The Peter Attia Drive - #09 - David Sabatini, M.D., Ph.D.： rapamycin and the discovery of mTOR — the nexus of aging and longevity？

00:00:00.000 Hey everyone, welcome to the Peter Atiyah Drive. I'm your host, Peter Atiyah.

00:00:10.160 The Drive is a result of my hunger for optimizing performance, health, longevity, critical thinking,

00:00:15.600 along with a few other obsessions along the way. I've spent the last several years working with

00:00:19.840 some of the most successful, top-performing individuals in the world, and this podcast

00:00:23.620 is my attempt to synthesize what I've learned along the way to help you live a higher quality,

00:00:28.360 more fulfilling life. If you enjoy this podcast, you can find more information on today's episode

00:00:33.000 and other topics at peteratiyahmd.com.

00:00:41.340 In this podcast, I'll be speaking with my close friend and amazing scientist, David Sabatini.

00:00:46.800 David's a professor of biology and a member of the Whitehead Institute at MIT. He's also an

00:00:51.260 investigator at the Howard Hughes Medical Institute and a senior member of the Broad Institute,

00:00:55.020 along with a bunch of other accolades that would take too long to get into here.

00:00:59.460 This podcast was actually recorded initially as part of an interview series I was doing

00:01:03.520 for research around my book, and this was recorded in August of 2017. Maybe at some point, we'll even

00:01:10.100 just put the video up as this was actually done as a video interview with David, along with a number

00:01:14.740 of his amazing postdocs, and certainly some of those will probably make their way into the podcast as

00:01:19.100 well. Now, in this episode, we talk about his amazing journey in science and the work and stuff

00:01:25.060 that he's done around mTOR and rapamycin. And if you've been following the blog and or paying

00:01:30.680 attention to stuff that I'm interested in, you'll know that mTOR and rapamycin sit kind of at the

00:01:35.220 heart of it. Now, about four years ago, David and I were having lunch one day, and it was kind of

00:01:40.460 the first time that he ever really told me the full story of his work as a graduate student at

00:01:45.300 Hopkins, where he was part of the MD-PhD program. And I was just, you know, I remember sitting there

00:01:50.480 taking notes on a napkin and thinking, God, this is such an incredible story of science.

00:01:55.400 And I remember thinking, God, you know, one day we have to have this discussion again, but such that

00:02:00.440 most people can actually hear it besides just me. So part of what we discuss on this podcast is

00:02:05.220 actually that journey and how as a young PhD newbie grad student, David methodically went after a

00:02:14.840 problem that really wasn't even deemed particularly interesting at the time, which was to basically

00:02:19.580 figure out how this thing called rapamycin actually worked. And of course, through the process ended up

00:02:24.460 being the first person to identify this mechanistic target of rapamycin in mammalian cells. Now,

00:02:30.880 stuff that I found really interesting in this podcast is that David points out that he's from

00:02:35.160 an academic standpoint, kind of an unusual bird in that he's one of the few people who has carried his

00:02:40.660 work from graduate school into his career. And that's actually pretty unusual. He's incredibly

00:02:45.740 thoughtful. And some of you may have already heard a podcast that David, myself, and Nav Chandell,

00:02:51.100 another good friend who will also be on the podcast, recorded back with Tim Ferriss on Easter Island

00:02:55.700 back in the fall of 2016. We'll link to that here as well. And obviously the reason we went to Easter

00:03:02.160 Island was as sort of a pilgrimage based on the discovery of the bacteria that ultimately led to

00:03:08.620 rapamycin, a bacteria by the name of Streptomyces hydrocophagus. The interest I have in mTOR, of

00:03:14.420 course, has to do with its central role in nutrient sensing. And of course, it's, I believe, and many

00:03:21.440 believe its central role in longevity. So if you are interested in longevity, if you're interested in

00:03:27.240 fasting, if you're interested in rapamycin, you're really going to want to listen to this podcast because

00:03:31.760 David is effectively mTOR man. I don't think there really is a person on the planet, and I'm saying that

00:03:37.880 without trying to be hyperbolic, but I don't think there's anybody on the planet who knows more about

00:03:42.180 rapamycin and mTOR than David Sabatini. And if you like this podcast, please make sure to check out the

00:03:47.960 one that's going to be out soon with Matt Caberlin, which will take this discussion to another level as

00:03:54.380 well, looking at Matt's work in dogs. So without further delay, here's my discussion and conversation

00:03:59.980 with David Sabatini.

