Speaker 1: Pushkin. I'm Jacob Goldstein and this is What's Your Problem,
Speaker 1: the show where I talk to people who are trying
Speaker 1: to make technological progress. My guest today is Manola's. Kellis
Speaker 1: Manola's is a professor of computer science at MIT, and
Speaker 1: he works in computational biology. It's a field where researchers
Speaker 1: take giant data sets relating to things like genetics and
Speaker 1: health outcomes and try and understand basically what's going on,
Speaker 1: things like what are the cellular mechanisms of disease and
Speaker 1: how can we intervene to keep people healthy. In particular,
Speaker 1: Minola's research focuses on genomics and a related field called epigenomics.
Speaker 1: Here's how Manola's explains.
Speaker 2: What that means. What's extraordinary with genomics is that we
Speaker 2: can see beyond the limits of human imagination. We're talking
Speaker 2: about millions of cells across hundreds of people, across thousands
Speaker 2: of genes, and now we can now look at how
Speaker 2: the single genome manifests in every cell type of the
Speaker 2: human body in a slightly different way to create this
Speaker 2: extraordinary symphony that is the human life, that is human thought,
Speaker 2: that is human understanding, cognition, and every biological process that
Speaker 2: ability to now start understanding the building blocks of how
Speaker 2: this human genome manifests into all of these myriad of
Speaker 2: cell types and their interactions and their combinations and their
Speaker 2: coordination and their communication is what we can do for
Speaker 2: the first time. They're also giving us the entry points
Speaker 2: for understanding the basis of human variation, the basis of
Speaker 2: human disease, and the basis for reversing human disease.
Speaker 1: So that is the very big picture view of what
Speaker 1: Manola's does. In our conversation, we got into a lot
Speaker 1: more detail. For one thing, Manola's talked about his work
Speaker 1: on obesity, and that work is based on epigenomics, which
Speaker 1: is basically the way in which different genes are turned
Speaker 1: on and off, and this turns out to be a
Speaker 1: really big deal. Manola's and I also talked about his
Speaker 1: work on Alzheimer's disease. In that part of the conversation,
Speaker 1: he talked about how he and his colleagues are trying
Speaker 1: to find these key biological pathways that contribute to lots
Speaker 1: of different diseases, and how they're trying to come up
Speaker 1: with drugs to target those pathways. We started our conversation
Speaker 1: by talking about Manola's early work on the human genome,
Speaker 1: which led to the work he's doing now.
Speaker 2: So the human genome was mapped by K ninety nine
Speaker 2: or two thousand and three, depending on how you count.
Speaker 2: And then we had all of the nucleotides, all of
Speaker 2: the letters through into billion letters. Then the hard part begins,
Speaker 2: how do you make sense of that book? So that
Speaker 2: was the Book of Life. So we had all of
Speaker 2: the letters, how do you make sense of the book?
Speaker 2: My own PhD was developing evolutionary signatures for understanding systematically
Speaker 2: the human genome. So how do you recognize where are
Speaker 2: the protein coding parts? What are the parts that code
Speaker 2: for protein? We didn't even know.
Speaker 1: And just to be clear, sort of non intuitively, most
Speaker 1: of the human genome is not protein coding, right, Like
Speaker 1: there's this very basic idea that like, oh, sure the genome,
Speaker 1: that's what codes for proteins, but in fact, most of
Speaker 1: the genome is not doing that.
Speaker 2: Ninety eight percent of the human genome does not code
Speaker 2: for protein.
Speaker 1: It's wild. That is so nonintuitive, correct.
Speaker 2: So in that mysterious ninety eight percent of the genome
Speaker 2: lie control regents that are responsible for turning genes on
Speaker 2: and off. And that's where ninety three percent of the
Speaker 2: disease associated genetic variants are sitting.
Speaker 1: Huh, it's not the genes that actually code for proteins,
Speaker 1: it's the genes that control when are proteins made, when
Speaker 1: are they not made, how much are they made.
Speaker 2: That's exactly right.
Speaker 1: Okay, so I get that in a broad sense. That's
Speaker 1: sort of the state of affairs when you're coming into the.
