 34:43 - 34:45
    (Man) That's okay...
    (Narrator) I don't think the blinking
  • 34:45 - 34:47
    light is a good sign...
  • 34:47 - 34:50
    (Man) Looks like we have an air leak
    somewhere.
  • 34:50 - 34:52
    Uh oh, look at this.
  • 34:52 - 34:54
    (Man) It's cracked.
  • 34:54 - 34:56
    (Eric Landers) It's sort of like flying a
  • 34:56 - 34:58
    very large plane and repairing it while
  • 34:58 - 35:00
    you're flying. And you're trying to figure
  • 35:00 - 35:03
    out what went wrong.
  • 35:03 - 35:06
    And you also realize that you're spending
  • 35:06 - 35:08
    10's of thousands of dollars an hour,
  • 35:08 - 35:10
    so you feel under a little pressure
  • 35:10 - 35:13
    to sort of work this out as quickly as
    you can.
  • 35:13 - 35:16
    (Narrator) So, he calls the customer
    service line.
  • 35:16 - 35:18
    And of course, he's put on hold...
  • 35:18 - 35:24
    (music playing)
  • 35:24 - 35:28
    So, he waits...
  • 35:30 - 35:34
    And he waits...
  • 35:36 - 35:40
    And he waits...
  • 35:41 - 35:44
    Anyway, it turns out the the $300,000
  • 35:44 - 35:47
    machine does have one tiny little valve
  • 35:47 - 35:52
    that is broke, and so it doesn't work.
  • 35:52 - 35:54
    (Man) Alright...
  • 35:54 - 35:56
    (Eric) You never know whether the problem
  • 35:56 - 36:03
    is due to some robot, some funky bit of
    biochemistry, some
  • 36:03 - 36:05
    chemical that you've got that isn't really
  • 36:05 - 36:10
    working. And, so, it's incredibly
    complicated.
  • 36:10 - 36:14
    Woman - So, we have a transformation,
    where we transform a tenth of
  • 36:14 - 36:15
    our ligation...
  • 36:15 - 36:17
    Man - And add SDS to lice the phage.
  • 36:17 - 36:20
    Man - And all of our thermo-cyclers are
    three to four well plates.
  • 36:20 - 36:22
    (Man) So, if you basically determine your
  • 36:22 - 36:24
    Man - (inaudible scientific jargon ... and
  • 36:24 - 36:28
    give them each a different run module...
  • 36:28 - 36:32
    (Francis) Try to ramp something up...
    anything that's the slightest bit
  • 36:32 - 36:35
    cloogey suddenly becomes a major
    bottleneck.
  • 36:35 - 36:37
    Man - We talked about doing a full-out
  • 36:37 - 36:41
    test today and we weren't quite feeling
    good about doing that yet, so...
  • 36:41 - 36:44
    (Francis) There was a considerable sense
  • 36:44 - 36:47
    of white knuckles, because here we made
  • 36:47 - 36:49
    this promise, we were on the record here
  • 36:49 - 36:51
    saying we were gonna do this.
  • 36:51 - 36:53
    And things weren't working, the machines
  • 36:53 - 36:57
    were breaking down.
  • 36:57 - 36:59
    Woman - This is like... November?
  • 36:59 - 37:02
    Francis - And it's gotta work now. The
    time is running out.
  • 37:02 - 37:06
    Man - This is one of it's three inaugural
    runs and
  • 37:06 - 37:09
    (Man) it seems to be flawless, so far.
  • 37:09 - 37:11
    (Narrator) It took awhile, but the
    government teams finally hit
  • 37:11 - 37:14
    their stride.
  • 37:14 - 37:16
    (Francis) But the fall of that year was
  • 37:16 - 37:20
    really, sort of, the determining time.
  • 37:20 - 37:22
    The Center's really proved their mettle.
  • 37:22 - 37:24
    And every one of them began to catch
  • 37:24 - 37:29
    this rising curve, and ride it and we
    began to see data
  • 37:29 - 37:32
    appearing at prodigious rates.
  • 37:33 - 37:34
    Man - Do all...
  • 37:34 - 37:37
    (Francis) By early 2000's, a thousand
  • 37:37 - 37:39
    base pairs-per-second were rolling out of
  • 37:39 - 37:43
    this combined enterprise, seven days a
    week, 24 hours a day,
  • 37:43 - 37:45
    1,000 base pairs a second.
  • 37:45 - 37:49
    Then it really starts to go.
  • 37:49 - 37:51
    (Narrator) And those thousands of base
    pairs poured out of
  • 37:51 - 37:55
    university labs directly onto the
    internet.
  • 37:55 - 38:03
    (music playing)
  • 38:03 - 38:05
    (Narrator) Updated every night, it's
  • 38:05 - 38:07
    available for anyone, and everybody.
  • 38:07 - 38:11
    Including, by the way, the competition.
  • 38:12 - 38:13
    Man - Customers love our data...
  • 38:13 - 38:15
    (Narrator) Celera admits they got lots of
  • 38:15 - 38:17
    data directly from the government.
  • 38:17 - 38:19
    And Tony White, who runs the company
  • 38:19 - 38:22
    that owns Celera says, "Why not?"
  • 38:22 - 38:24
    Tony - That's publicly available data.
  • 38:24 - 38:26
    I'm a taxpayer, Celera's a taxpayer.
  • 38:26 - 38:29
    Ya know, why should we be excluded from
  • 38:29 - 38:32
    getting it? I mean, again, are they
    creating it
  • 38:32 - 38:35
    to give to all mankind except Celera?
  • 38:35 - 38:38
    Is that the idea, it isn't about us
  • 38:38 - 38:41
    getting the data, it's about this
    academic jealousy.
  • 38:41 - 38:43
    It's about the fact that our data, in
  • 38:43 - 38:46
    combination with theirs, give us a
  • 38:46 - 38:48
    perceived unfair advantage over this
  • 38:48 - 38:50
    so-called race.
  • 38:50 - 38:54
    Eric - If they wanna race us, that's
    their business.
  • 38:54 - 38:55
    I suppose they may.
  • 38:55 - 38:57
    (Narrator) I suspect strongly that they
    may.
  • 38:57 - 39:03
    Eric - Our job to get that data so that
    everybody can go use it.
  • 39:03 - 39:05
    (Narrator) Since Celera was sequencing the
  • 39:05 - 39:07
    genome with private money, some critics
  • 39:07 - 39:09
    wondered why should the government put
  • 39:09 - 39:14
    so much cash into the exact same research?
  • 39:15 - 39:18
    (Eric) In the United States, we invested
    in a National Highway system
  • 39:18 - 39:22
    in the 1950's. We got tremendous return
  • 39:22 - 39:25
    from building roads for free, and letting
  • 39:25 - 39:26
    everybody drive up and down them for
  • 39:26 - 39:28
    whatever purpose they wanted. We're
  • 39:28 - 39:31
    building a road up and down the
    chromosomes... for free.
  • 39:31 - 39:33
    People can drive up and down those
  • 39:33 - 39:35
    chromosomes from whatever they need
  • 39:35 - 39:37
    to. They can make discoveries, they can
  • 39:37 - 39:39
    learn about medicine, they can learn
  • 39:39 - 39:41
    about history, whatever they want.
  • 39:41 - 39:43
    It is worth the public investment to
  • 39:43 - 39:45
    make those roads available.
  • 39:45 - 39:47
    (Narrator) Wait a second, what I really
  • 39:47 - 39:49
    want to know is if you're making a roadmap
  • 39:49 - 39:53
    of a human being, which human beings are
  • 39:53 - 39:56
    we mapping? I mean humans come in so many
  • 39:56 - 39:59
    varieties, so whose genes, exactly, are
  • 39:59 - 40:01
    we looking at?
  • 40:01 - 40:03
    Eric - Yeah, it's mostly a guy from
    Buffalo and a woman from
  • 40:03 - 40:05
    Buffalo. That's because the laboratory--
  • 40:05 - 40:08
    Narrator - Whoa, whoa, wait... An
    anonymous couple from
  • 40:08 - 40:09
    Buffalo?
  • 40:09 - 40:11
    Eric - No, they're not a couple. They've
  • 40:11 - 40:12
    never met. The laboratory was a
  • 40:12 - 40:14
    laboratory in Buffalo. So, they put an ad
  • 40:14 - 40:16
    in the Buffalo newspapers, and they got
  • 40:16 - 40:18
    random volunteers from Buffalo.
  • 40:18 - 40:20
    They got about 20 of them, and chose
  • 40:20 - 40:23
    at random the sample, and that sample,
  • 40:23 - 40:25
    and that sample, so nobody knows who
  • 40:25 - 40:26
    they are.
  • 40:27 - 40:28
    (Narrator) And what about Celera?
  • 40:28 - 40:31
    Whose DNA are they mapping?
  • 40:31 - 40:33
    They also got a bunch of volunteers,
  • 40:33 - 40:37
    around 20, and picked five lucky winners
  • 40:37 - 40:39
    Craig - We tried to have some diversity,
  • 40:39 - 40:41
    in terms of, if we had an African American
  • 40:41 - 40:44
    or somebody's self-proclaimed Chinese
  • 40:44 - 40:48
    history, two Caucasians, and a Hispanic.
  • 40:48 - 40:50
    So, some of the volunteers were here
  • 40:50 - 40:51
    on the staff...
  • 40:51 - 40:53
    Narrator - I have to ask, cause everybody
  • 40:53 - 40:54
    does, are you one of them?
  • 40:54 - 40:57
    Craig - I am one of the volunteers, yes.
  • 40:57 - 40:59
    Narrator - Do you know whether or
    not you're one
  • 40:59 - 41:00
    of the winners?
  • 41:00 - 41:04
    I have a pretty good idea, yes, but I
  • 41:04 - 41:07
    can't disclose that, because it doesn't
  • 41:07 - 41:08
    matter.
  • 41:08 - 41:10
    Narrator - Well, if you're the head of
  • 41:10 - 41:13
    the company and you're watching the
    decoding of muah. That has a little
  • 41:13 - 41:15
    Miss Piggy quality to it.
  • 41:15 - 41:17
    Craig - Well, any scientist that I know
  • 41:17 - 41:18
    would love to be looking at their own
  • 41:18 - 41:20
    genetic code. I mean, how could you not
  • 41:20 - 41:22
    want to in this field?
  • 41:22 - 41:24
    (Narrator) Well, I don't know, I don't
  • 41:24 - 41:28
    work in this field. But I do wonder, can
  • 41:28 - 41:30
    any small group, and could that guy
  • 41:30 - 41:33
    from Buffalo, could he really be a
    stand-in
  • 41:33 - 41:37
    for all humankind?
  • 41:38 - 41:39
    Hasn't it been drummed into us since
  • 41:39 - 41:42
    birth that we're all... different? Each
  • 41:42 - 41:46
    and every one of us, completely unique.
  • 41:48 - 41:51
    We certainly look different.
  • 41:51 - 41:53
    People come in so many shapes, and
  • 41:53 - 41:59
    colors, and sizes... The DNA of these
  • 41:59 - 42:02
    humans has got to be significantly
  • 42:02 - 42:06
    different than the DNA of this human.
    Right?
  • 42:07 - 42:09
    Eric - The genetic difference between any
  • 42:09 - 42:12
    two people is 1/10th of a percent.
  • 42:12 - 42:14
    Those two, and any people on this planet,
  • 42:14 - 42:18
    are 99.9% identical at the DNA level.
  • 42:19 - 42:21
    It's only one letter in a thousand
    difference.
  • 42:22 - 42:25
    Narrator - And if I were to bring,
  • 42:25 - 42:29
    secretly into another room, a black man,
  • 42:29 - 42:32
    an Asian man, and a white man, and
  • 42:32 - 42:35
    show you only their genetic code,
  • 42:35 - 42:37
    could you tell which one was the white
    one?
  • 42:37 - 42:38
    Eric - I could not.
  • 42:38 - 42:40
    What's going on? Well, it tells us that,
  • 42:40 - 42:45
    first, as a species, we are very very
  • 42:45 - 42:47
    closely related. Cause any two human
  • 42:47 - 42:49
    beings being 99.9% identical, means that
  • 42:49 - 42:51
    we are much more closely related than
  • 42:51 - 42:53
    any two chimpanzees in Africa.
  • 42:53 - 42:55
    (Narrator) Wait, wait, you mean if two
  • 42:55 - 42:57
    chimpanzees are swinging through the
  • 42:57 - 42:59
    forest, and you look at the genes of
  • 42:59 - 43:00
    chimp A, and you look at the genes
  • 43:00 - 43:01
    of chimp B...
  • 43:01 - 43:03
    Eric - Average difference between those
  • 43:03 - 43:05
    chimps, is 4 - 5 times more than the
  • 43:05 - 43:08
    average between two humans that you
  • 43:08 - 43:09
    could pluck off this planet.
  • 43:09 - 43:11
    (Narrator) Because we're such a young
    species?
  • 43:11 - 43:12
    (Eric) That's right.
  • 43:12 - 43:15
    See, the thing is, we are the descendants
  • 43:15 - 43:20
    of a very small founding population.
  • 43:21 - 43:23
    Every human on this planet goes back
  • 43:23 - 43:25
    to a founding population of, perhaps,
  • 43:25 - 43:28
    10 or 20 thousand people in Africa.
  • 43:28 - 43:31
    About 100,000 years ago.
  • 43:31 - 43:34
    That little population didn't have a great
  • 43:34 - 43:36
    deal of genetic variation, and what
  • 43:36 - 43:39
    happened was, it was successful; it
  • 43:39 - 43:41
    multiplied all over the world, but in that
  • 43:41 - 43:44
    time, relatively little new genetic
  • 43:44 - 43:47
    variation is built up. So, we have today
  • 43:47 - 43:49
    on our planet, about the same genetic
  • 43:49 - 43:51
    variation that we walked out of Africa
  • 43:51 - 43:53
    with.
  • 43:57 - 43:59
    (Narrator) So, people are incredibly
  • 43:59 - 44:02
    similar to each other. But not only that,
  • 44:02 - 44:04
    it turns out that we also share many
  • 44:04 - 44:07
    genes with, well, everything.
  • 44:07 - 44:11
    50% of the genes of a banana are different
    from us?
  • 44:11 - 44:13
    Eric - How different are you from a
    banana?
  • 44:13 - 44:15
    Narrator - I feel, and I feel like can say
  • 44:15 - 44:17
    this with some authority, very different
  • 44:17 - 44:17
    from a banana.
  • 44:17 - 44:21
    Eric - You may feel different from -
    Narrator - I eat a banana but I-
  • 44:21 - 44:23
    Eric - Look, you've got cells, you've
  • 44:23 - 44:25
    gotta make those cells divide. So, all
  • 44:25 - 44:28
    the machinery for replicating your DNA,
  • 44:28 - 44:30
    all the machinery for controlling the
  • 44:30 - 44:33
    cell-cycle, the cell's surface, for making
  • 44:33 - 44:36
    nutrients, all that's the same in you and
  • 44:36 - 44:40
    a banana.
