Healthcare’s Next Frontier: The Power to Extend Life

Solving the riddle of the genetic code

Published in

StartUp Health

10 min readApr 13, 2016

Sue Siegel, CEO of GE Ventures, sits down with Health Transformer J. Craig Venter, PhD, one of the leading scientists of the 21st century, at the StartUp Health Festival to chat about the power of our “software of life,” the human genetic code.

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GUEST: J. Craig Venter, PhD, Human Longevity, Inc.

HOST: Sue Siegel, GE Ventures

LOCATION: StartUp Health Festival, San Francisco, CA

IN THIS EPISODE:

A Synopsis of Craig Venter + Human Longevity, Inc.
Genotypes and Phenotypes
Amassing the Data: A Digital Challenge

Show Notes and Key Takeaways

(Access the full transcript here.)

A Synopsis of Craig Venter

[0:54] Sue Siegel: I am going to introduce a man that needs no introduction; I suspect most of you know that. If you think back to ESTs. How many of you know what an EST is, actually? Different audience, aha. So, Craig, way back when, when he was at the NIH, essentially found ESTs, which are expressive sequence tags. And a lot of people said, “You cannot sequence the human genome using ESTs.” This was the first, sort of, incarnation of doing sequencing.
[1:35] Sue: He then went off to found HGS, Human Genome Sciences. He was a co-founder there. He founded TIGR, which, for those of you who don’t know, TIGR, different audience, this was The Institute for Genomic Research. And this was the first sequence of a prokaryote, so bugs, essentially was done by Craig.
[1:59] Sue: And then we went off and he said, Now, I’m gonna start Celera. He had this vision that in fact if you took a bunch of machines. You put them all together. And you put the power of company and resources behind it, you could sequence the human genome faster than anybody else could. And in fact, what did he do? You know that the human genome was supposed to be done by the year, sort of, 2002–2003? When was it done? 2000.
[3:01] Sue: He created the JCVI Institute. And he said he was going to go comb the oceans. And see, and sample the oceans. And he went and sampled, and he came back, and he sequenced millions and millions of organisms of which he discovered a number of them. And during that time he also created synthetic genomics. He synthesized the first organism. So, again, Craig not having done enough, said well, you know, now, I’m actually going to bring together all the things I’ve been doing before, but I want to do something about translation of it too. And I’m going to create Human Longevity Institute.

Human Longevity, Inc.

[5:19] J. Craig Venter: It’s Human Longevity, Inc. People confuse these things, because the Venter Institute’s a not-for-profit institute. Human Longevity is definitely for-profit. It’s not yet profitable, but it’s not intended to be a not-for-profit. So, the goal was when we finished the first genome, it was hard to interpret. In fact, that hasn’t changed much in 15 years. And we realize we needed really large numbers of genomes. And we needed phenotype information to go with them. So, we’ve set out to try and do a million genomes by 2020 and collect phenotype information on everybody whose genome we’re sequencing.
[6:00] Craig: So, it’s a huge informatics challenge. It’s a computational challenge. And, we’re adding into it machine learning to try and put this data together. So, it’s mixing all these together. Just in our startup phase, we passed 20,000 human genomes all with phenotype information and using GE and some other equipment, we’re making some combination findings with the genome that I think are gonna blow people away.

Amassing The Data: A Digital Challenge

[9:08] Craig: The digital world is a challenge and so in 1999 we had to build the third largest computer just for assembling the first genome. And that cost about 50 million dollars and was only one and a half teraflops.
[9:25] Sue: A teraflop is, 10 to the…
[9:28] Craig: It’s $100.00 today. [laughs] If that puts it in context.
[9:33] Sue: Alright, got it.
[9:34] Craig: So, there’s been a slight change. And with cloud computing, we can use distributed computing and not have to worry about building a machine to do this. Although when you do a brain MRI image it generates about three gigs of data and a group of scientists, computational scientists at UCSD led by Anders Dale, developed an algorithm that takes that three gigs of data and converts it into a single page, a table of volumes of different brain regions where they can detect the slightest changes and repeated MRIs over about three months that can detect if you’re developing dementia or not.
[10:15] Craig: So, that information coupled with the genome is very powerful, but it takes three gigs of data then converts it into a few KB of data that then is easy to take into a machine learning algorithm with lots of other cohorts of it.
[10:30] Craig: So, it’s not just the sheer volume of data. It’s the intelligent reduction of the complexity so it’s usable for comparing to everything else.