Speaker 2: Field's exactly right. So I wrote a series of papers,
Speaker 2: both as a student and as a faculty member that
Speaker 2: sought to then uncover how to even parse the genome,
Speaker 2: how to even start understanding reading that book of life.
Speaker 2: So that's one part. The second part is where the
Speaker 2: regulatory motifs are. What are regulatory motifs. They are the
Speaker 2: short words of the language of DNA that are bound
Speaker 2: by regulators to turn genes on and off. So there's
Speaker 2: these regulatory regions, and within these regions lie these words
Speaker 2: which are the regulatory mode.
Speaker 1: And just to be clear, the regulatory motifs are part
Speaker 1: of what determine sort of when and how much different
Speaker 1: genes express different proteins.
Speaker 2: That's exactly right, that's exactly right. And that's where the
Speaker 2: human epigenome comes in. So what we needed to now
Speaker 2: understand is how that genome turns to life. So you
Speaker 2: can think of the epigenome as the living genome, as
Speaker 2: the genome. There's the genome itself is static. It's just
Speaker 2: the book the tablets, if you wish that Moses brought
Speaker 2: down from the mountain, and then the epigenome is the
Speaker 2: music that gets played from the orchestra. The epigenome tells
Speaker 2: you which parts are active in the brain and the liver,
Speaker 2: and the heart and the muscle and so and so forth.
Speaker 1: So your work on the epigenome is really interesting to me.
Speaker 1: And I know you've done some work on obesity, and
Speaker 1: the epigenome tell me about that.
Speaker 2: The strongest genetic association with obesity sits in one gene
Speaker 2: called FTO, and FTO was renamed fat and obesity associated
Speaker 2: after that discovery, and it remained mysterious for seven years.
Speaker 2: People had no idea how that gene works.
Speaker 1: You just saw correlate.
Speaker 2: There was a correlation.
Speaker 1: There was a correlation.
Speaker 2: Just the problem of genetics and the beauty of genetics.
Speaker 2: The beauty of genetics is that it tells you what
Speaker 2: region is responsible for disease. Regardless of how it functions.
Speaker 2: The downside is that it after he tells.
Speaker 1: You it's the same thing.
Speaker 2: After it tells you that he has a role, you
Speaker 2: have no idea how it functions. And what we showed
Speaker 2: in our work is that that region doesn't affect the
Speaker 2: FTO gene at all.
Speaker 1: So like in the middle of a gene, there is
Speaker 1: this whatever series of nucleotides, but those those nucleotides are
Speaker 1: just randomly in the middle of that gene and actually
Speaker 1: have nothing to do with that gene. I didn't even
Speaker 1: know you could do that.
Speaker 2: Fairly, you can't. So there are eighty nine differences, eighty
Speaker 2: nine common variants, common genetic variants that are all coinherited.
Speaker 2: If you get a here, you get all of the
Speaker 2: other you know, actage, you get that passage. If you
Speaker 2: get that package, it spans fifty thousand letters. But there
Speaker 2: are only eighty nine differences in these fifty thousand letters. Wow,
Speaker 2: and these will increase your body weight by one standard deviation,
Speaker 2: which is like how much it's like nine pounds, Like
Speaker 2: it's a lot, okay. So so basically what that does
Speaker 2: is that it functions somehow to increase your risk for
Speaker 2: a basits, it's like the strongest genetic association before. And
Speaker 2: what we reason is, how could it be acting. It
Speaker 2: could be acting in your brain to decide whether you
Speaker 2: like sweets or salting. It could be acting your muscle
Speaker 2: to make you more fit or less fit. It could
Speaker 2: be asking in your digestives. So we basically said, okay,
Speaker 2: well that's speculation. Let's look at the data. And we
Speaker 2: looked at the data and we found that there was
Speaker 2: this massive control region that was active in mesenchymal stem
Speaker 2: cells what are mesimo cells and sells. They are the
Speaker 2: progenitors of brown fat and white fat. Now, white fat
Speaker 2: is white because it's full of lipids. That's where all
Speaker 2: the calories are stored. Brown fat is brown because of
Speaker 2: all of the iron in the mitochondria. That's where the
Speaker 2: calories are burned. So it turns out that our fat
Speaker 2: cells make a developmental decision in their first three days
Speaker 2: of differentiation to go down the white path lineage or
Speaker 2: the brown path lineage. And what the white fat does
Speaker 2: is it stores energies and brown burns energies. So it
Speaker 2: turns out that I'm actually homozygous risk for the store
Speaker 2: calories position, which is the obesity risk.