00:04:04.180 David, thank you so much for making time to sit down today and talk about what is potentially

00:04:08.800 mutually our favorite topic of discussion. Before we jump into it, though, maybe for people who don't

00:04:13.320 know you, can you tell us a little bit about how you got here and what it is you do specifically?

00:04:18.060 Sure, sure. So thank you, Peter, for coming and for visiting and both of you and for wanting to talk

00:04:22.560 to me. So I am a biologist. I'm a professor of biology at MIT, and I'm also a member of the

00:04:28.980 Whitehead Institute, which is where we are today. And I receive a lot of funding from the Howard Hughes

00:04:33.700 Medical Institute, which is a key charity that works with biomedical researchers. I have studied this

00:04:39.840 protein that I'm glad you like a lot. It's my favorite protein called the mTOR protein, which is

00:04:44.620 the protein through which this drug, rapamycin, which gets quite a bit of attention now, acts. And I

00:04:50.080 basically worked on that from the earliest point. We discovered that when I was a student. And so my

00:04:56.200 career is that as an MD-PhD, never really following the clinical track, though, and staying on the

00:05:01.340 research side and finishing that and actually coming to the Whitehead in a program that is quite

00:05:06.880 interesting and very unique at the time, which is that you could start your own lab after graduate

00:05:11.620 school. And so I did that, and I eventually joined the faculty here, and now I've moved up the academic

00:05:16.460 ranks. And to some extent, I'm a little bit strange from an academic point of view because I've

00:05:21.880 continued to work on, not exclusively, but to a large extent, what I started in my graduate school.

00:05:26.680 Most people, as you know, do something in graduate school, they do something in their postdoc, and

00:05:30.320 then they sort of morph along the way. I've kind of stuck with this mTOR protein, and in many ways,

00:05:35.760 I was very lucky because we were there at the beginning, and it turned out to be such an exciting

00:05:39.260 thing to work on.

00:05:40.120 So let's go back to the beginning a little bit. You were an MD-PhD student at Hopkins, and after a couple

00:05:47.520 of years of doing preclinical stuff, you pick a lab.

00:05:51.080 Exactly, right. So you do two years of medical school, and then you pick a lab. And I was very fortunate

00:05:55.900 to be taken by Solomon Snyder, who was at the time the head of neuroscience. He had a very big lab, lots of

00:06:02.300 MD-PhDs. A lot of MD-PhDs wanted to go to his lab, and I was lucky that he let me go.

00:06:07.520 So, and Saul was a, is a really interesting man. He still has actually a really prominent lab at

00:06:13.120 Hopkins. In fact, the department is now named after him. And he was that person who had a lot

00:06:17.740 of varied interests. So he was a neuroscientist, but he was also a psychiatrist, and he was also

00:06:21.380 a pharmacologist. So he, he really loved small molecules, and he loved particularly potent small

00:06:29.820 molecules. That is, small molecules that act at low doses.

00:06:32.540 How do we define in pharmacology a small molecule? What's the cutoff point?

00:06:35.860 You know, some people say a thousand Daltons, which rapamycin is about that. To a larger

00:06:41.560 extent, it's sort of a non-peptide also. So it's not a piece of a protein. In many cases,

00:06:46.160 it's not a natural molecule. Although in our case, it's made by microorganisms. It's not natural

00:06:50.420 to our body. So probably a thousand Daltons. And so he had these set of interests. And when

00:06:56.840 I went to his lab, I was actually really interested in neuroscience. So I'd had some classes in which

00:07:00.820 I was sort of fascinated by some neuro questions. But when I got to his lab, I actually never did

00:07:06.720 anything on neuroscience. And I often told the story that the most influential scientific

00:07:11.120 discussion I probably ever had is when I went to talk to Saul. And basically, as a student,

00:07:17.520 you need to pick a project. This is something that is quite challenging. And I see with my own

00:07:22.760 students, they really get quite apprehensive of what their project is. So I went to talk to Saul,

00:07:26.140 and I went to his office. And I only met with Saul, like, I don't know, maybe five or six times

00:07:31.080 during my PhD. So this was like a big deal. And so I went to talk to him, and he basically said,

00:07:36.480 David, we work on the brain. And I thought that was great because I wanted to do neuroscience.

00:07:40.300 But then he didn't say anything else. So that was it. And so I remember leaving his office really

00:07:44.400 anxious because basically, like, I didn't have a project. But I realize now in retrospect what he did

00:07:50.500 is he actually was giving me complete freedom to do what I wanted to do. And that was,

00:07:54.680 as I said, probably the most important thing anyone's ever done for me. Because it really

00:07:57.800 forced me to come up with my own project, and I think was a key sort of foundation in becoming

00:08:02.800 the scientist I have. And it's something that I try to foster amongst my own people.