  • 44:41 - 44:44
    (Eric) Deep down, the fundamental
    mechanisms of
  • 44:44 - 44:48
    life were worked out only once on this
    planet.
  • 44:52 - 44:56
    And they've gotten reused in every
    organism.
  • 44:58 - 45:00
    The closer and closer you get to a
  • 45:00 - 45:03
    cell, the more you see a bag with
  • 45:03 - 45:05
    stuff in it and a nucleus and most of
  • 45:05 - 45:08
    those basic functions are the same.
  • 45:11 - 45:13
    Evolution doesn't go reinvent something
  • 45:13 - 45:15
    when it doesn't have to.
  • 45:15 - 45:21
    (music playing)
  • 45:24 - 45:28
    Take baker's yeast, baker's yeast, we're
  • 45:28 - 45:30
    related to one-and-a-half billion years
  • 45:30 - 45:32
    ago. But even even after one-and-a-half
  • 45:32 - 45:34
    billion years of evolutionary separation,
  • 45:34 - 45:36
    the parts are still interchangeable for
  • 45:36 - 45:38
    lots of these genes.
  • 45:38 - 45:40
    Narrator - Now, does that mean,
    I want to understand,
  • 45:40 - 45:42
    does that mean when you look through
  • 45:42 - 45:44
    those things, that all the C's, A's, T's,
  • 45:44 - 45:46
    T's, and the G's, are you seeing the same
  • 45:46 - 45:48
    exact same letter sequences in the
  • 45:48 - 45:50
    exact same alignment? When you look at
  • 45:50 - 45:52
    the yeast and you look at the person, is
  • 45:52 - 45:53
    it the same?
  • 45:53 - 45:55
    Eric - Sometimes it's eerie. The gene
  • 45:55 - 45:57
    sequence is nearly identical. There are
  • 45:57 - 46:01
    some genes, like Ubiquitin, that's 97%
  • 46:01 - 46:04
    identical between humans and yeast,
  • 46:04 - 46:06
    even after a billion years of evolution.
  • 46:06 - 46:08
    Narrator - Well, with a name like that,|
    it's gotta be...
  • 46:08 - 46:10
    Eric - Well, yeah, but you gotta
  • 46:10 - 46:12
    understand that deep down we are very
  • 46:12 - 46:14
    much partaking of that same bag of tricks
  • 46:14 - 46:16
    that evolution's been using to make
  • 46:16 - 46:19
    organisms all over this planet.
  • 46:21 - 46:23
    (Narrator) It seems incredible, but all
  • 46:23 - 46:26
    this information about evolution, about
  • 46:26 - 46:28
    our relationship to each other, and to all
  • 46:28 - 46:31
    living things, it's all right here in this
  • 46:31 - 46:36
    monotonous stream of letters. And as the
  • 46:36 - 46:38
    Human Genome Project progressed, and
  • 46:38 - 46:41
    hit high gear, the pace of discovery
  • 46:41 - 46:43
    quickened.
  • 46:44 - 46:46
    Once, they got fully automated, it wasn't
  • 46:46 - 46:48
    long until Lander, and Collins, and all
  • 46:48 - 46:51
    the other public project teams had reason
  • 46:51 - 46:53
    to celebrate.
  • 46:56 - 46:58
    Francis - I'm Francis Collins, the
  • 46:58 - 47:00
    Director of the National Human Genome
  • 47:00 - 47:02
    Research Institute, and we are happy to be
  • 47:02 - 47:05
    here together to have a party today.
  • 47:05 - 47:07
    (Narrator) By November of 1999, they had
  • 47:07 - 47:11
    reached a major milestone. In a five-way
  • 47:11 - 47:13
    award ceremony, hooked up by
  • 47:13 - 47:16
    satellite, the major university teams
  • 47:16 - 47:17
    announced they had finished a billion
  • 47:17 - 47:20
    base pairs of DNA. A third of the total
  • 47:20 - 47:22
    genome.
  • 47:23 - 47:27
    (corks popping)
  • 47:29 - 47:33
    (Eric) Have we got everybody?
  • 47:34 - 47:37
    Eric Lander - I would like to propose
  • 47:37 - 47:44
    a toast. A billion base pairs, all on the
  • 47:44 - 47:46
    public internet, available to anybody in
  • 47:46 - 47:49
    the world. It's an incredible achievement.
  • 47:49 - 47:53
    It hasn't been completely painless.
    (laughing)
  • 47:53 - 47:55
    And, because, I know everybody in this
  • 47:55 - 47:59
    room is living and breathing and thinking
  • 47:59 - 48:01
    every single moment of the day about how
  • 48:01 - 48:04
    to make all this happen... how we can hit
  • 48:04 - 48:07
    full-scale. I want to be sure you realize
  • 48:07 - 48:09
    what a remarkable thing we pulled off.
  • 48:09 - 48:14
    I hope you also know that this is history.
  • 48:15 - 48:19
    Whatever else you do in your lives, you're
  • 48:19 - 48:22
    apart of history. We're apart of an
  • 48:22 - 48:24
    amazing effort on the part of the world to
  • 48:24 - 48:26
    produce. And it isn't gonna be like the
  • 48:26 - 48:28
    moon, where we just visit occasionally.
  • 48:28 - 48:30
    This is gonna be something that every
  • 48:30 - 48:34
    student, every doctor uses, everyday in
  • 48:34 - 48:36
    the next century, and the century after
  • 48:36 - 48:40
    that. It's something to tell your kids
  • 48:40 - 48:42
    about. Something to tell your
  • 48:42 - 48:44
    grand-kids about. It's something you
  • 48:44 - 48:46
    should all be tremendously proud of.
  • 48:46 - 48:49
    I'm tremendously proud of you.
  • 48:49 - 48:52
    A toast, to this remarkable group.
  • 48:52 - 48:55
    To the work we've done. To the work ahead.
  • 48:55 - 48:56
    Hear, hear.
  • 48:56 - 48:57
    Everyone - Hear, hear.
  • 48:57 - 48:59
    (Whistling)
  • 48:59 - 49:01
    (Narrator) Everybody here is hoping the
  • 49:01 - 49:04
    genome project will help cure disease.
  • 49:04 - 49:06
    And the sooner it's done, the better for
  • 49:06 - 49:07
    all of us.
  • 49:07 - 49:10
    (inaudible)
  • 49:10 - 49:12
    (Narrator) But there's something more than
  • 49:12 - 49:14
    idealism, more than even pride that's
  • 49:14 - 49:18
    driving this race to finish the genome.
  • 49:18 - 49:20
    And that's the knowledge that with every
  • 49:20 - 49:23
    day that passes, more and more pieces
  • 49:23 - 49:25
    of our genome are being turned into
  • 49:25 - 49:30
    private property by way of the U.S. Patent
    Office.
  • 49:32 - 49:34
    (printing machine noises)
  • 49:34 - 49:38
    Woman - I said property.
  • 49:38 - 49:40
    (Narrrator) The office is inundated with
  • 49:40 - 49:42
    requests for patents for every imaginable
  • 49:42 - 49:45
    invention. From Star Wars action figures
  • 49:45 - 49:49
    to jet engines.
  • 49:49 - 49:52
    (paper shuffling sounds)
  • 49:52 - 49:55
    And here, along with all those gizmos, are
  • 49:55 - 49:59
    requests for patents for human genes.
  • 49:59 - 50:01
    Things that exist naturally in every one
  • 50:01 - 50:05
    of us. How is this possible?
  • 50:06 - 50:09
    (Todd) We regard genes as a patent-able
  • 50:09 - 50:11
    subject matter, as we regard almost any
  • 50:11 - 50:15
    chemical. We have issued patents on a
  • 50:15 - 50:18
    number of compounds and compositions
  • 50:18 - 50:20
    that are found in the human body.
  • 50:20 - 50:25
    For example, the gene that encodes insulin
  • 50:25 - 50:27
    has been patented, and that now has been
  • 50:27 - 50:29
    used to make almost all of the insulin
  • 50:29 - 50:31
    that is made. So, people's lives are being
  • 50:31 - 50:33
    saved today. Diabetics' lives are better.
  • 50:33 - 50:37
    As a matter of fact, if we ruled out every
    chemical that's found in the human body
  • 50:37 - 50:41
    there'd be an awful lot of inventions that
    would not be able to be protected.
  • 50:41 - 50:43
    (Narrator) Generally, to patent an
  • 50:43 - 50:45
    invention you've got to prove that it's
  • 50:45 - 50:47
    new and useful. But a few years ago
  • 50:47 - 50:49
    critics said that the patent office
  • 50:49 - 50:51
    wasn't being tough enough, so applicants
  • 50:51 - 50:53
    would say, "Well, here's a brand new
  • 50:53 - 50:55
    sequence of A's, C's, T's, and G's right
  • 50:55 - 50:57
    out of our machine's. That's new."
  • 50:57 - 50:59
    Now, "useful", wonder what they're gonna
  • 50:59 - 51:01
    be used for? Well, they were kind of vague
  • 51:01 - 51:04
    about use, says Eric Lander.
  • 51:04 - 51:06
    Eric - The sort of thing that people used
  • 51:06 - 51:08
    to do then was they would say that, "It
  • 51:08 - 51:12
    could be used as a probe to detect
    itself."
  • 51:12 - 51:14
    It's a trivial use. I mean it's like
  • 51:14 - 51:17
    saying, "I could use this new protein as
  • 51:17 - 51:20
    packing peanuts to stuff in a box."
  • 51:20 - 51:22
    I mean, it's true-
    Narrator - Well, wouldn't the patent
  • 51:22 - 51:26
    examiner say, "Well, that's not useful."
    Eric - No, no, no, you see, you the patent
  • 51:26 - 51:30
    guidelines are very unclear. I don't
    object to giving someone that limited time
  • 51:30 - 51:33
    of monopoly when they've really invented
    a cure for a disease; some really
  • 51:33 - 51:36
    important therapy.
    I do object to giving a monopoly when
  • 51:36 - 51:38
    somebody has simply described a couple
  • 51:38 - 51:40
    hundred letters of a gene, but has no idea
  • 51:40 - 51:42
    what use it could have in medicine...
  • 51:42 - 51:45
    cause what's happen is you've given away
    that precious monopoly to someone who's
  • 51:45 - 51:49
    done just a little bit of work, and then
    the people who come along and want to
  • 51:49 - 51:53
    do a lot of work to turn it into a
    therapy, well... they've gotta go pay the
  • 51:53 - 51:57
    person who already owns it. I think it's
    a bad deal for society.
  • 51:59 - 52:01
    (Narrator) It takes at least two years for
  • 52:01 - 52:03
    the patent office to process a single
  • 52:03 - 52:06
    application. So, right now, the patent
  • 52:06 - 52:09
    office says there are about 20,000 genetic
  • 52:09 - 52:12
    patents waiting for approval. All of them
  • 52:12 - 52:15
    are in limbo. This can cause problems for
  • 52:15 - 52:16
    drug companies who are trying to work
  • 52:16 - 52:20
    with genes to cure disease.
  • 52:20 - 52:22
    Narrator - I'm a company trying to do
  • 52:22 - 52:24
    work on this, this, and this rung of the
  • 52:24 - 52:25
    ladder.
    Eric - Right
  • 52:25 - 52:27
    Narrator - Cause I think that I can maybe
  • 52:27 - 52:29
    develop a cure for cancer,
    right here, for the sake
  • 52:29 - 52:31
    of arguing. But, of course, I have to
  • 52:31 - 52:33
    worry that somebody owns this space.
  • 52:33 - 52:35
    Eric - Oh, you have to worry a lot, that
  • 52:35 - 52:39
    this region here that you're working on
    that might cure cancer, has already been
  • 52:39 - 52:43
    patented by somebody else. And that
    patent filing is not public, and so you're
  • 52:43 - 52:45
    living with the shadow that all of your
  • 52:45 - 52:47
    work may go for naught.
  • 52:47 - 52:49
    Narrator - Because one day the phone
  • 52:49 - 52:52
    rings and says, "Sorry, you can't work
    here. Get off my territory."
  • 52:52 - 52:55
    Eric - That's right.
    Narrator - Or, you can work here, but I'm
  • 52:55 - 52:58
    gonna charge you $100,000 a week.
    Or, you can work here and I'll charge you
  • 52:58 - 53:01
    a nickel, but I want 50% of whatever you
    discover.
  • 53:01 - 53:05
    Eric - And the problem there is, it's even
    worse, because many companies don't start
  • 53:05 - 53:07
    the work whenever there's a cloud over
  • 53:07 - 53:10
    who owns that. If there's uncertainty,
  • 53:10 - 53:11
    companies would rather be working
  • 53:11 - 53:14
    some place where they don't have
    uncertainty.
  • 53:14 - 53:16
    And, therefore, I think, work doesn't get
  • 53:16 - 53:17
    done, because of the confusion over who
  • 53:17 - 53:18
    owns stuff.
  • 53:20 - 53:22
    (Narrator) Supporters of patents say they
  • 53:22 - 53:26
    are a crucial incentive for drug
    companies.
  • 53:27 - 53:29
    Drug research is phenomenally expensive
  • 53:29 - 53:31
    but if a company can monopolize a big
  • 53:31 - 53:33
    discovery with a patent, it can make
  • 53:33 - 53:37
    hundreds of millions of dollars.
  • 53:39 - 53:41
    Research scientists suddenly find
  • 53:41 - 53:43
    themselves in an unfamiliar world, ruled
  • 53:43 - 53:47
    by big money.
  • 53:47 - 53:49
    (Sheldon) Every scientist that does
    is research
  • 53:49 - 53:52
    now being looked upon as a generator of
  • 53:52 - 53:55
    wealth. Even if that person is not
  • 53:55 - 53:58
    interested in it. If they sequence some
  • 53:58 - 54:01
    DNA, that could be patent-able material.
  • 54:01 - 54:05
    So, whether the scientist likes it or not,
  • 54:05 - 54:07
    he or she becomes an entrepreneur just
  • 54:07 - 54:11
    by virtue of doing science.
  • 54:12 - 54:14
    (Narrator) Craig Venter is first a
    scientist
  • 54:14 - 54:17
    but he has made the leap from academia
  • 54:17 - 54:20
    into the business world.
  • 54:20 - 54:22
    Narrator - Let me talk about the business
  • 54:22 - 54:23
    of this. Do you consider yourself a
  • 54:23 - 54:24
    business man?
  • 54:24 - 54:26
    Craig - No, in fact, I still, sort of
  • 54:26 - 54:28
    bristle at the term for some reason.