Speaker 1: So you have the obesity.
Speaker 2: I have two copies of the obesity risk. My wife
Speaker 2: has zero. I can tell you, you know, we look
Speaker 2: very different. Fair So we basically realize that it sits
Speaker 2: in the progenitor cells of white and brown flat and
Speaker 2: then we could show that the true target was not
Speaker 2: the ftogene at all. It was instead two other genes
Speaker 2: that are sitting one point two million letters away from
Speaker 2: this region and six hundred thousand letters away, and those
Speaker 2: genes turned out to be master controllers of thermogenesis. They
Speaker 2: are basically switching your metabolic state. So my cells are
Speaker 2: stuck on the store position and my wife cells are
Speaker 2: stuck on the burn position.
Speaker 1: And so what is the relationship between the genes that
Speaker 1: are acting here and this this you know, package variant
Speaker 1: that is far away from them.
Speaker 2: It comes back to the epigena. So our DNA is
Speaker 2: stored inside a tiny little space. The way that gene
Speaker 2: regulation works is that you have these control regions that
Speaker 2: are scattered throughout the region of every gene that are
Speaker 2: linked together to that gene in three dimensions. So they
Speaker 2: do around and.
Speaker 1: So it's it's far away. If you think of it
Speaker 1: as a strand but in three dimensional space, right there,
Speaker 1: three dimension pats right, Ah, that's satisfying.
Speaker 2: And when we took these genes and we modulated them,
Speaker 2: we show that you can turn off one gene in mouse,
Speaker 2: in specifically the adipocytes of mouse with a dominant negative
Speaker 2: cus of fat cells with a dominant negative construct, and
Speaker 2: that turned the mouse fifty percent leaner. They eat the
Speaker 2: same amount, they exercise the same amount, but they burn
Speaker 2: calories when they're awake and they burn calories when they're sleeping.
Speaker 2: And what's really fascinated with that story is that the
Speaker 2: variant associated with obesity is at two percent frequency in Africa,
Speaker 2: but forty two percent frequency in Europe and forty four
Speaker 2: percent frequency in Southeast Asia. So it rose from two
Speaker 2: percent to forty four percent maybe because of positive selection.
Speaker 2: Maybe it was beneficial to be able to store every
Speaker 2: kind of.
Speaker 1: Places where food is, where you have food is scarce
Speaker 1: in moments of famine, exactly.
Speaker 2: In the out of Africa event, this may have been
Speaker 2: selected for. Or in the you know, ice ages, it
Speaker 2: may have been selected for. And it's only after World
Speaker 2: War two that this variant became associated with obesity.
Speaker 1: Because food became so abundant.
Speaker 2: And we stopped exercising as much. So it's fascinating to
Speaker 2: see how the environmental shift led to a new genetic
Speaker 2: association which is now plaguing our society, and of course
Speaker 2: the hope that by understanding the circuit systematically, we can
Speaker 2: now solve so many different circuits and ultimately so many
Speaker 2: different pathways and ultimately so many different disorders.
Speaker 1: In a minute, Manola's describes how he and his colleagues
Speaker 1: are trying to turn their genomic research into new medicines.
Speaker 1: That's the end of the ads.
Speaker 2: Now we're going back to the show.