Face Recognition

[10:46] Craig: Franz Och was hired out of Google into HLI. I’m sure you know what he developed. He developed Google Translate. So, if you haven’t used that I’m definitely in the wrong room. And, he used a unique machine learning approach to convert languages into each others. I convinced him the human genome was the ultimate translation problem.
[11:58] Craig: Using machine learning is a way of doing what thousands or even tens of thousands of scientists themselves can never get to with looking at a small bit at a time. So the first challenge I gave to Franz and his group was to see if we could predict your photograph straight from your genome. Just A’s, C’s, G’s, and T’s. And we’ll be submitting a paper on this shortly.
[12:33] Craig: It predicts your age, just post puberty. You might wonder what your genome code is for, it’s for what you look like post puberty. And the algorithm does a little smoothing, so it makes you, it does make you look good.
[12:56] Craig: The algorithm also makes faces totally symmetrical right now which they’re not, but we’re very good at predicting your face. But if we were recording your voice just from these microphones we can accurately predict your sex, your age, and your height. So, there’s information contained in everything that’s human and it’s a matter of using new approaches to pull this out right from your genetic code.
[13:24] Craig: I was absolutely wrong. I said you would get your genome sequence once in life unless you had cancer. It turns out the exact age somebody is when they have a blood sample drawn is essential because we can now predict your age right from your genetic code. It changes throughout life. It becomes of measurement of aging. So using machine learning we compiled everything that was known about human traits and physical predictions and the machine learning out-predicted those by a substantial margin.
[14:02] Craig: So, just knowing the components wasn’t sufficient. The other thing machine learning does, is what your body does. It uses information across the genome. It doesn’t just use one gene and one snip and a gene like we’ve been measuring. It uses this information in an integrated fashion and that’s key to understanding different diseases, complex diseases, understanding aging, etcetera.

The Health Nucleus

[16:45] Craig: The Health Nucleus is our ultimate phenotyping center. So we have the latest GE 3T MRI machine and we have protocols that only we and GE research have. We have DEXA scanners, we’re adding CT and a second MRI. We have 4D echocardiograms; we have ways for measuring neurological functions and phenotyping.
[17:19] Craig: We measure the human genome, the microbiome. We bleed people and take 18 tubes of blood and measure literally thousands of chemicals.
[17:31] Craig: And, we integrate this information. So, just the imaging has been absolutely stunning in terms of the discoveries.
[17:45] Craig: The 4D echo, we’ve discovered two people with aortic aneurysms that thought they were totally healthy. And we integrate this data. And you find a physical trait, and we can go into the genome and we found a gene duplication event associated with aortic aneurysms. We can find different types of brain tumors, and find the genes associated with them.
[18:07] Craig: So, it’s not hypothetical anymore. We can start with the genome and go the other way. And we predict these people might have these diseases, because they have a higher risk. And we scored. They do or they don’t.
[19:47] Craig: And, we only have healthy people come in.
[19:56] Craig: But they don’t leave healthy… What if you’re between 50 and 75 and you’re a male. You have a 30% chance of dying in that time period. And a third of that is from cancer. And another third is from heart disease. People just don’t know they have heart disease or cancer until they get to the symptomatic stage.
[20:24] Craig: If you’re female, it goes down to a 20% chance of dying. But still a third is from cancer and a third is from heart disease. 1.3 million people get diagnosed with cancer each year. Those cancers just didn’t appear a few minutes before they were diagnosed. So, a third of the population’s walking around with cancer, with heart disease, with pre-dementia symptoms that they’re unaware of.
[20:50] Craig: The difference is when we find them early, they’re treatable. Or preventable or switched into a combination of the two. The problem we have with this comprehensive cancer work we’re doing, we get individuals when they’re discovered and diagnosed with cancer at stage four, where the odds of successful treatment are way down, vs what we have. So, I”ll talk tomorrow about one case. A tumor discovered under the breastbone that, you know, showed up with the MRI imaging was removed. It hadn’t penetrated the tissue and the guy went home two days later. Completely healthy, cancer free, and risk free.
[21:36] Craig: Had he not had this test, probably in a few years it would have penetrated the tissue and the survival time goes down to about a year.