  • 54:28 - 54:31
    But my philosophy is, we would not get
    medical
  • 54:31 - 54:34
    breakthroughs in this country, at all, if
  • 54:34 - 54:37
    it wasn't done in a business setting.
  • 54:37 - 54:39
    We would not have new therapies if we
  • 54:39 - 54:41
    didn't have a biotech and pharmaceutical
  • 54:41 - 54:42
    industry.
  • 54:42 - 54:45
    Narrator - But are they... if you bristle
  • 54:45 - 54:46
    at the word "businessman", that might be
  • 54:46 - 54:49
    because in some part of your soul, you may
  • 54:49 - 54:51
    think that the business of science and the
  • 54:51 - 54:53
    business of business are fundamentally
  • 54:53 - 54:55
    incompatible for one simple reason:
  • 54:55 - 54:58
    that the business has to sell something
  • 54:58 - 55:01
    and the science has to learn, or teach,
  • 55:01 - 55:02
    something.
  • 55:02 - 55:04
    Craig - I think I bristle at it because
  • 55:04 - 55:06
    it's used as an attack. It's used as a
  • 55:06 - 55:10
    criticism. In this case, if the science is
  • 55:10 - 55:14
    not spectacular, if the medicine is not
  • 55:14 - 55:18
    spectacular, there will be no profits.
  • 55:18 - 55:21
    (Narrator) Venter was given $300,000,000
  • 55:21 - 55:24
    to set up Celera, and his investors are
  • 55:24 - 55:27
    expecting something in return.
  • 55:27 - 55:30
    But how can they profit from the genome?
  • 55:30 - 55:33
    At the moment, the company is banking on
  • 55:33 - 55:37
    pure computer power.
  • 55:39 - 55:43
    This is Celera's master control.
  • 55:43 - 55:45
    24 hours a day, technicians monitor all
  • 55:45 - 55:48
    the company's major operations, including
  • 55:48 - 55:49
    the hundreds of sequencers that are
  • 55:49 - 55:53
    constantly decoding our genes.
  • 55:55 - 55:57
    And they oversee Celera's main source of
  • 55:57 - 56:01
    income: a massive website, where, for a
  • 56:01 - 56:03
    fee, you can explore several genomes,
  • 56:03 - 56:06
    including those of fruit flies, mice, and,
  • 56:06 - 56:09
    of course, humans. What all this
  • 56:09 - 56:11
    adds up to is something like a big
  • 56:11 - 56:13
    browser. A user-friendly interface between
  • 56:13 - 56:16
    you and your genes.
  • 56:16 - 56:19
    Tony White - Our business is to sell
  • 56:19 - 56:21
    products that enable research. That's
  • 56:21 - 56:24
    essentially what we do. So, we're used to
  • 56:24 - 56:26
    selling the picks and the shovels to the
  • 56:26 - 56:30
    miners. Tools to interpret the human
  • 56:30 - 56:32
    genome and other related species.
  • 56:32 - 56:35
    Or, merely more products along the
  • 56:35 - 56:38
    same genre, they just happen to be less
  • 56:38 - 56:41
    tangible than a machine.
  • 56:41 - 56:43
    (Narrator) So, Celera's business plan is
  • 56:43 - 56:45
    to gather information from all kinds of
  • 56:45 - 56:47
    creatures, put it together, and sell their
  • 56:47 - 56:48
    findings to drug companies, or
  • 56:48 - 56:51
    universities, or whomever. But it's the
  • 56:51 - 56:54
    selling part, selling scientific
  • 56:54 - 56:56
    information, that makes some scientists
  • 56:56 - 56:58
    very uncomfortable.
  • 56:58 - 57:00
    Todd - This is a big change in the ethos
  • 57:00 - 57:03
    of the scientific community, which is,
  • 57:03 - 57:07
    supposedly, it was built upon the idea of
  • 57:07 - 57:10
    community values of the free and open
  • 57:10 - 57:13
    exchange of information. The fundamental
  • 57:13 - 57:16
    idea that when you learn something, you
  • 57:16 - 57:18
    publish it immediately, you share it with
  • 57:18 - 57:22
    others. Science grows by this community
  • 57:22 - 57:24
    interest of shared knowledge.
  • 57:24 - 57:26
    Tony - I think, why doesn't Pfeiffer give
  • 57:26 - 57:28
    away their drugs? They could help a lot
  • 57:28 - 57:30
    more people if they didn't charge for
    them.
  • 57:30 - 57:33
    Man - At what point is "free" really free?
  • 57:33 - 57:35
    (Narrator) Tony White has absolutely no
  • 57:35 - 57:38
    problem with making money from the human
    genome.
  • 57:38 - 57:40
    Tony - I hope we have a legal monopoly on
  • 57:40 - 57:42
    the information. I hope the product is so
  • 57:42 - 57:45
    good, and so valuable to people, that they
  • 57:45 - 57:47
    feel that it's necessary to come through
  • 57:47 - 57:50
    us to get it.
  • 57:50 - 57:52
    Anybody who wants to can build all the
  • 57:52 - 57:55
    tools that we're gonna build. Whether or
  • 57:55 - 57:56
    not they will choose to is a different
  • 57:56 - 57:58
    matter.
  • 57:58 - 58:02
    Narrator - Which is the better business
    to be in, do you think? The landlord
  • 58:02 - 58:05
    business? Or this, "You subscribe and
    I'll give you some information about
  • 58:05 - 58:07
    anything you want business."
  • 58:07 - 58:10
    Eric - They're both lousy businesses.
    (Narrator laughing) Lousy?
  • 58:10 - 58:11
    Eric- They're lousy businesses by
  • 58:11 - 58:14
    comparison with the real business:
    Make drugs.
  • 58:14 - 58:17
    Actually make molecules that cure people.
  • 58:17 - 58:18
    Narrator - curing people is the whole
  • 58:18 - 58:20
    point, right? But if there is one thing
  • 58:20 - 58:22
    that the Human Genome Project has
  • 58:22 - 58:25
    taught us, is that finding cures is a
  • 58:25 - 58:27
    whole lot harder than simply reading
  • 58:27 - 58:30
    letters of DNA.
  • 58:30 - 58:31
    (Narrator) Take for example, the case of
  • 58:31 - 58:35
    little Riley Demoush.
  • 58:36 - 58:40
    (baby sounds)
  • 58:41 - 58:43
    At two months, Riley appears to be
  • 58:43 - 58:47
    a perfectly healthy baby boy, but he's
    not.
  • 58:48 - 58:51
    When Riley was just 13 days old, Kathy
  • 58:51 - 58:55
    got the call that every parent dreads.
  • 58:57 - 58:59
    Kathy - The pediatrician called on a
  • 58:59 - 59:04
    Thursday evening, and he said, "I need
  • 59:04 - 59:06
    to talk to you about the baby."
  • 59:06 - 59:09
    He said, "Are you sitting down?"
  • 59:09 - 59:13
    I'm like, "Yeah." And that really suprised
  • 59:13 - 59:15
    me. And he said, "Are you holding the
  • 59:15 - 59:17
    baby." Because he didn't want me to drop
  • 59:17 - 59:18
    the baby, obviously.
  • 59:18 - 59:21
    And he said, "The tests came through, and
  • 59:21 - 59:25
    Riley tested positive to cystic fibrosis."
  • 59:25 - 59:28
    And I was in shock.
  • 59:28 - 59:30
    (Narrator) As Kathy and her husband
  • 59:30 - 59:33
    would soon learn, Cystic Fibrosis, "CF"
  • 59:33 - 59:35
    for short, attacks several organs of the
  • 59:35 - 59:38
    body, but especially the lungs.
  • 59:38 - 59:40
    It's victims suffer from chronic
  • 59:40 - 59:43
    respiratory infections, and half of all
  • 59:43 - 59:47
    CF patients die before the age of 30.
  • 59:49 - 59:51
    David Waltz - To think that we can still
  • 59:51 - 59:53
    be hopeful that their child will grow up
  • 59:53 - 59:57
    to have a normal, healthy, happy, and long
  • 59:57 - 60:00
    life. But at the present time, I don't
  • 60:00 - 60:03
    have any guarantees about that.
  • 60:03 - 60:05
    Kathy - Someone had asked me, "Are you
  • 60:05 - 60:09
    prepared to bury your son at such a young
  • 60:09 - 60:12
    age, whether it's four or 40?" And he was
  • 60:12 - 60:14
    seventeen days old when that happened
  • 60:14 - 60:15
    and I said, "I've had him for seventeen
  • 60:15 - 60:19
    days, and I wouldn't trade those seventeen
    days."
  • 60:19 - 60:21
    (Narrator) Finding the genetic defect
  • 60:21 - 60:25
    that causes CF was big news back in 1989.
  • 60:26 - 60:28
    News woman - Medical researchers say
  • 60:28 - 60:30
    they have discovered the gene which is
  • 60:30 - 60:32
    responsible for Cystic Fibrosis; the most
  • 60:32 - 60:34
    common inherited fatal disease in this
    country.
  • 60:34 - 60:36
    Robert Dresing - We're going to cure this
  • 60:36 - 60:37
    disease...
  • 60:37 - 60:39
    (Narrator) A lot of people expected the
  • 60:39 - 60:43
    cure to arrive any day... it didn't.
  • 60:43 - 60:45
    Francis Collins, now head of the gov's
  • 60:45 - 60:47
    Genome Project, led one of the teams that
  • 60:47 - 60:50
    discovered the CF gene.
  • 60:50 - 60:52
    Francis - We still have not seen this
  • 60:52 - 60:54
    disease cured or even particularly
  • 60:54 - 60:55
    benefitted by all of this wonderful
  • 60:55 - 60:57
    molecular biology. CF is still treated
  • 60:57 - 60:59
    pretty much the way it was 10 years ago,
  • 60:59 - 61:02
    but that is going to change.
  • 61:02 - 61:03
    (Narrator) The original hope was that
  • 61:03 - 61:06
    babies like Riley could be cured by gene
  • 61:06 - 61:08
    therapy. Medicine that would provide a
  • 61:08 - 61:12
    good working copy of a broken gene, but
  • 61:12 - 61:14
    attempts at gene therapy have hardly ever
  • 61:14 - 61:17
    worked. They remain highly controversial,
  • 61:17 - 61:19
    so if there's gonna be an effective
  • 61:19 - 61:22
    treatment for Riley, instead of fixing his
  • 61:22 - 61:24
    genes, we're gonna take a look -- and this
  • 61:24 - 61:28
    is new territory -- at his proteins.
  • 61:28 - 61:30
    Narrator - What do proteins do?
  • 61:30 - 61:32
    Venter - When you look at yourself in the
  • 61:32 - 61:34
    mirror, you don't see DNA, you don't see
  • 61:34 - 61:37
    RNA... you see proteins and the result of
  • 61:37 - 61:40
    protein-action. That's what we are
  • 61:40 - 61:42
    physically composed of.
  • 61:42 - 61:44
    Narrator - So it's not a Rodgers and
    Hammerstein thing
  • 61:44 - 61:46
    where one guy does the tune and the other
  • 61:46 - 61:48
    guy does the lyrics, this is a case where
  • 61:48 - 61:50
    the genes create the proteins and the
  • 61:50 - 61:51
    proteins create us.
  • 61:51 - 61:53
    Craig - That's right, we are the
  • 61:53 - 61:54
    accumulation of our proteins and our
  • 61:54 - 61:57
    protein activities.
  • 61:57 - 61:59
    (Narrator) A protein starts out as a long
  • 61:59 - 62:01
    chain of different chemicals, amino acids.
  • 62:01 - 62:04
    But, unlike genes, proteins won't work in
  • 62:04 - 62:07
    a straight line.
  • 62:07 - 62:10
    Francis - Genes are effectively
    one-dimensional.
  • 62:10 - 62:13
    If you write down the sequence of A,C,G,
    and T,
  • 62:13 - 62:15
    that's kinda what you need to know about
  • 62:15 - 62:18
    that gene. But proteins are 3-dimensional.
  • 62:18 - 62:20
    They have to be, because we're
    3-dimensional and
  • 62:20 - 62:22
    we're made of those proteins, otherwise
  • 62:22 - 62:25
    we'd all sort of be linear, unimaginably
  • 62:25 - 62:28
    weird creatures.
  • 62:28 - 62:30
    (Narrator) Here's part of the protein.
  • 62:30 - 62:34
    Think of them as tangles of ribbon.
  • 62:34 - 62:35
    They come in any number of different
  • 62:35 - 62:38
    shapes. They can look like this, or like
  • 62:38 - 62:43
    this, or like this. The varieties are
  • 62:43 - 62:47
    endless. But, when it's created, every
  • 62:47 - 62:50
    protein is told, "Here is your shape."
  • 62:50 - 62:53
    and that shape defines what it does,
  • 62:53 - 62:55
    tells all the other proteins what it does,
  • 62:55 - 62:57
    and that's how they recognize each other
  • 62:57 - 63:00
    when they hook up and do business.
  • 63:00 - 63:03
    In the protein world, your shape is your
  • 63:03 - 63:06
    destiny.
  • 63:06 - 63:09
    Francis - They have needs and reasons to
  • 63:09 - 63:11
    want to be snuggled up against each
  • 63:11 - 63:13
    other in a particular way. And actually,
  • 63:13 - 63:15
    a particular amino acid sequence will
  • 63:15 - 63:18
    almost always fold in a precise way.
  • 63:18 - 63:20
    Narrator - Should I think origami-like?
  • 63:20 - 63:21
    When you're stretching, folding, and--
  • 63:21 - 63:24
    Francis - It's elegant, very complicated,
  • 63:24 - 63:27
    and we still do not have the ability to
  • 63:27 - 63:29
    precisely predict how that's going to
  • 63:29 - 63:32
    work, but obviously it does work.
  • 63:32 - 63:34
    (Narrator) Except, of course, if something
  • 63:34 - 63:36
    does go wrong, and that's what happened
  • 63:36 - 63:39
    to baby Riley.
  • 63:39 - 63:42
    Riley has a tiny error in his DNA, just
  • 63:42 - 63:44
    three letters out of three billion are
  • 63:44 - 63:47
    missing, but because of that error he has
  • 63:47 - 63:49
    a faulty gene, and that faulty gene
  • 63:49 - 63:52
    creates a faulty or misshapen protein.