Speaker 1: Another area where Manola's and his colleagues have done a
Speaker 1: lot of work is on Alzheimer's disease. They looked at
Speaker 1: a common genetic variant called apo E four. People with
Speaker 1: two copies of this variant have a much much higher
Speaker 1: risk of getting Alzheimer's, and Manola's and his colleagues were
Speaker 1: trying to figure out why. They found that having this
Speaker 1: Apoe four variant was linked to problems with moving cholesterol
Speaker 1: around in the brain, a process called cholesterol transport. Then
Speaker 1: they did experiments and mice that found that drugs that
Speaker 1: restore cholesterol transport actually restored cognition in the mice. Now
Speaker 1: that's in mice, and Alzheimer's is a notoriously difficult disease
Speaker 1: to treat in humans. So I asked Minolas what it
Speaker 1: will take to move his research from mice to humans,
Speaker 1: and his answer was really interesting. It pointed not only
Speaker 1: two ideas about treating Alzheimer's, but to bigger ideas about
Speaker 1: treating human disease more generally.
Speaker 2: The way that I'm thinking about this, the way that
Speaker 2: our team is thinking about these, is how do we
Speaker 2: enable personalized medicine and precision medicine. Namely, Alzheimer's is not
Speaker 2: going to be only about transport. It's going to be
Speaker 2: a combination. Every person has some combination of these regulations.
Speaker 2: A point four is the strongest genetic risk, but there
Speaker 2: are many others. And the question is how do we
Speaker 2: now systematically take a person with Alzheimer's, or take a
Speaker 2: family with risk, develop treatments that are either directly addressing
Speaker 2: the root causes rather than treating the symptoms, and are
Speaker 2: not only preventative but adapted to every family and every person.
Speaker 1: And just to be clear, like having you know, two
Speaker 1: copies of the APO four lil is neither necessary nor
Speaker 1: sufficient to get Alzheimer's. Right, that's exactly both of them
Speaker 1: and not get it. You can have neither of them
Speaker 1: and get it. So it's exactly so complicated hard.
Speaker 2: So, as with everything with human disease, genetics is not destiny.
Speaker 2: Genetics is a predisposition, and there are environmental factors. There
Speaker 2: are behavioral factors, there are nutritional exercise factors, there are
Speaker 2: socio economic factors. There's so many other factors that are
Speaker 2: affecting how your genetics will manifest ultimately into disease. But
Speaker 2: now the question is for every person, how do we
Speaker 2: create a drug? And it's not going to be feasible
Speaker 2: economically or in any other way to create one pill
Speaker 2: for each person. The way that we're going to enable
Speaker 2: personalized medicine is by understanding what are the hallmarks of disease,
Speaker 2: what are the hallmarks of Alzheimer's, the wholemarks of obesity,
Speaker 2: the whole moods of diabetes, the hallmarks of cardiac disorders,
Speaker 2: and develop therapeutics for every one of those hallmarks. So
Speaker 2: think of it as an arsenal of twelve or twenty
Speaker 2: different drugs for Alzheimer's that you're going to be taking
Speaker 2: a combination of it.
Speaker 1: Seems like oncology is already some way down that road, right,
Speaker 1: I mean, you know her two positive breast cancers have
Speaker 1: certain drugs that target them that sort of thing, right,
Speaker 1: is that the model?
Speaker 2: That's exactly the model. So the hallmarks of cancer have
Speaker 2: been the way of thinking about cancer for twenty plus
Speaker 2: years now. And the difference in cancer is the following.
Speaker 2: Cancer is subject to positive selection. What does that mean?
Speaker 2: That means that because it's a replicative disorder where the cell,
Speaker 2: the cancer cells make more of themselves. If a cell
Speaker 2: acquires a mutation that allows it to replicate faster, you
Speaker 2: will have more of that cell. So you are subject
Speaker 2: to positive selection where the bad mutations are increasing in
Speaker 2: frequency in every generation of the cancer. By contrast, most
Speaker 2: complex disorders are subject to purifying selection, where the mutations
Speaker 2: that are responsible for them are maintained at low frequency
Speaker 2: by evolution.
Speaker 1: Huh.
Speaker 2: So it's working at the opposite ends of the evolutionary spectrum.
Speaker 2: So cancer has a small number of genes that drive
Speaker 2: the disorder. Complex traits have thousands of genes that are
Speaker 2: maintained at low frequency or at weak effects.