The First Synthesized Genome and the Venter Institute

[25:05] Craig: So, what the Venter Institute, the not-for-profit institute and another for-profit company, the Synthetic Genomics that’s presenting at this conference as well, over the last five years… So, the first one we did was sort of basically copying most of a known species genome. And seeing if we could actually reproduce it from four bottles of chemicals. And we were able to, with some key alterations. Then we set out to design a new species for the first time. And we thought it would be a lot, pretty straightforward to do this on first principles. And first principles of biology don’t get you to a living cell. So the reason it’s taken us five years to do is there are a lot of iterations of things, testing them to see if they worked, and didn’t work, and sort of a story that goes along with this, when I was on a book tour of my book about synthetic life, “Life at the Speed of Light,” I was in Seattle and my late uncle was one of the lead designers for the Boeing 757. And, and I said, imagine building a new aircraft without knowing what all the parts do. And he shouted from the back of the room, “What makes you think we knew?” [laughs]
[26:20] Craig: So, designing life is kinda like building aircraft. If they fall out of the sky they find out what’s wrong and they fix it on the next generation. We kept testing until we found what was needed, and that paper hopefully will be out soon describing the first species designed through this combination of actual design in iterative testing to get something that would work.
[26:45] Sue: Wow. And when you say that would work. So it has functional life?
[26:50] Craig: Well, it lives and self replicates. It’s not extremely sophisticated but…
[27:14] Craig: It can make billions of copies of itself.
[27:18] Sue: And they’re all identical?
[27:32] Sue: And, how complex is this organism?
[27:35] Craig: It has a smallest genome of any self replicating organism ever known. We tried to get to what a minimal set would be. So, yet, its software is still immensely complex. It has all these regulatory functions. It has what people consider epigenetics. And the point is, all of that is coded for in the linear code of the DNA.
[8:05] Craig: And I think that’s a surprise to a lot of people. Because you can get a separate class on epigenetics, you think it’s a separate phenomenon. All that is coded for in that linear basic code that we all have as our software. So, it doesn’t matter where this minimal cell, or our human cells, that’s my understanding of the software is so critical.

Craig’s Advice to Entrepreneurs

[29:31] Craig: I think it’s essential to have a belief in what you’re doing. And it can’t be a delusionary belief.
[29:40] Sue: But people thought you were delusionary.
[29:47] Craig: And had we failed, that’s how it would have gone down in history. So, that’s how you build confidence in your own predictions, in your own intuition, by actually doing things. If you’re right…
[30:01] Craig: It’s like making movies. You make a blockbuster movie, somebody will give you money to make the next movie. After you’ve made 10 blockbuster movies you can have a few failures and somebody will still give you money to make another one. But if you never have a success you can’t really be a successful entrepreneur.
[30:26] Craig: You have to pick your battles. You have to really be passionate about it. You have to believe in what you’re trying to do. And you have to pick the right thing.

Resources, Websites and Tools Mentioned:

[0:43] StartUp Health
[1:35] The Institute for Genomic Research (TIGR)
[1:59] Celera Genomics
[1:59] Human Genome
[3:01] J. Craig Venter Institute (JCVI Institute)
[3:01] Synthetic Genomics, Inc.
[3:01] Human Longevity, Inc.
[3:01] “America’s Dumbest Criminals”
[4:51] “Jay Leno’s Garage”
[4:51] “Chasing Classic Cars”
[4:51] “American Pickers”
[6:00] GE
[10:46] Franz Och
[14:29] Der Spiegel
[25:05] “Life at the Speed of Light”

Related and Recommended StartUp Health NOW! Episodes:

Healthcare’s Next Frontier: The Power to Extend Life

Solving the riddle of the genetic code

Written by StartUp Health