  • 63:52 - 63:55
    And just the slightest little changes in
  • 63:55 - 63:58
    shape and "Boom" the consequences are
  • 63:58 - 64:01
    huge, because it is now misshapen and
  • 64:01 - 64:03
    a key protein that is found in the lung
  • 64:03 - 64:07
    cells can't do it's job. So, let's take a
  • 64:07 - 64:10
    look at some real lung cells, we'll travel
  • 64:10 - 64:14
    in. This is the lining, or the membrane
  • 64:14 - 64:16
    of a lung cell, and here's how the protein
  • 64:16 - 64:20
    is supposed to work. The top of your
  • 64:20 - 64:22
    screen is the outside of a cell, the
  • 64:22 - 64:24
    bottom the inside of the cell, of course,
  • 64:24 - 64:26
    and our healthy protein is providing a,
  • 64:26 - 64:29
    kind of chute so that salt can enter and
  • 64:29 - 64:31
    leave the cell. Those little green bubbles
  • 64:31 - 64:34
    that's salt and. as you see here, the salt
  • 64:34 - 64:39
    is getting through. But if the protein is
  • 64:39 - 64:42
    not the right shape, then it's not allowed
  • 64:42 - 64:45
    into the membrane; it can't do its job.
  • 64:45 - 64:48
    And, without that protein, as you see here
  • 64:48 - 64:50
    salt gets trapped inside the cell and that
  • 64:50 - 64:53
    triggers a whole chain of reactions that
  • 64:53 - 64:55
    makes the cell surface sticky and covered
  • 64:55 - 64:59
    with thick mucus.
  • 65:00 - 65:02
    Woman - The first two positions that are
  • 65:02 - 65:04
    done sitting up are probably a little more
  • 65:04 - 65:05
    difficult to do...
  • 65:05 - 65:07
    (Narrator) The mucus has to be dislodged
  • 65:07 - 65:10
    physically. Riley's family is learning to
  • 65:10 - 65:12
    loosen the mucus that may develop in
  • 65:12 - 65:14
    his lungs and fight infections with
  • 65:14 - 65:15
    antibiotics.
  • 65:15 - 65:17
    (Woman) You sort of wanna do it with
  • 65:17 - 65:18
    a cupped hand.
  • 65:18 - 65:20
    (Father) Trying to get at the top of the
  • 65:20 - 65:22
    lungs?
    Woman - Yep, you wanna be like right here-
  • 65:22 - 65:26
    (Narrator) But what the doctors and the
    scientists would love to do is, if they
  • 65:26 - 65:30
    can't fix baby Riley's genes, then maybe
    there's someway to treat Riley's misshapen
  • 65:30 - 65:34
    protein and restore the original shape.
  • 65:34 - 65:36
    Because if you could just get them shaped
  • 65:36 - 65:38
    right, the protein should become instantly
  • 65:38 - 65:40
    recognizable to other proteins and get
  • 65:40 - 65:43
    back to business.
  • 65:43 - 65:45
    But, look at these things. How would we
  • 65:45 - 65:48
    ever learn to properly fold wildly
  • 65:48 - 65:51
    multi-dimensional proteins? It may
  • 65:51 - 65:55
    be doable, but it won't be easy.
  • 65:55 - 65:57
    Eric Lander - The Genome Project was
  • 65:57 - 65:59
    a piece of cake compared to most other
  • 65:59 - 66:01
    things, because genetic information is
  • 66:01 - 66:03
    linear. It goes in a simple line up and
  • 66:03 - 66:06
    down the chromosome. Once you start
  • 66:06 - 66:08
    talking about the 3-dimensional shapes
  • 66:08 - 66:12
    into which protein change can fold, and
  • 66:12 - 66:13
    how they can stick to each other in many
  • 66:13 - 66:16
    different ways to do things. Or the ways
  • 66:16 - 66:18
    in which cells can interact, like wiring
  • 66:18 - 66:20
    up in your brain... you're not in a
  • 66:20 - 66:22
    one-dimensional problem anymore. You're
  • 66:22 - 66:26
    not in Kansas anymore.
  • 66:26 - 66:28
    (Narrator) As the scientists head into
  • 66:28 - 66:29
    the world of proteins, they're looking
  • 66:29 - 66:34
    very closely at patients like Tony Ramos.
  • 66:34 - 66:36
    Tony has cystic fibrosis, but it's not the
  • 66:36 - 66:40
    typical case. CF almost always develops in
  • 66:40 - 66:43
    early childhood. Tony didn't have any
  • 66:43 - 66:47
    symptoms until she was 15.
  • 66:47 - 66:49
    Tony - I started having a cough, and then
  • 66:49 - 66:51
    we kept thinking I was catching a lot of
  • 66:51 - 66:54
    colds and my step-mother thought,
  • 66:54 - 66:56
    "Well, that's not right." So, I started
  • 66:56 - 66:58
    going to doctors, trying to figure it out.
  • 66:58 - 67:03
    And went through a lot of tests because
    I don't fit the profile. Tuberculosis,
  • 67:03 - 67:07
    walking pneumonia, ya know, test after
    test.
  • 67:08 - 67:09
    (Narrator) At the time of diagnosis,
  • 67:09 - 67:11
    Tony's family was told she might not
  • 67:11 - 67:16
    survive beyond her twenty-first birthday.
  • 67:16 - 67:19
    She's now in her mid-forties. But her CF
  • 67:19 - 67:21
    is worsening. 2 or 3 times a year she does
  • 67:21 - 67:23
    have to be admitted to the hospital to
  • 67:23 - 67:27
    clean out her lungs.
  • 67:27 - 67:29
    Tony - Ya know, they were always doing
  • 67:29 - 67:33
    some little funky study to help the cause,
    because we're not the normal, ya know
  • 67:33 - 67:36
    there's not a whole lot of us. I know that
  • 67:36 - 67:38
    they don't know why, and it's the big
  • 67:38 - 67:41
    question mark, and hopefully research
  • 67:41 - 67:43
    will keep going and figure it out.
  • 67:43 - 67:47
    (Narrator) Here's the question: Tony was
    born with a mistake in the same gene as
  • 67:47 - 67:50
    baby Riley, and yet for some reason, when
  • 67:50 - 67:54
    Tony was a baby, she didn't get sick. Why?
  • 67:54 - 67:57
    And now that she is sick, she hasn't died.
  • 67:57 - 68:01
    Why? What does Tony have that the other
  • 68:01 - 68:04
    CF patients don't have?
  • 68:06 - 68:09
    Dr. Craig Gerrard believes the answer lies
  • 68:09 - 68:11
    in her genes; in her DNA.
  • 68:11 - 68:13
    Dr. Gerrard - Good morning.
    Tony - Good morning.
  • 68:13 - 68:17
    Dr. Gerrard - So, do you think the change
    in the antibiotics is helping you?
  • 68:17 - 68:19
    Tony - Yes, and I've dropped four pounds
  • 68:19 - 68:21
    overnight.
    Dr. Gerrard - (laughing) That's a lot of
  • 68:21 - 68:22
    weight.
    Tony - Yeah!
  • 68:22 - 68:24
    Dr. Gerrard - Okay, mind if I have a
    listen?
  • 68:24 - 68:26
    (Dr. Gerrard) - No gene acts in isolation,
  • 68:26 - 68:29
    it is always acting as a part of a larger
  • 68:29 - 68:33
    picture. And therefore, the other genes,
  • 68:33 - 68:35
    which compensate.
  • 68:35 - 68:37
    (Narrator) Could it be that Tony has some
  • 68:37 - 68:40
    other genetic mutations, good mutations,
  • 68:40 - 68:43
    that are producing good proteins, that
  • 68:43 - 68:45
    kept her healthy for 15 years and are
  • 68:45 - 68:47
    keeping her alive right now?
  • 68:47 - 68:51
    (Dr. Gerrard) You sound a lot better than
    you did when you came in. So, I think
  • 68:51 - 68:54
    you're on the mend. Okay, hang in there.
    Tony - Thanks, bye.
  • 68:54 - 68:56
    Dr. Gerrard - In my opinion, there are
  • 68:56 - 68:59
    genes that are allowing her to have a
  • 68:59 - 69:03
    more beneficial course, if you will, than
  • 69:03 - 69:07
    another patient.
  • 69:07 - 69:10
    Woman - You sound good.
  • 69:10 - 69:12
    (Narrator) Dr. Gerrard is searching for
  • 69:12 - 69:16
    the special ingredient in Tony. If it
  • 69:16 - 69:19
    turns out she has one or two proteins that
  • 69:19 - 69:21
    are helping her, maybe we could bottle
  • 69:21 - 69:24
    them and use them to help all CF patients,
  • 69:24 - 69:26
    like little Riley.
  • 69:26 - 69:29
    (Woman) If there was ever an emergency,
    and I didn't know how to do it, and I
  • 69:29 - 69:33
    couldn't get in touch with you--
    (Narrator) No one can predict Riley's
  • 69:33 - 69:36
    future, or to what extent CF will affect
    his life. But now that we are getting a
  • 69:36 - 69:39
    map of our genes, we'll be able to take
  • 69:39 - 69:41
    the next big step. Because, what genes
  • 69:41 - 69:45
    do, basically, is they make proteins.
  • 69:46 - 69:48
    Narrator - I get the sense that everybody
  • 69:48 - 69:52
    is getting out of the gene business, and
    suddenly going into this new business
  • 69:52 - 69:54
    I hear about, called, "The Protein
    Business."
  • 69:54 - 69:57
    There's even a new name, instead of
    "The Genome" I'm hearing this other
  • 69:57 - 69:59
    name, "The Proteome".
    Eric - The Proteome.
  • 69:59 - 70:01
    (Narrator) The Proteome.
    Eric - Yes.
  • 70:01 - 70:04
    (Narrator) What is that?
    Eric - Well, the genome is the collection
  • 70:04 - 70:08
    of all your genes and DNA. The proteome,
    is the collection of all your proteins.
  • 70:08 - 70:12
    See, what's happening is, we're realizing
    that if we wanted to understand life, we
  • 70:12 - 70:14
    had to start systematically at the bottom
  • 70:14 - 70:16
    and get all the building blocks. The first
  • 70:16 - 70:19
    building blocks are the DNA letters, from
  • 70:19 - 70:22
    them we can infer the genes. From the
  • 70:22 - 70:25
    genes we can infer the protein products
  • 70:25 - 70:26
    that they make, that do all the work of
  • 70:26 - 70:29
    the cell. Then we've gotta understand what
  • 70:29 - 70:30
    each of those proteins does, what its
  • 70:30 - 70:32
    shape is, how they interact with each
  • 70:32 - 70:35
    other, and how they make kind of circuits
  • 70:35 - 70:37
    and connections with each other. So,
  • 70:37 - 70:39
    in some sense, this is just the beginning
  • 70:39 - 70:42
    of a very comprehensive, systematic
  • 70:42 - 70:46
    program to understand all the components
  • 70:46 - 70:48
    and how they all connect with each other.
  • 70:48 - 70:50
    (Narrator) All the components and how they
  • 70:50 - 70:53
    connect. But how many components are
  • 70:53 - 70:55
    there? How many genes and how many
  • 70:55 - 70:57
    proteins do we have?
  • 70:57 - 70:59
    Eric - A real shock about the genome
  • 70:59 - 71:02
    sequence was that we have so many fewer
  • 71:02 - 71:03
    genes than we've been teaching our
  • 71:03 - 71:05
    students. The official textbook answer is
  • 71:05 - 71:08
    the human has a 100,000 genes. Everyone's
  • 71:08 - 71:11
    known that since the early 1980's, the
  • 71:11 - 71:13
    only problem is is it's not true. Turns
  • 71:13 - 71:16
    we only have 30,000 or so genes.
  • 71:16 - 71:18
    (Narrator) 30,000 genes, that's it? Not
  • 71:18 - 71:20
    everybody agrees with this number, but
  • 71:20 - 71:25
    that's about as many as a mouse. Even
    a fruit-fly has 14,000 genes.
  • 71:25 - 71:27
    Eric - That's really bothersome to many
  • 71:27 - 71:29
    people that we only have about twice as
  • 71:29 - 71:31
    many genes as a fruit-fly, because we
  • 71:31 - 71:33
    really like to think of ourselves as a lot
  • 71:33 - 71:35
    more than twice as complex. Well, don't
  • 71:35 - 71:37
    you? I certainly like to think of myself
  • 71:37 - 71:41
    that way. And so, it raises two questions,
  • 71:41 - 71:43
    are we really more complex?
  • 71:43 - 71:46
    Narrator - You show me the fruit fly that
    can compose like Mozart and then I'll--
  • 71:46 - 71:48
    Eric - Well, show me a human that can fly.
  • 71:48 - 71:50
    Right? So...
    (Narrator) Ooo (laughing)
  • 71:50 - 71:53
    Eric - (laughing) We all have our talents,
    right?
  • 71:53 - 71:55
    (Narrator) I suppose we do, but as it
  • 71:55 - 71:57
    happens, we have lots of genes that are
  • 71:57 - 72:01
    virtually identical in us and fruit flies.
  • 72:01 - 72:04
    But, happily, our genes seem to do more,
  • 72:04 - 72:07
    so, let's say that I am a fruit fly. One
    of
  • 72:07 - 72:11
    my fruit fly genes may make one and two
  • 72:11 - 72:14
    slightly different proteins, but now I'm a
  • 72:14 - 72:17
    human and the very same gene in me
  • 72:17 - 72:21
    might make one, two, three, four different
  • 72:21 - 72:23
    proteins, and these four proteins could
  • 72:23 - 72:29
    combine and build even bigger and more
    proteins.
  • 72:29 - 72:32
    Eric - It turns out, that the gene makes a
  • 72:32 - 72:35
    message, but the message can be spliced up
  • 72:35 - 72:37
    in different ways. And, so, a gene might
  • 72:37 - 72:40
    make three proteins or four proteins
  • 72:40 - 72:43
    and then that protein can get modified.
  • 72:43 - 72:45
    There could be other proteins that stick
  • 72:45 - 72:48
    some phosphate group on it or two
  • 72:48 - 72:53
    phosphates groups. And, in fact, all these
  • 72:53 - 72:55
    modifications to the proteins could make
  • 72:55 - 72:57
    them function differently. So, while you
  • 72:57 - 73:00
    might only have 30,000 genes, you could
  • 73:00 - 73:03
    have 100,000 distinct proteins, and when
  • 73:03 - 73:06
    you're done putting all of the different
    modifications on them, there might be
  • 73:06 - 73:08
    a million of them.
    (narrator clears throat)
  • 73:08 - 73:09
    Scary thought.
  • 73:09 - 73:11
    Narrator - So, starting with the same raw
  • 73:11 - 73:16
    ingredients, the fruit fly goes, "mmm,
    spch, mmm, spch, mmm, spch."
  • 73:16 - 73:19
    but the human, by somehow or other, being
  • 73:19 - 73:21
    able to arrange all the parts in many
  • 73:21 - 73:26
    different ways, can produce melodies,
    perhaps?
  • 73:26 - 73:29
    Eric - Yes, although we're not that good
  • 73:29 - 73:32
    at hearing the melodies yet. One of the
  • 73:32 - 73:36
    exciting things about reading the genome
    sequence now, is we're getting a glimpse
  • 73:36 - 73:39
    at that complexity of the parts and how
  • 73:39 - 73:42
    it's a symphony, rather than a simple tune
  • 73:42 - 73:46
    but it's not that easy to just read
    that sheet
  • 73:46 - 73:49
    music there and hear the symphony that's
  • 73:49 - 73:51
    coming out of it.