Speaker 1: Except that sounds much harder. It's harder to figure out
Speaker 1: what's going on harder.
Speaker 2: But the saving grace is that even though you have
Speaker 2: extreme heterogeneity in the number of drivers, for every one
Speaker 2: of these disorders, they coalesce, they cluster, they converge in
Speaker 2: a small number of recurrent pathways, and these are the hallmarks.
Speaker 1: Huh.
Speaker 2: So you can find multiple genes associated with lipid transport,
Speaker 2: you can find multiple genes associated with new inflammation with
Speaker 2: DNA damage, so.
Speaker 1: You target the sort of pathways where they converge.
Speaker 2: That's exactly right. So we're not going to make a
Speaker 2: drug for Alzheimer's that we might make a drug for
Speaker 2: DNA damage, a drug for lipid metabolism, a drug for
Speaker 2: cholesterol transport, et cetera. And that's what we're working.
Speaker 1: That's satisfying. That's a satisfying explanation.
Speaker 2: It basically says that it is a limited number. There's
Speaker 2: a billion people in the planet. We're not going to
Speaker 2: have a billion drugs. What we're going to have it's
Speaker 2: a small number of drugs, one for each pathway, and
Speaker 2: these are sometimes going to be actually reused between different disorders.
Speaker 2: So we work on cardie disorders, we're finding the same
Speaker 2: genes underlying Alzheimer's, and specifically the lipid and cholesterol component
Speaker 2: are in fact reused in the heart disease. And again
Speaker 2: it's about lipids. It's about saturation of the fat stores
Speaker 2: of an individual and now the lipid escaping into the
Speaker 2: blacks into the bloodstream, forming these plaques that will then
Speaker 2: cause heart you know, failure and heart damage and so
Speaker 2: and so forth. So that's where we're at.
Speaker 1: So is there. I mean, the dream is that there
Speaker 1: is some dysfunction that is common to all these different
Speaker 1: diseases that you could target, right, Like, I mean, the
Speaker 1: naive dream is find the cure for everything, or not everything,
Speaker 1: but find the cure for a lot of things, or
Speaker 1: at least find a drug that will reduce risks of
Speaker 1: many different bad things, right, I mean, is that plausible
Speaker 1: or am I just naive in going there? From what
Speaker 1: you're saying.
Speaker 2: So you're right that some of the time these pathways
Speaker 2: that we're finding are going to be helping in multiple frauds,
Speaker 2: And then that's absolutely the dream. We should basically start
Speaker 2: not with what is the worst disease, but maybe what
Speaker 2: is the best pathway that if we fix that one,
Speaker 2: we're going to have an impact on most diseases.
Speaker 1: Right, like the highest return on investments for example.
Speaker 2: Like, Yeah, that's a great way to think about it.
Speaker 2: But the way that I would say is that for
Speaker 2: each person, this might be a different molecule.
Speaker 1: So now I'm not hopeful.
Speaker 2: But that with a small number of these molecules, say
Speaker 2: one hundred, one hundred and fifty two hundred molecules.
Speaker 1: When you say molecule, you mean drug.
Speaker 2: I mean trust, might I mean drust. Yeah, Basically that
Speaker 2: there's going to be a small number of pathways and
Speaker 2: a small number of these modulators, and that those are
Speaker 2: going to be mixed and matched in each person to
Speaker 2: then target a communatorially large number of people.
Speaker 1: Yeah, it just got hard. I know, I know biology
Speaker 1: is hard, but I got up to for a second.
Speaker 2: There's not going to be a single silver bullet for
Speaker 2: all of those. In fact, for any one of these diseases,
Speaker 2: there's no silver bullet. But the moment you build your
Speaker 2: panelbly of fifty silver bullets, then you're going to be
Speaker 2: hitting two hundred diseases. That's the beauty of it.
Speaker 1: Fifty bronze bo there's no silver bullet, but maybe.
Speaker 2: You can find it for hearts exactly right.
Speaker 1: We'll be back in a minute with the lightning round. Now,
Speaker 1: let's get back to the show. I read that you
Speaker 1: have been an author on more than two hundred and
Speaker 1: thirty papers, which is a lot. Which one was the
Speaker 1: most fun?
Speaker 2: Oh? You know what, don't I tell you about my
Speaker 2: very first one?
Speaker 1: Sure?
Speaker 2: And the very first paper was published in c graph
Speaker 2: and it now has like two thousand citations, And it
Speaker 2: was about how do we reconstruct the surface of an
Speaker 2: object from a cloud of points? So you can basically
Speaker 2: use laser scanning to sort of figure out points in
Speaker 2: three D and then the question is what is the
Speaker 2: surface that goes between them. I've always been fascinated with
Speaker 2: three D space, so it was very fun for me
Speaker 2: to just like you know, as a kid, basically as
Speaker 2: as a freshman at to work on such a project
Speaker 2: and then showing up at the conference. He was in Disneyland,
Speaker 2: so it was my first time in Disneyland as an
Speaker 2: author of a vapor.
Speaker 1: Sounds relevant for motion capture, not knowing anything about it.
Speaker 1: When I think of, like, you know, people, the way
Speaker 1: they make movies now exactly as they put a bunch
Speaker 1: of censors on people and they move around and then
Speaker 1: you can turn them into a dragon or whatever you want.
Speaker 2: Yeah, that's exactly right. So you know that paper has
Speaker 2: been quite influential and used for a lot of a
Speaker 2: lot of different things.
Speaker 1: What's the most overrated Greek island?
Speaker 2: Oh my god, I can tell you about the most
Speaker 2: underrated Santorini. Definitely not overrated tons of people, but worth
Speaker 2: every time. I can tell you about my first day
Speaker 2: in Santorini, which is I walked out on this balcony
Speaker 2: and I asked the owner of the restaurant if I
Speaker 2: can take a look at the view and I'm not
Speaker 2: order anything. He said, please be my guest, and I
Speaker 2: walked out, and ten minutes later, I'm like, I can't leave.
Speaker 2: I'm gonna have to order. He tells me, ten years ago,
Speaker 2: I came here to look at the view.
Speaker 1: I want you to throw a little bit of shade.
Speaker 1: I want you to get in a little bit of drug.
Speaker 2: Can't.
Speaker 1: What's one place in Greece I should not cannot.
Speaker 2: It's not possible. I mean, you know, if you keep insisting,
Speaker 2: I'll give you another twenty amazing places to visit.
Speaker 1: Well, that's fair, that's fair. I did what I could do.
Speaker 1: If everything goes well, what problem will you be trying
Speaker 1: to solve in five years?
Speaker 2: I think what I'm trying to solve now of actually
Speaker 2: creating these drugs in such a modular, AI driven, personalized,
Speaker 2: reusable way, centered on pathways. That's going to keep me
Speaker 2: busy for a long time. And I hope that in
Speaker 2: five years we have actually sold a big chunk of
Speaker 2: the platform and that we have a few drugs in
Speaker 2: clinical trials. So you know, my dream needs to take
Speaker 2: all of these circuits that we have uncovered and make
Speaker 2: a difference for humanity, make a difference for you know,
Speaker 2: my fellow beings. That's my big goal.
Speaker 1: Great, it's fun to talk to you.
Speaker 2: I learned a lot, such a pleasure, thank you, and
Speaker 2: I love that you're fearless. You're like, well, we're gonna
Speaker 2: jump into this new topic and find it all about it.
Speaker 1: Man nola's Kellis is a professor of computer science at MIT.
Speaker 1: Today's show was produced by Edith Russelo, edited by Karen Chakerji,
Speaker 1: and engineered by Sarah Bruguer. You can email us at
Speaker 1: problem at pushkin dot FM. I'm Jacob Goldstein. One last
Speaker 1: thing we are going to be taking a break for
Speaker 1: a couple of weeks, but we'll be back with new
Speaker 1: shows in early twenty twenty four. Thanks for listening, Happy
Speaker 1: New Year, that t