  • 73:51 - 73:55
    (Narrator) Okay, so it's not just the
    number of genes, it's all the different
  • 73:55 - 73:59
    proteins they can make and the ways those
    proteins interact, and to find out all of
  • 73:59 - 74:01
    those interactions and how they affect
  • 74:01 - 74:04
    health and disease, that's gonna keep
  • 74:04 - 74:06
    scientists very busy for the next few
  • 74:06 - 74:09
    decades. But, of course, before the
  • 74:09 - 74:11
    research can begin in earnest, they first
  • 74:11 - 74:13
    have to complete the parts list of all the
  • 74:13 - 74:17
    genes. And by the spring of 2000, both
  • 74:17 - 74:20
    sides, the public labs and Celera, they
  • 74:20 - 74:23
    were in hyper-drive. Each camp madly
  • 74:23 - 74:26
    trying to be first to reach the finish
    line and get
  • 74:26 - 74:30
    all 3 billion letters.
  • 74:30 - 74:32
    (Gene Myers) The pace of things, and the
  • 74:32 - 74:35
    magnitude of things was really incredible.
  • 74:35 - 74:37
    I mean, I would remember coming in and
  • 74:37 - 74:39
    just having this gripping feeling in my
  • 74:39 - 74:43
    gut, just an intense, "Oh my God, am up to
    this?"
  • 74:45 - 74:47
    Robert Cook-Degan - You know whoever has
  • 74:47 - 74:49
    this reference sequence to the human
  • 74:49 - 74:51
    genome out there in the world first...
  • 74:51 - 74:54
    they're going to be famous. They're gonna
  • 74:54 - 74:56
    be on the front page of The New York
    Times,
  • 74:56 - 74:58
    and a lot more than that, for a long time.
  • 74:58 - 75:01
    And they're gonna be, ya know,
    celebrities.
  • 75:01 - 75:06
    And, ya know, when that's going on, it's
  • 75:06 - 75:08
    not unreasonable that people are gonna
  • 75:08 - 75:10
    reach for that star and try to get there
  • 75:10 - 75:14
    before the other person.
  • 75:14 - 75:16
    (Tony White) I thought that the
    really intense
  • 75:16 - 75:19
    Tony - competition of this world was
    among business, where there is a profit
  • 75:19 - 75:24
    motive. I now find that we are pikers in
    the business world, compared to the
  • 75:24 - 75:27
    academic competition that exists out
    there.
  • 75:27 - 75:31
    And I'm beginning to understand why,
    because their currency is publication.
  • 75:31 - 75:33
    Their currency is attribution. And their
  • 75:33 - 75:37
    next funding comes from their last
    victory.
  • 75:38 - 75:42
    (Robert) I think we're all better off for
    the fact that there is this competition.
  • 75:42 - 75:45
    What you want is a system that gets
  • 75:45 - 75:47
    people riled up and try to do something
  • 75:47 - 75:51
    faster, better, and cheaper than the next
    guy.
  • 75:53 - 75:55
    (New Speaker, Male) The environment at
  • 75:55 - 76:00
    Celera was extremely intense and it
    reminded me of finals week at Cal. Tech.
  • 76:00 - 76:02
    Man - And there's a tradition at Cal. Tech
  • 76:02 - 76:05
    that on the very first day of finals week,
  • 76:05 - 76:08
    the Ride of The Valkyries is played at
    full blast.
  • 76:08 - 76:10
    And, so, I thought well, since every
  • 76:10 - 76:12
    week feels like it's finals week here, why
  • 76:12 - 76:15
    don't I play the ride and see what
    happens?
  • 76:15 - 76:17
    So, we got a whole bunch of viking hats,
  • 76:17 - 76:20
    and we end up buying Nerf bows, because
  • 76:20 - 76:24
    we're Nordic Valkyrians. So, the next week
  • 76:24 - 76:26
    we're shooting each other, and we go,
  • 76:26 - 76:29
    "Ya know, there's something not right
    about this." So, we decided the next week
  • 76:29 - 76:34
    that we would start doing raiding parties
    and raid some of the other teams.
  • 76:34 - 76:38
    Unannounced to us, they had been preparing
    themselves.
  • 76:38 - 76:40
    Man - Hey, you guys go to the back-stairs.
  • 76:40 - 76:43
    (Man) - They had little beanie caps, and
  • 76:43 - 76:47
    their own Nerf weapons and the war
    started.
  • 76:47 - 76:51
    (shooting sounds)
    (Ride of the Valkyries music playing)
  • 76:51 - 76:56
    (shooting sounds)
  • 76:56 - 76:59
    (laughing)
  • 76:59 - 77:03
    (shooting)
  • 77:09 - 77:14
    (Man) It's just a release. It's a way of
  • 77:14 - 77:16
    dealing with the pressure, I think.
  • 77:16 - 77:20
    (laughing)
    (shooting)
  • 77:25 - 77:27
    (Man) We all ran like crazy for 5 or 10
  • 77:27 - 77:31
    minutes, and got a little physical
    exercise.
  • 77:33 - 77:36
    And, had a few laughs and then we're
  • 77:36 - 77:40
    ready to really go after it.
    (laughing)
  • 77:40 - 77:43
    (Narrator) The Wagner seems to be working.
  • 77:43 - 77:46
    Output at Celera continues at a relentless
  • 77:46 - 77:51
    pace. Venter insists that the urgency
  • 77:51 - 77:53
    stems not only from a desire to beat
  • 77:53 - 77:55
    the government project, but the firm
  • 77:55 - 77:57
    belief that what's coming out of these
  • 77:57 - 78:00
    machines, all the C's, T's, G's, and A's,
  • 78:00 - 78:04
    will have a profound impact on all our
    lives.
  • 78:05 - 78:08
    (Venter) It's a new beginning in science.
  • 78:08 - 78:10
    And, until we get all that data, that
  • 78:10 - 78:12
    can't really take place.
    Venter - I mean anyone who
  • 78:12 - 78:15
    has cancer, anybody who has a family
  • 78:15 - 78:18
    member with a serious disease, this data
  • 78:18 - 78:20
    and information offers some tremendous
  • 78:20 - 78:23
    hope that things could change in the
    future.
  • 78:23 - 78:27
    Eric Lander - In the past, if you wanted
    to explain Diabetes, you always had to
  • 78:27 - 78:30
    scratch your head and say, "Well, it might
    be something else that we've never seen
  • 78:30 - 78:34
    before." But knowing that you have the
    whole parts list, radically changes
  • 78:34 - 78:38
    biomedical research. Because you can't
    wave hand and say it might be something
  • 78:38 - 78:40
    else. There is no "something else".
  • 78:40 - 78:43
    (Man) 1, 2, 3, 4, 5 C's in a row.
  • 78:43 - 78:46
    (Narrator) In the past,
    Narrator - Finding genes that cause
  • 78:46 - 78:48
    disease was a painstakingly slow process,
  • 78:48 - 78:52
    but, with the completion of a list, it
  • 78:52 - 78:54
    should be much easier to make a direct
  • 78:54 - 78:58
    connection from disease to gene. But how?
  • 78:58 - 79:00
    Well, let's say I'm looking for a gene
  • 79:00 - 79:02
    that causes something, we'll make it
  • 79:02 - 79:04
    Male Pattern Baldness, how would I go
  • 79:04 - 79:07
    about that. Well, I'd want to get a bunch
  • 79:07 - 79:09
    of bald guys. So, here are three bald guys
  • 79:09 - 79:12
    and take their blood and look at their
  • 79:12 - 79:15
    DNA. Now, I'll take three guys with lots
  • 79:15 - 79:18
    of hair, and here's their DNA. And, what
  • 79:18 - 79:21
    if the bald guys all share a particular
  • 79:21 - 79:23
    spelling right here in this spot, which we
  • 79:23 - 79:26
    will call, "The Bald Spot", and at the
  • 79:26 - 79:29
    same spot, you notice the hairy guys have
  • 79:29 - 79:34
    a different spelling. So, is this the gene
  • 79:34 - 79:38
    that causes baldness? Maybe, but probably
  • 79:38 - 79:41
    not. This could be a coincidence. So, how
  • 79:41 - 79:43
    do I improve my chances of finding the
  • 79:43 - 79:45
    specific spelling difference that relates
  • 79:45 - 79:48
    to baldness? It would help if I knew that
  • 79:48 - 79:50
    the bald guys, and the hairy guys had
  • 79:50 - 79:54
    really similar DNA, except for the genes
  • 79:54 - 79:57
    I suspect may make them bald or hairy.
  • 79:57 - 79:59
    Where do I find guys who are very very
  • 79:59 - 80:03
    similar with a few exceptions? A family
  • 80:03 - 80:05
    right? If they were brothers, and fathers,
  • 80:05 - 80:07
    and sons, and cousins, for instance, who
  • 80:07 - 80:09
    share lots of genes...
  • 80:09 - 80:13
    (Narrator) So, let's say these three guys
    are brothers. Astonishing similarities,
  • 80:13 - 80:17
    really, in the face, but notice that one
    of them is hairy and two are bald.
  • 80:17 - 80:20
    Whatever is making this one different,
  • 80:20 - 80:22
    should stand out when you compare their
  • 80:22 - 80:25
    genes. And same for these guys. There's
  • 80:25 - 80:27
    a difference clearly in the photos, but
  • 80:27 - 80:31
    that difference may turn up in the genes.
  • 80:31 - 80:35
    Narrator - You can do the same thing for
    any disease that you'd like. So, if I
  • 80:35 - 80:38
    could comb through the DNA of lots of
    people who are related, and I find some
  • 80:38 - 80:42
    of them are sick and some of them are
    healthy, I might have a much better
  • 80:42 - 80:47
    chance of figuring out which genes are
    involved. But where do I do this?
  • 80:49 - 80:52
    (Narrator) Well, one place is a little
  • 80:52 - 80:55
    island nation in the North Atlantic,
  • 80:55 - 80:59
    Iceland. In many ways, Iceland is the
  • 80:59 - 81:01
    perfect place to look for genes that
  • 81:01 - 81:05
    cause diseases. It's got a tiny population
  • 81:05 - 81:08
    only about 280,000 people, and virtually
  • 81:08 - 81:11
    all of them are descended from the
  • 81:11 - 81:13
    original settlers: Vikings who came here
  • 81:13 - 81:16
    over 1,000 years ago.
  • 81:16 - 81:18
    (music playing)
  • 81:18 - 81:20
    (Kari) If you drive around this country,
  • 81:20 - 81:23
    you will have great difficulties finding
  • 81:23 - 81:25
    any evidence of a dynamic culture that
  • 81:25 - 81:28
    was here for all of these 1,100 years.
  • 81:28 - 81:30
    There are no great buildings, there are
  • 81:30 - 81:32
    no monuments.
  • 81:32 - 81:34
    (Narrator) But, one thing Iceland does
  • 81:34 - 81:36
    have is a fantastic written history,
  • 81:36 - 81:40
    including almost everybody's family tree.
  • 81:43 - 81:47
    And now, it's all in a giant database.
  • 81:47 - 81:49
    Just punch in a social security number and
  • 81:49 - 81:52
    there they are, all of your ancestors,
  • 81:52 - 81:55
    right back to the original Viking.
  • 81:55 - 81:57
    Thordur - So, what we have here is my
  • 81:57 - 81:59
    ancestor tree. I'm here at the bottom,
  • 81:59 - 82:03
    this is my father and mother. My
    grandparents,
  • 82:03 - 82:05
    great-grandparents, and so on. We
  • 82:05 - 82:08
    can find an individual that was one of
  • 82:08 - 82:10
    the original settlers of Iceland. Here we
  • 82:10 - 82:14
    have, "Ketill Bjarnarson" called,
  • 82:14 - 82:16
    "Ketill "flatnefur", meaning he had a
  • 82:16 - 82:19
    flat nose. So, he may have broken it in
  • 82:19 - 82:21
    a fight or something. And we estimate
  • 82:21 - 82:25
    that he was born around the year 805.
  • 82:25 - 82:28
    (music playing)
  • 82:28 - 82:30
    (Narrator) Kari Stephanson is a Harvard
  • 82:30 - 82:32
    trained scientist who saw the potentional
  • 82:32 - 82:34
    gold mine that might be hidden in
  • 82:34 - 82:38
    Iceland's genetic history. He set up a
  • 82:38 - 82:41
    company called, "Decode Genetics" to
  • 82:41 - 82:42
    combine age-old family trees with
  • 82:42 - 82:45
    state-of-the-art DNA analysis and computer
  • 82:45 - 82:48
    technology, and systematically hunt down
  • 82:48 - 82:51
    the genes that cause disease.
  • 82:51 - 82:53
    Kari - Our idea was to try to bring
  • 82:53 - 82:55
    together as much data on healthcare
  • 82:55 - 82:58
    as possible. As much data on genetics
  • 82:58 - 83:01
    as possible, and the genealogy, and simply
  • 83:01 - 83:04
    use the information tools to help us to
  • 83:04 - 83:06
    discover new knowledge. To discover
  • 83:06 - 83:10
    new ways to diagnose, treat, prevent
    diseases.
  • 83:11 - 83:13
    (Narrator) One of Decode's first projects
  • 83:13 - 83:19
    was to look for the genes that might
    cause Osteoarthritis.
  • 83:19 - 83:21
    Ryn Hydr Magnus Dotre had the
  • 83:21 - 83:24
    debilitating disease most of her life.
  • 83:24 - 83:29
    Ryn - The first symptoms appeared when I
  • 83:29 - 83:33
    was 12, and by the age of 14 my knees
  • 83:33 - 83:38
    hurt very badly. No one really paid any
  • 83:38 - 83:41
    attention, that's just the way it was.
  • 83:41 - 83:45
    But, by the age of 39, I'd had enough, so
  • 83:45 - 83:49
    I went to a Doctor.
  • 83:49 - 83:52
    (Narrator) Mrs. Magnus was never alone in
  • 83:52 - 83:56
    her suffering, she is one of 17 children.
  • 83:56 - 83:58
    11 of them were so stricken with arthritis
  • 83:58 - 84:01
    that they had to have their hips replaced.
  • 84:01 - 84:04
    This was exactly the kind of family that
  • 84:04 - 84:07
    Decode was looking for. They got
  • 84:07 - 84:10
    Mrs. Magnus and other members of her
  • 84:10 - 84:12
    family to donate blood samples for DNA
  • 84:12 - 84:15
    analysis. And to find more of her
  • 84:15 - 84:17
    relatives, people she'd never met,
  • 84:17 - 84:19
    Decode just entered her social security
  • 84:19 - 84:22
    number into their giant database and there
  • 84:22 - 84:26
    they were. But which of these people have
  • 84:26 - 84:31
    Arthritis? To find out, Stephanson asked
  • 84:31 - 84:33
    the government of Iceland to give his
  • 84:33 - 84:35
    company exclusive access to the entire
  • 84:35 - 84:38
    country's medical records. In exchange,
  • 84:38 - 84:40
    Decode would pay a million dollars a year
  • 84:40 - 84:43
    plus a share of any profits. That way,
  • 84:43 - 84:45
    Decode could link everything in their
  • 84:45 - 84:48
    computers: DNA, health records, and
  • 84:48 - 84:50
    family trees.
  • 84:50 - 84:53
    Stephanson - This idea was probably more
  • 84:53 - 84:55
    debated than any other issue in the
  • 84:55 - 84:59
    history of the republic. On the eve of
  • 84:59 - 85:01
    that parliamentary vote, on the bill,
  • 85:01 - 85:03
    there was an opinion poll taken that
  • 85:03 - 85:05
    showed that 75% of those that took a
  • 85:05 - 85:07
    stand on the issue, supported the
  • 85:07 - 85:11
    passage of the bill, 25% were against it.
  • 85:11 - 85:14
    (Narrator) Among that 25% against were
  • 85:14 - 85:18
    most of Iceland's doctors.
  • 85:18 - 85:20
    (Tomas Zoega) I first thought that there
  • 85:20 - 85:23
    was something fundamentally wrong
  • 85:23 - 85:25
    in all of this. They do know everything
  • 85:25 - 85:28
    about you. Not only about your medical
  • 85:28 - 85:30
    history, about your medical past, but now
  • 85:30 - 85:35
    have your gene, the DNA. They know about
  • 85:35 - 85:39
    your future, about something about your
  • 85:39 - 85:41
    children, and something about your elders.
  • 85:41 - 85:44
    Bjorn Gundmarsson - We find ourselves
  • 85:44 - 85:46
    paralyzed, because there is really nothing
  • 85:46 - 85:49
    we can do. Because the one who takes the
  • 85:49 - 85:52
    responsibility is the management of the
  • 85:52 - 85:55
    Health Center. If they give away this
  • 85:55 - 85:58
    information from the medical records, they
  • 85:58 - 86:01
    get money. And everybody needs money.
  • 86:01 - 86:05
    Healthcare really needs money.
  • 86:05 - 86:07
    Narrator - So, what's really the problem
  • 86:07 - 86:10
    here? Well, let's take a hypothetical
    example, I'm gonna make all this up. Let's
  • 86:10 - 86:14
    pretend these are medical records of an
    average person, all we'll suppose that
  • 86:14 - 86:20
    right here I see a HIV test, and then over
    here is medication for anxiety after what
  • 86:20 - 86:23
    appears to be a messy divorce, and over
  • 86:23 - 86:26
    here a parent who died of Alzheimer's.
  • 86:26 - 86:28
    Now, this is all stuff that could happen
  • 86:28 - 86:30
    to anybody, but do you want it all in
  • 86:30 - 86:33
    a central computer bank, and do you
  • 86:33 - 86:35
    want it linked in the same computer
  • 86:35 - 86:37
    to all of your relatives? And to your own
  • 86:37 - 86:40
    personal DNA file? And should anybody have
  • 86:40 - 86:42
    to go on a fishing expedition through
  • 86:42 - 86:46
    your medical history, and your DNA?
  • 86:46 - 86:48
    (Narrator) Well, it may frightening, but
  • 86:48 - 86:52
    it also might work. Decode claims it's
  • 86:52 - 86:54
    discovered several genes that may
  • 86:54 - 86:57
    contribute to Osteoarthritis. So, this
  • 86:57 - 86:59
    approach, combining family trees, medical
  • 86:59 - 87:01
    records, and DNA could lead to better
  • 87:01 - 87:04
    drugs, or to cures for a whole range of
  • 87:04 - 87:06
    diseases.
  • 87:06 - 87:08
    Stephanson - To have all of the data in
  • 87:08 - 87:10
    one place so you can use the modern
  • 87:10 - 87:13
    information equipment, to juxtapose the
  • 87:13 - 87:15
    pieces of data and hope that the pieces
  • 87:15 - 87:18
    fit. It's an absolutely fascinating
  • 87:18 - 87:19
    possibility.
  • 87:19 - 87:20
    (printing sounds)
  • 87:20 - 87:22
    (Narrator) Stephanson says no one's forced
  • 87:22 - 87:24
    to do this, and there are elaborate
    privacy
  • 87:24 - 87:27
    protections in place. No names are used,
  • 87:27 - 87:29
    social security numbers are encoded. He
  • 87:29 - 87:31
    also argues that the DNA part of the
  • 87:31 - 87:34
    database is voluntary.
  • 87:34 - 87:38
    Stephanson - The healthcare database
    only contains healthcare information.
  • 87:38 - 87:41
    We can cross reference it with DNA
  • 87:41 - 87:43
    information, but only from those
  • 87:43 - 87:46
    individuals who have been willing to give
  • 87:46 - 87:48
    us blood, allowing us to isolate DNA,
  • 87:48 - 87:50
    genotype it and cross referencing it with
  • 87:50 - 87:53
    the database. Only from those who have
  • 87:53 - 87:56
    deliberately taken that risk. It's not
  • 87:56 - 87:59
    imposed on anyone, and no one who is
  • 87:59 - 88:03
    scared of it, ya know who is really afraid
  • 88:03 - 88:07
    of it, should come and give us blood.
  • 88:07 - 88:09
    (Narrator) DNA databases are popping
  • 88:09 - 88:13
    up all over the world, including the US.
  • 88:13 - 88:15
    They all have rules for protecting privacy
  • 88:15 - 88:18
    but they still make ethicists nervous.
  • 88:18 - 88:23
    (George Annas) I like to use the analogy
    of the DNA molecule to a "future diary".
  • 88:23 - 88:25
    There's a lot of information in the DNA
  • 88:25 - 88:29
    molecule. The reason I call it a "future
    diary",
  • 88:29 - 88:31
    is because I think it's that private.
  • 88:31 - 88:33
    I don't think anybody should be able to
  • 88:33 - 88:36
    open up your future diary, except you.
  • 88:36 - 88:38
    (Narrator) One rather bleak vision of
  • 88:38 - 88:40
    where all of this could lead it presented
  • 88:40 - 88:43
    in the Hollywood film, "GATTACA".
  • 88:43 - 88:46
    This is a world where everybody's DNA,
  • 88:46 - 88:48
    everybody's future diary, is an open
  • 88:48 - 88:51
    book. Everyone who can afford to has
  • 88:51 - 88:54
    their children made to spec. But what
  • 88:54 - 88:57
    happens to the poor slob who's conceived
  • 88:57 - 88:59
    the old fashion way?
  • 88:59 - 89:00
    (Boy from GATTACA film) I'll never
  • 89:00 - 89:04
    understand what possessed my mother
    to her faith in God's hands rather than
  • 89:04 - 89:09
    those of her local geneticists.
    (baby crying)
  • 89:09 - 89:11
    Ten fingers, ten toes; that's all that
  • 89:11 - 89:14
    used to matter. Not now... now, only
  • 89:14 - 89:17
    seconds old, the exact time and cause of
    (suction noise)
  • 89:17 - 89:21
    my death was already known.
  • 89:21 - 89:24
    (baby crying)
  • 89:24 - 89:26
    Nurse - Neurological condition: 60%
  • 89:26 - 89:30
    probability. ADD: 89% probability.
  • 89:30 - 89:36
    Heart disorder....: 99% probability.
  • 89:36 - 89:42
    Life expectancy: 30.2 years.
    Father - 30 years...
  • 89:42 - 89:45
    (Narrator) 30.2 years. The nurse seems to
  • 89:45 - 89:47
    know precisely what is going to happen to
  • 89:47 - 89:49
    this baby, which is ridiculous, right?
  • 89:49 - 89:52
    Never happen. Or... is it possible that
  • 89:52 - 89:54
    one day we will be able to look, with
  • 89:54 - 89:57
    disturbing clarity, into our future?
  • 89:57 - 90:01
    10, 20, even 70 years ahead...
  • 90:02 - 90:04
    George - That is one possible future,
  • 90:04 - 90:06
    where this becomes so routine that, at
  • 90:06 - 90:09
    birth, everyone gets a profile that goes
  • 90:09 - 90:11
    right to their medical record, one copy
  • 90:11 - 90:14
    goes to the FBI, so we have an
    identification
  • 90:14 - 90:17
    system for all possible crimes in the US.
  • 90:17 - 90:20
    One copy goes to your grade school,
  • 90:20 - 90:22
    to the high school, to the college, to the
  • 90:22 - 90:26
    employer, the military. Like a horrific
  • 90:26 - 90:28
    future, although, I have to say there are
  • 90:28 - 90:30
    many in the Biotech industry and the
  • 90:30 - 90:32
    medical professions that think that's
  • 90:32 - 90:34
    a terrific future.
  • 90:34 - 90:36
    (Narrator) In fact, a lot of the
    technology
  • 90:36 - 90:40
    already exists now, today.
  • 90:40 - 90:43
    These guys in the funny suits are making
  • 90:43 - 90:46
    "gene chips". The little needles are
  • 90:46 - 90:49
    dropping tiny, nearly invisible, bits of
  • 90:49 - 90:53
    DNA onto glass slides. And where do the
  • 90:53 - 90:57
    DNA come from? From babies. Thousands
  • 90:57 - 91:02
    of them. Each chip can support 80,000 DNA
    tests.
  • 91:04 - 91:05
    Mark Schena - So, a single chip, in
  • 91:05 - 91:08
    principle, will allow you to test, say,
  • 91:08 - 91:11
    1,000 babies for 80 different human
  • 91:11 - 91:14
    diseases. So, within a few minutes, you
  • 91:14 - 91:16
    can have a readout for thousands, or even
  • 91:16 - 91:20
    tens-of-thousands of babies in a single
    experiment.
  • 91:20 - 91:22
    (Narrator) Already, babies are routinely
  • 91:22 - 91:24
    tested for a handful of diseases, but with
  • 91:24 - 91:26
    gene chips, everybody could be tested for
  • 91:26 - 91:29
    hundreds of conditions.
  • 91:29 - 91:31
    Mark - Knowing is great. Knowing early is
  • 91:31 - 91:35
    even better. And that's really what the
    technology allows us to do.
  • 91:36 - 91:38
    (Narrator) Well, taking a test and knowing
  • 91:38 - 91:40
    is great for the baby, anybody really,
  • 91:40 - 91:42
    as long as there's something you can do
  • 91:42 - 91:44
    about it.
  • 91:44 - 91:46
    Narrator - But think about this, because
  • 91:46 - 91:48
    sometimes there may be a test, but it
  • 91:48 - 91:53
    might take 20 years, or 50 years, 50 years
  • 91:53 - 91:56
    to find a cure. So, you could take the
  • 91:56 - 91:58
    tests, and you could learn that there is
  • 91:58 - 92:00
    a disease coming your way, but you can't
  • 92:00 - 92:03
    do a thing about it. Do you still wanna
  • 92:03 - 92:05
    know? Or, you could take the test, but
  • 92:05 - 92:08
    the test won't say that you're going to
  • 92:08 - 92:10
    get the disease. It will simply say that
  • 92:10 - 92:14
    you may get a disease. And, as you know,
  • 92:14 - 92:16
    there is a big difference between
  • 92:16 - 92:20
    "you will" and "you may".
  • 92:20 - 92:23
    (women talking in distance)
  • 92:23 - 92:25
    (Narrator) Lisa Capos and Lori Segal are
  • 92:25 - 92:27
    sisters who shared the wrenching
  • 92:27 - 92:31
    experience of cancer in the family. Way
  • 92:31 - 92:34
    back, there was three sisters in 1979.
  • 92:34 - 92:36
    The youngest of the three, Melanie, was
  • 92:36 - 92:39
    diagnosed with ovarian cancer.
  • 92:39 - 92:41
    (Lisa) When my sister was diagnosed, my
  • 92:41 - 92:44
    response was disbelief. She was
  • 92:44 - 92:49
    Lisa - 30 years old, and I'd never known
  • 92:49 - 92:53
    anybody of that age to have ovarian
    cancer.
  • 92:53 - 92:54
    (Narrator) Melanie fought her cancer
  • 92:54 - 92:59
    for four years, but died in 1983.
  • 92:59 - 93:01
    It seemed an isolated piece of bad luck,
  • 93:01 - 93:04
    but then, just about a year later, Lisa
  • 93:04 - 93:07
    discovered that she had breast cancer.
  • 93:07 - 93:10
    She was only 34, but the cancer hadn't
  • 93:10 - 93:12
    spread, so the long-term outlook seemed
  • 93:12 - 93:15
    optimistic.
  • 93:15 - 93:16
    Lisa - I actually had a radiation
  • 93:16 - 93:20
    therapist, who was, tops in the field.
  • 93:20 - 93:24
    Wrote many books on breast cancer, and
  • 93:24 - 93:28
    was very optimistic. And what I remember
  • 93:28 - 93:30
    him saying was that he and I would grow
  • 93:30 - 93:33
    old together.
  • 93:33 - 93:36
    (Narrator) And Lisa was fine for 12 years,
  • 93:36 - 93:38
    and then she found another lump in the
  • 93:38 - 93:40
    same breast.
  • 93:40 - 93:44
    Lisa - It was the worst fear come true.
  • 93:44 - 93:48
    The first time I could hold onto hope,
  • 93:48 - 93:50
    the second time nobody was talking
  • 93:50 - 93:54
    with me about living to be old.
  • 93:54 - 93:56
    (Narrator) When Lisa discovered her
  • 93:56 - 93:59
    second cancer in 1996, scientists were
  • 93:59 - 94:00
    just beginning to work out the link
  • 94:00 - 94:02
    between breast and ovarian cancers
  • 94:02 - 94:04
    that run in families.
  • 94:05 - 94:08
    Mary Claire King was one of the scientists
  • 94:08 - 94:10
    who discovered the changes, or mutations
  • 94:10 - 94:12
    in two specific genes make a woman's
  • 94:12 - 94:15
    risk of breast and ovarian cancer much
  • 94:15 - 94:18
    higher. The genes are called, "BRCA-1" and
  • 94:18 - 94:21
    "BRCA-2"
  • 94:21 - 94:23
    Mary - BRCA-1 and BRCA-2 are perfectly
  • 94:23 - 94:27
    healthy, normal genes that all of us have.
  • 94:27 - 94:29
    But in a few families, mutations in these
  • 94:29 - 94:33
    genes are inherited.
  • 94:33 - 94:35
    (Narrator) - So, in a normal gene, we're
  • 94:35 - 94:37
    gonna spell it out for you here, letter by
  • 94:37 - 94:39
    letter, this is the normal sequence
  • 94:39 - 94:47
    ending, "GTAGCAGT". Now, we're gonna make
  • 94:47 - 94:51
    a copy. Now, we're gonna lose two of the
  • 94:51 - 94:54
    letters, just two, and then see, watch
  • 94:54 - 94:56
    them shift over. You see that? This new
  • 94:56 - 94:58
    configuration is a mutation which can
  • 94:58 - 95:01
    often cause breast cancer.
  • 95:01 - 95:03
    Mary - In the United States and Western
  • 95:03 - 95:06
    Europe, and Canada, the risk of developing
  • 95:06 - 95:08
    breast cancer for women in the population
  • 95:08 - 95:11
    as a whole, is about 10% over the course
  • 95:11 - 95:13
    of her lifetime. With, of course, most of
  • 95:13 - 95:16
    that risk occurring later in her life.
  • 95:16 - 95:18
    For a woman with a mutation in BRCA-1 or
  • 95:18 - 95:21
    BRCA-2, the lifetime risk of breast cancer
  • 95:21 - 95:25
    is about 80%. It's very high.
  • 95:25 - 95:27
    (Narrator) Right around the time of Lisa's
  • 95:27 - 95:29
    second bout of breast cancer, a test for
  • 95:29 - 95:33
    BRCA mutations became available. Lisa and
  • 95:33 - 95:36
    her sister, Lori, decided to be tested.
  • 95:36 - 95:39
    (Lori) I do remember the day that I went
  • 95:39 - 95:41
    to find out the results.
  • 95:41 - 95:44
    Lori - Panic, terror. I mean, what was I
  • 95:44 - 95:51
    gonna find out? Talking about the blood
  • 95:51 - 95:53
    surging through your temples, I mean
  • 95:53 - 95:57
    I just remember sheer terror.
  • 95:57 - 96:00
    (Narrator) Turns out, Lori was fine, but
  • 96:00 - 96:02
    Lisa discovered that she does carry a
  • 96:02 - 96:07
    BRCA mutation. It is not easy waking
  • 96:07 - 96:09
    up every morning, wondering if today's
  • 96:09 - 96:11
    the day you may get sick.
  • 96:11 - 96:14
    (Doctor) Any questions about the results
  • 96:14 - 96:16
    from the biopsy from April?
  • 96:16 - 96:19
    Lisa - No questions about the results,
  • 96:19 - 96:21
    again it feels like often my life is
  • 96:21 - 96:24
    dodging bullets...
  • 96:25 - 96:27
    (Narrator) With the second cancer, Lisa
  • 96:27 - 96:29
    had her right breast completely removed.
  • 96:29 - 96:31
    And then another operation to take out her
  • 96:31 - 96:33
    ovaries.
  • 96:33 - 96:35
    (Nurse) Just make a tight fist until I'm
    in.
  • 96:35 - 96:40
    (Narrator) She also has a high risk of
    cancer in her left breast. BRCA mutations
  • 96:40 - 96:43
    are relatively rare, and only cause maybe
  • 96:43 - 96:46
    only 5 or 10% of all breast cancer.
  • 96:46 - 96:48
    But knowing that there's a BRCA mutation
  • 96:48 - 96:52
    in the family affects everybody.
  • 96:52 - 96:55
    (Man) The gene doesn't go away. The time
  • 96:55 - 96:57
    passed since the last cancer doesn't buy
  • 96:57 - 96:59
    you the safety.
  • 96:59 - 97:03
    Man - And, the consequences run through
  • 97:03 - 97:08
    the family. And, I suppose, that, for my
  • 97:08 - 97:11
    daughter, who yet has not shown any
  • 97:11 - 97:19
    significant impact of this. The knowledge
  • 97:19 - 97:21
    that there's a genetic component that she
  • 97:21 - 97:26
    can't deny. Will, I'm sure, color her life
  • 97:26 - 97:29
    in serious ways.
  • 97:29 - 97:32
    (Narrator) Lisa's son, Justin, is 21. Her
  • 97:32 - 97:36
    daughter, Alana is 18. There is a 50/50
  • 97:36 - 97:38
    chance that each of them has inherited
  • 97:38 - 97:41
    the BRCA mutation from Lisa. The only way
  • 97:41 - 97:44
    to know, would be to take a test.
  • 97:44 - 97:47
    And when should they do that? When is the
  • 97:47 - 97:49
    right time?
  • 97:49 - 97:53
    Alana - I actually never really thought
    about it until biology this year, when my
  • 97:53 - 97:57
    teacher posed a hypothetical, supposedly,
    question to people saying, "What would
  • 97:57 - 98:00
    you do? Can you imagine what you would
    do if you were faced with a situation
  • 98:00 - 98:06
    where you knew that you might have this
    disease that would be deadly, or cause
  • 98:06 - 98:08
    you to be sick? And you could do a test
  • 98:08 - 98:11
    to find out whether or not you had it."
  • 98:11 - 98:13
    And I was sitting there in class saying,
  • 98:13 - 98:15
    "Maybe it's not so hypothetical."
  • 98:15 - 98:19
    (Narrator) And then in her senior year of
    highschool, Alana felt a lump in her own
  • 98:19 - 98:21
    breast.
  • 98:21 - 98:25
    (Alana) I did have the, "Oh, it can't be
    happening to me. Not yet." kind of thing.
  • 98:25 - 98:27
    I mean, I have the reservation in the
  • 98:27 - 98:30
    back of my mind
    Alana - that eventually it may very well
  • 98:30 - 98:32
    happen to me. And, if it does, I'll fight
  • 98:32 - 98:34
    it then. I'll deal with it then, but I
  • 98:34 - 98:37
    don't expect, or I definitely didn't
  • 98:37 - 98:39
    expect for this to be happening to me
  • 98:39 - 98:43
    when I was 17 years old.
  • 98:44 - 98:47
    (Narrator) Alana's lump was not cancer.
  • 98:47 - 98:49
    And for now, she doesn't want the test.
  • 98:49 - 98:51
    Because, if she knew that she had the bad
  • 98:51 - 98:53
    gene, she'd only have two options:
  • 98:53 - 98:55
    The choice of removing her breasts and
  • 98:55 - 98:57
    ovaries to try to reduce her risk. Or just
  • 98:57 - 99:01
    to be closely monitored, and wait.
  • 99:01 - 99:04
    Lisa - She's followed every year. Seems
  • 99:04 - 99:09
    a little young to, ya know, have her have
  • 99:09 - 99:11
    to face that. On the other hand, it also
  • 99:11 - 99:13
    feels like the belt-and-suspenders
  • 99:13 - 99:15
    technique, and we just have to do
  • 99:15 - 99:19
    everything we can do.
  • 99:19 - 99:20
    (Narrator) In the next 20 years, this
  • 99:20 - 99:24
    family's predicament will become more
    and more common as more and more
  • 99:24 - 99:28
    genes are linked to more and more diseases
    and more tests become available. But we
  • 99:28 - 99:32
    will all have to ask, "Do we want to
    know?"
  • 99:32 - 99:35
    And, when we know, can we live with an
  • 99:35 - 99:40
    answer that says, "Maybe... but maybe
    not?"
  • 99:40 - 99:43
    (Lisa) Driving home from work today, I was
  • 99:43 - 99:46
    tuned into public radio. And there was a
  • 99:46 - 99:48
    professor of astronomy talking about a
  • 99:48 - 99:50
    brand new telescope to look into the
  • 99:50 - 99:53
    galaxies. And they're calling it the
  • 99:53 - 99:57
    equivalent of The Human Genome Project.
  • 99:57 - 100:00
    And I was thinking, "Hmm, not quite the
  • 100:00 - 100:02
    equivalent of The Human Genome Project."
  • 100:02 - 100:05
    because it's without some of the ethical,
  • 100:05 - 100:10
    moral angst, real-people issues, where,
  • 100:10 - 100:12
    it's a bit of a roller-coaster ride
  • 100:12 - 100:15
    between, ya know, this is gonna hold
  • 100:15 - 100:17
    answers, and hope, and treatments,
  • 100:17 - 100:21
    and interventions, and cure versus - it's
  • 100:21 - 100:25
    not clear what this all means.
  • 100:27 - 100:30
    (Narrator) And if things aren't clear now,
  • 100:30 - 100:32
    what about the future when we may not
  • 100:32 - 100:35
    only cure disease but do so much more.
  • 100:35 - 100:39
    (Doctor) Your extracted eggs are, Marie,
  • 100:39 - 100:45
    have been fertilized with Antonio's sperm.
  • 100:45 - 100:48
    You have specified Hazel eyes, dark hair,
  • 100:48 - 100:51
    and fair skin. All that remains is to
  • 100:51 - 100:55
    select the most compatible candidate.
  • 100:55 - 100:57
    I've taken the liberty of eradicating any
  • 100:57 - 101:01
    potentially prejudicial condition.
    Premature baldness, myopia, alcoholism,
  • 101:01 - 101:03
    obesity, and so...
    Woman - We didn't want... I mean...
  • 101:03 - 101:07
    diseases, yes, but um..
    Man - Right, we were just wondering
  • 101:07 - 101:09
    if, if it's good to just leave a few
  • 101:09 - 101:11
    things to chance.
    Doctor - We want to give your child the
  • 101:11 - 101:14
    best possible start.
  • 101:14 - 101:16
    Keep in mind, this child is still you.
  • 101:16 - 101:19
    Simply the best of you. You could
  • 101:19 - 101:21
    conceive naturally a thousand times and
  • 101:21 - 101:24
    never get such a result.
  • 101:24 - 101:26
    (Francis) GATTACA really raised some
  • 101:26 - 101:28
    interesting points. The technology that's
  • 101:28 - 101:31
    being described there is, in fact, right
  • 101:31 - 101:34
    in front of us or almost in front of us.
  • 101:34 - 101:35
    Narrator - That seems to me almost
  • 101:35 - 101:37
    extremely likely to happen. Cause, what
  • 101:37 - 101:40
    parent wouldn't want.. ya know, to
  • 101:40 - 101:42
    introduce a child that wouldn't have,
  • 101:42 - 101:44
    at least, be where all the other kids
  • 101:44 - 101:44
    could be?
  • 101:44 - 101:48
    Francis - That's why this scenario is
    chilling. It portrayed a society where
  • 101:48 - 101:51
    genetic determinism had basically run
  • 101:51 - 101:54
    wild. I think society, in general, has
  • 101:54 - 101:56
    smiled upon the use of genetics for
  • 101:56 - 101:58
    preventing terrible diseases. But, when
  • 101:58 - 102:00
    you begin to blur that boundary of making
  • 102:00 - 102:02
    your kids genetically different, in a way
  • 102:02 - 102:05
    that enhances their performance in some
  • 102:05 - 102:09
    way, that starts to make most of us
    uneasy.
  • 102:10 - 102:12
    (Narrator) What if we lived in the world
  • 102:12 - 102:16
    of Star Trek Voyager? Talk about uneasy.
  • 102:16 - 102:19
    Actress - Computer, access Belana Toras'
  • 102:19 - 102:20
    medical file.
  • 102:20 - 102:22
    (Narrator) Lieutenant Toras is 50% human
  • 102:22 - 102:25
    and 50% Klingon.
    Actress - Project a holographic image of
  • 102:25 - 102:29
    the baby.
    (Narrator) She's also 100% pregnant.
  • 102:30 - 102:32
    Actress - Now extrapolate what the child's
  • 102:32 - 102:36
    facial features will look like at 12 years
    old.
  • 102:36 - 102:37
    (Narrator) Like any caring parent, she
  • 102:37 - 102:40
    doesn't want her child to be teased. For
  • 102:40 - 102:43
    having a forehead that looks like... well,
  • 102:43 - 102:45
    like a tire tread.
    Actress - Display the fetus genome.
  • 102:45 - 102:49
    Delete the following gene sequences.
    (Narrator) But here's the twist...
  • 102:49 - 102:52
    She can do something about it.
  • 102:52 - 102:54
    Actress - Extrapolate what the child
  • 102:54 - 102:58
    would look like with those genetic
    changes.
  • 102:58 - 103:00
    (Narrator) Hmm, she threw in some blonde
    (music playing)
  • 103:00 - 103:03
    hair, too.
  • 103:03 - 103:06
    And is this limit? Or, could we go even
  • 103:06 - 103:09
    further?
    Actress - Save changes.
  • 103:09 - 103:11
    Narrator - If you can eventually isolate
  • 103:11 - 103:14
    all of these things, can you then build a
  • 103:14 - 103:17
    creature that has never existed before?
  • 103:17 - 103:20
    For example, I would like the eyesight of
  • 103:20 - 103:23
    a hawk. And I'd like the hearing of a dog.
  • 103:23 - 103:24
    Otherwise, I'm quite content exactly how
  • 103:24 - 103:27
    I am. So, could I pluck the eyesight and
  • 103:27 - 103:30
    the hearing and patch it in?
  • 103:30 - 103:32
    Eric - Well, we don't know. We really
  • 103:32 - 103:36
    don't know how that engineering occurs
  • 103:36 - 103:38
    and how we can improve on it. It'd be
  • 103:38 - 103:41
    very much like getting a pile of parts to
  • 103:41 - 103:44
    a Boeing 777, and a whole pile of parts
  • 103:44 - 103:47
    to an Airbus, and saying, "Well, I'm gonna
  • 103:47 - 103:48
    mix and match some of these, so it'll
  • 103:48 - 103:52
    have some of the properties... I'll make
    it a little fatter, but I also wanna make
  • 103:52 - 103:56
    it a little shorter." By the time you were
    done, you'd think you'd made lots of
  • 103:56 - 104:00
    clever improvements, but the thing
    wouldn't get off the ground. It's a very
  • 104:00 - 104:02
    complex machine, and going in with a
  • 104:02 - 104:05
    monkey wrench to change a piece, odds
  • 104:05 - 104:07
    are, most changes we would make today,
  • 104:07 - 104:11
    almost ALL changes we'd make today would
    break the machine.
  • 104:11 - 104:15
    (Narrator) We may not be able to
    genetically modify humans or klingons,
  • 104:15 - 104:18
    yet. But we do do it to plants and animals
  • 104:18 - 104:20
    everyday. Look at this stuff, tobacco
  • 104:20 - 104:23
    plants with a gene from a firefly. And
  • 104:23 - 104:26
    they use that same insect gene to create
  • 104:26 - 104:28
    glowing mice.
  • 104:28 - 104:30
    Narrator - So, it's theoretically possible
  • 104:30 - 104:32
    that we could create humans with other
  • 104:32 - 104:34
    advantages that borrowed from other
  • 104:34 - 104:36
    creatures?
    Eric - That's right, but the humility of
  • 104:36 - 104:40
    science right now is to appreciate how
    little we know about how you could
  • 104:40 - 104:43
    even begin to go about that. That is the
  • 104:43 - 104:45
    difference between 20th century and
  • 104:45 - 104:48
    21st century biology, is, it's now our job
  • 104:48 - 104:50
    in this century to figure out how the
  • 104:50 - 104:54
    parts fit together.
    (music playing)
  • 104:56 - 104:58
    (Narrator) And just as the 20th century
  • 104:58 - 105:00
    was winding down, the race to finish the
  • 105:00 - 105:03
    genome was full throttle. The competitive
  • 105:03 - 105:06
    juices were flowing.
  • 105:06 - 105:10
    Venter - I am competitive, but, when the
  • 105:10 - 105:12
    social order doesn't allow you to make
  • 105:12 - 105:15
    progress, and it doesn't for most people,
  • 105:15 - 105:18
    I said, "To hell with the social order,
  • 105:18 - 105:21
    I'll find a new way to do it."
  • 105:21 - 105:23
    (Tony White) It changed the paradigm on
  • 105:23 - 105:25
    people, and people don't like that.
  • 105:25 - 105:27
    Tony - It was very offensive to these
  • 105:27 - 105:29
    people. "How dare they?" Ya know,
  • 105:29 - 105:31
    rain on our parade, this is our turf.
  • 105:31 - 105:36
    Eric - This was a challenge to the whole
    idea of public generation of data. That's
  • 105:36 - 105:39
    what offended people, was that we really
  • 105:39 - 105:41
    felt deeply that these were data that had
  • 105:41 - 105:43
    to be available for everybody. And there
  • 105:43 - 105:45
    was an attempt to claim the public
  • 105:45 - 105:47
    imagination for the proposition that
  • 105:47 - 105:49
    these data were better done in some
  • 105:49 - 105:51
    private fashion and owned.
  • 105:51 - 105:55
    Tony - You wanna say, "Well, wait a
    minute. Ya know, if you could do it in
  • 105:55 - 105:58
    two years, why weren't ya doing it in
    two years? Why do we have to come
  • 105:58 - 106:01
    along to turn a 15 year project into a
    two year project?"
  • 106:01 - 106:05
    Eric - I must say that The Human Genome
    Project had a tremendous amount of
  • 106:05 - 106:09
    internal competition, even amongst the
    academic groups. There's competition
  • 106:09 - 106:11
    amongst academic scientists, to be sure.
  • 106:11 - 106:13
    And more than anything, there's
  • 106:13 - 106:17
    competition against disease. There's a
  • 106:17 - 106:20
    strong sense that what we're trying
  • 106:20 - 106:21
    to find out is the most important
  • 106:21 - 106:25
    information that you could possibly get.
  • 106:25 - 106:28
    Tony - I don't know, I mean, I hope that
    this will all go away.
  • 106:28 - 106:31
    (Narrator) In June of 2000, it kind of did
  • 106:31 - 106:35
    go away.
    (orchestral music playing)
  • 106:39 - 106:42
    The contentious race to finish the genome
  • 106:42 - 106:44
    came to an end.
    (Announcer) Ladies and gentlemen, the
  • 106:44 - 106:47
    President of the United States.
  • 106:47 - 106:50
    (Narrator) And the winner was...
  • 106:50 - 106:52
    Well, you probably heard, they decided
  • 106:52 - 106:55
    to call it a tie.
  • 106:55 - 106:56
    (Francis) I think both Craig and I were
  • 106:56 - 106:59
    really tired of the way in which the
  • 106:59 - 107:02
    representations had played out and wanted
  • 107:02 - 107:06
    to see that sort of put behind us. It was
  • 107:06 - 107:09
    probably not good for Celera, as a
  • 107:09 - 107:11
    business to have this image of being
  • 107:11 - 107:13
    sort of always in contention with the
  • 107:13 - 107:15
    public project. It certainly wasn't good
  • 107:15 - 107:17
    for the public project to be seen as
  • 107:17 - 107:21
    battling with the private sector
    enterprise.
  • 107:22 - 107:24
    (Narrator) President Clinton, himself,
  • 107:24 - 107:26
    got the public guys and the Celera guys
  • 107:26 - 107:29
    to play nice, shake hands, and share
  • 107:29 - 107:33
    credit for sequencing the genome.
    (clapping)
  • 107:37 - 107:38
    President Clinton - Nearly two centuries
  • 107:38 - 107:45
    ago, in this room, on this floor, Thomas
  • 107:45 - 107:48
    Jefferson and a trusted aid spread out a
  • 107:48 - 107:52
    magnificent map. The aid was Merryweather
  • 107:52 - 107:54
    Louis and the map was the product of his
  • 107:54 - 107:57
    courageous expedition across the American
  • 107:57 - 108:01
    frontier, all the way to the Pacific.
  • 108:01 - 108:03
    Today, the world is joining us here in the
  • 108:03 - 108:06
    East Room, to behold a map of even greater
  • 108:06 - 108:09
    significance. We are here to celebrate the
  • 108:09 - 108:12
    completion of the first survey of the
  • 108:12 - 108:15
    entire human genome. Without a doubt,
  • 108:15 - 108:17
    this is the most important, most
  • 108:17 - 108:21
    wondrous map ever produced by humankind.
  • 108:22 - 108:23
    (Narrator) And what does this map the
  • 108:23 - 108:25
    President is talking about, what does it
  • 108:25 - 108:26
    look like?
  • 108:26 - 108:28
    Narrator - When we look across the
  • 108:28 - 108:30
    landscape of our DNA for the 30,000 genes
  • 108:30 - 108:33
    that make up a human-being, what do we
  • 108:33 - 108:34
    see?
  • 108:34 - 108:37
    Eric - The genome is very lumpy.
    Narrator - Very lumpy?
  • 108:37 - 108:40
    Eric - Very lumpy, very uneven. You might
  • 108:40 - 108:42
    think if we have 30,000 genes they're
  • 108:42 - 108:44
    distributed kind of uniformly across the
  • 108:44 - 108:46
    chromosomes. Not so.
  • 108:46 - 108:48
    (music playing)
  • 108:48 - 108:50
    (Eric) They're distributed like people are
  • 108:50 - 108:53
    distributed in America. They're all
  • 108:53 - 108:56
    bunched up in some places, and then you
  • 108:56 - 108:58
    have vast plains that don't have a lot of
  • 108:58 - 109:01
    people in them.
    (car horn honking)
  • 109:01 - 109:04
    It's like that with the genes.
  • 109:04 - 109:06
    (music playing)
  • 109:06 - 109:09
    (Eric) There are really gene-dense regions
  • 109:09 - 109:11
    that might have 15 times the density of
  • 109:11 - 109:15
    genes. Sort of a New York City over here.
  • 109:15 - 109:19
    (music playing)
  • 109:20 - 109:22
    (Eric) And there are other regions that
  • 109:22 - 109:24
    might go for two-million letters, and
  • 109:24 - 109:28
    there's not a gene to be found in there.
  • 109:30 - 109:31
    Eric - The remarkable thing about our
  • 109:31 - 109:33
    genome, is how little "gene" there is
  • 109:33 - 109:35
    in it. We have three billion letters of
  • 109:35 - 109:39
    DNA, but only 1 to 1.5% of it is gene.
  • 109:39 - 109:41
    Narrator - 1.5%?
  • 109:41 - 109:45
    Eric - The rest of it, 99% of it, is
    stuff.
  • 109:45 - 109:47
    Narrator - Stuff? This is the technical
    term?
  • 109:47 - 109:49
    Eric - A technical term. More than half
  • 109:49 - 109:53
    of your total DNA is not really yours. It
  • 109:53 - 109:58
    consists of selfish DNA elements that
  • 109:58 - 110:00
    somehow got into our genomes about
  • 110:00 - 110:02
    a billion-and-a-half years ago, and have
  • 110:02 - 110:04
    been hopping around making copies of
  • 110:04 - 110:06
    themselves. To those selfish DNA elements,
  • 110:06 - 110:10
    we're merely a host for them.
    Narrator - Wait a second...
  • 110:10 - 110:13
    Eric - They view the human being just as a
  • 110:13 - 110:16
    vehicle for transmitting themselves.
    Narrator - Wait, wait, wait...
  • 110:16 - 110:19
    We have, in each and every one of our
    cells that carry DNA, we have these
  • 110:19 - 110:22
    little... they're not beings, they're just
  • 110:22 - 110:24
    hitchhikers...
    Eric - Yeah.
  • 110:24 - 110:26
    Hitchhiking hunks of DNA.
  • 110:26 - 110:29
    Narrator - And they've been in us for how
    long?
  • 110:29 - 110:31
    Eric - About a billion or a half years
    or so.
  • 110:31 - 110:35
    Narrator - And all they've done, as far as
    you can say, is stay there and multiply?
  • 110:35 - 110:36
    Eric - Well, they move around.
  • 110:36 - 110:38
    Narrator - And what is that? What do
  • 110:38 - 110:40
    you call that? I mean, it's not an animal,
  • 110:40 - 110:42
    it's not a vegetable. It's just...
  • 110:42 - 110:44
    Eric - It's a gene that knows how to look
  • 110:44 - 110:46
    out for itself and nothing else.
  • 110:46 - 110:48
    Narrator - And it's just riding around
    in us?
  • 110:48 - 110:52
    The majority of our genome is this stuff,
  • 110:52 - 110:54
    not us.
    Narrator - Wow.
  • 110:54 - 110:56
    (music playing)
  • 110:56 - 110:58
    (Narrator) It is a little humbling to
  • 110:58 - 111:02
    think that we, the paragon of animals,
  • 111:02 - 111:05
    the architects of great civilizations,
  • 111:05 - 111:07
    are used as taxi cabs by a bunch of
  • 111:07 - 111:09
    freeloading parasites who could care
  • 111:09 - 111:12
    less about us, but, that's the mystery of
  • 111:12 - 111:16
    it all.
    (lightning)
  • 111:16 - 111:20
    (music playing)
  • 111:25 - 111:27
    (Eric) - You come away from reading the
  • 111:27 - 111:29
    genome, recognizing that we are so
  • 111:29 - 111:31
    similar to every other living thing on
  • 111:31 - 111:33
    this planet.
  • 111:33 - 111:37
    (music playing)
  • 111:40 - 111:43
    (birds chirping)
  • 111:44 - 111:47
    (Eric) And every innovation in us... we
  • 111:47 - 111:49
    didn't really invent it. These were all
  • 111:49 - 111:52
    things inherited from our ancestors.
  • 111:52 - 111:56
    (music playing)
  • 111:57 - 112:00
    (Eric) This gives you a tremendous
  • 112:00 - 112:03
    respect for life. It gives you a respect
  • 112:03 - 112:06
    for the complexity of life, the innovation
  • 112:06 - 112:10
    of life. And the tremendous connectivity
  • 112:10 - 112:13
    amongst all life on the planet.
  • 112:13 - 112:17
    (music playing)
  • 112:21 - 112:24
    (Narrator) We are, in a very real sense,
  • 112:24 - 112:27
    ordinary creatures. Our parts are
  • 112:27 - 112:29
    interchangeable with all the other
  • 112:29 - 112:32
    animals, and even the plants around us.
  • 112:32 - 112:35
    And yet, we know that there's something
  • 112:35 - 112:39
    about us that is truly extraordinary.
  • 112:40 - 112:43
    What it is, we don't know, but what it
  • 112:43 - 112:47
    does is, it let's us ask questions and
  • 112:47 - 112:50
    investigate, and contemplate the
  • 112:50 - 112:52
    messages buried in a molecule shaped
  • 112:52 - 112:57
    like a twisted staircase. That's what we,
  • 112:57 - 113:04
    and maybe we alone, can do. We can wonder.
  • 113:04 - 113:08
    (music playing)
  • 113:10 - 113:12
    (Narrator) This program raises many
  • 113:12 - 113:14
    difficult questions, and we do want to
  • 113:14 - 113:16
    know what you think. So, please logon
  • 113:16 - 113:19
    to Nova's website and take our survey.
  • 113:19 - 113:21
    Also, you can see how scientists pinpoint
  • 113:21 - 113:24
    a gene, find out how sequencing works,
  • 113:24 - 113:28
    and more at PBS.org or AmericaOnline,
  • 113:28 - 113:30
    keyword: PBS.
  • 113:30 - 113:34
    (music playing)
  • 113:36 - 113:44
    (music playing)
  • 113:47 - 113:49
    (Announcer) To order this show or any
  • 113:49 - 113:51
    other Nova program for $19.95 plus
  • 113:51 - 113:55
    shipping and handling, call WGBH Boston
  • 113:55 - 113:59
    Video at 1-800-255-9424.
  • 114:00 - 114:03
    By inserting just one gene, our food can
  • 114:03 - 114:07
    grow bigger, resist disease, and feed the
    world.
  • 114:07 - 114:08
    (Man) This is a mass genetic
  • 114:08 - 114:11
    experiment that's going on in our diet.
  • 114:11 - 114:13
    (Announcer) Harvest of Fear, a NOVA
  • 114:13 - 114:15
    frontline special report.
  • 114:15 - 114:19
    (music playing)
  • 114:25 - 114:29
    (Announcer) NOVA is a production of WGBH
    Boston.
  • 114:29 - 114:33
    (music playing)
  • 114:33 - 114:36
    (Announcer) Major funding for NOVA is
  • 114:36 - 114:38
    provided by the Park Foundation.
  • 114:38 - 114:41
    Dedicated to education and quality
  • 114:41 - 114:45
    television.
  • 114:46 - 114:48
    (Female Announcer) Scientific achievement
  • 114:48 - 114:51
    is fueled by the simple desire to make
  • 114:51 - 114:57
    things clear. Sprint PCS is proud to
  • 114:57 - 114:59
    support NOVA.
  • 114:59 - 115:01
    (Male Announcer) This program is funded
  • 115:01 - 115:04
    in part by the Northwestern Mutual
    Foundation.
  • 115:04 - 115:06
    Some people already know, Northwestern
  • 115:06 - 115:08
    Mutual can help plan for your children's
  • 115:08 - 115:11
    education. Are you there yet? Northwestern
  • 115:11 - 115:14
    Mutual Financial Network.
  • 115:14 - 115:16
    (Announcer) Major funding for this program
  • 115:16 - 115:20
    is provided by the National Science
    Foundation,
  • 115:20 - 115:24
    America's investment in the future.
  • 115:24 - 115:27
    And by, the Corporation for Public
    Broadcasting,
  • 115:27 - 115:29
    and by contributions to your PBS station
  • 115:29 - 115:33
    by viewers like you. Thank you.
  • 115:33 - 115:37
    (music playing)
  • 115:37 - 115:40
    (Announcer) This is PBS.
Title:
Cracking the Code Of Life ✪ PBS Nova Documentary HD
Description:

more » « less
Video Language:
English
Duration:
01:55:42

English subtitles

Revisions