00:08:07.420 So anyways, I was in his lab, and I didn't have a project. And at the time, they were actually

00:08:11.880 working with this other drug called FK506, which is a well-

00:08:15.800 It's an immunosuppressant.

00:08:16.640 It's immunosuppressant, used clinically still. Mechanism of action, although structurally,

00:08:21.300 it's very different than cyclosporine. It actually mechanistically works on the same

00:08:24.820 target, which is calcineurin. And at the time, this is before we had a lot of the tools we have

00:08:29.940 now like RNAi or CRISPR. And so you needed controls. So if you had a drug, what you tended to use was

00:08:35.620 another drug that kind of looked like it, but didn't do the same thing. And so their control was rapamycin.

00:08:41.540 And when I started reading about rapamycin-

00:08:44.180 And this is what year?

00:08:45.060 This would have been in probably late 91 to 92. And it was clear to me that this molecule in many

00:08:55.460 ways was much more interesting than FK506. And as you very well know, this had come from

00:09:00.420 Wyeth Ayers, the pharmaceutical company by Soren Seagal, who championed it. And there was a number of

00:09:06.900 papers, which at the time were actually, a few papers were largely abstracts from meetings that

00:09:11.940 showed that it had antifungal effects, immunosuppressive effects, anti-cancer effects.

00:09:16.740 So it seemed like an interesting molecule. You know, I had just come from medical school. We'd

00:09:20.500 learned about immunosuppressants like cyclosporine, which at the time, you know,

00:09:23.860 were really just coming on and were really seen as miracle drugs.

00:09:26.740 But your lab's interest in FK506 was not its immunosuppressive properties,

00:09:31.060 but its calcineurin inhibition.

00:09:32.260 Exactly. Because calcineurin, as the name implies, there's a lot of them in the brain.

00:09:36.020 And so in Saul's lab, they're basically studying the modulation of calcineurin in the brain

00:09:40.980 using FK506 as a tool. And they were looking actually at cytotoxicity in the brain. Things

00:09:46.340 that at the end didn't lead, I think, in the directions they wanted to. But they were using

00:09:50.100 it as a pharmacological, as a probe, basically. And so I basically decided, why not try to work on

00:09:56.500 rapamycin? And so that's what I did. And so we-

00:09:59.300 Which was just a control that nobody particularly cared for.

00:10:02.100 Yeah. I mean, there were people in the world that were interested in that.

00:10:03.780 But in your lab.

00:10:04.420 In our lab, yeah.

00:10:05.140 In people in the lab, no one was studying rapamycin.

00:10:07.140 We had this great advantage, though, is that you couldn't buy rapamycin at the time.

00:10:10.660 So rapamycin was a compound that Wyeth was developing clinically. It wasn't clinically

00:10:14.980 available. You couldn't buy it. But Saul, being a very prominent scientist and having this

00:10:20.260 interest in pharmacology, had actually written Seren Segal. And actually, he had sent us,

00:10:25.940 probably, without any of the legalese that happens now. Now, if you try to get a molecule

00:10:30.340 out of a pharmaceutical company, the amount of paperwork and red tape is huge. Basically,

00:10:34.820 it sent us a very significant amount of rapamycin, which I remember when it did start to be sold,

00:10:40.180 which was incredibly expensive, I kind of back-calculated.

00:10:42.900 The street value.

00:10:43.860 Yeah. It was like millions of dollars.

00:10:45.220 Wow.

00:10:45.620 Now, of course, it didn't really have that value. But theoretically, it was sort of millions

00:10:49.300 of dollars. Which, incidentally, that tube followed me all the way here.

00:10:52.420 And then-

00:10:52.980 Do you still have the original tube?

00:10:53.940 No. At some point, it was lost. It disappeared at some point.

00:10:56.900 But anyway, so we actually had it, which was cool. So we could actually do experiments with it.

00:11:02.900 And so I went on to try to understand how it worked. And eventually, we purified this protein

00:11:07.460 that, at the time, we actually called RAFT1, was the original name we gave it. And eventually,

00:11:11.220 it was called-

00:11:11.860 And RAFT1 stood for-

00:11:13.220 It was rapamycin and FKBTP target one. And the reason that we, and also Stuart Shriver,

00:11:20.020 who was at Harvard, and when he was working on this, he was also at Harvard,

00:11:22.820 also identified mTOR basically at the same time. He called it FRAP, which was FKBP,

00:11:28.100 rapamycin-associated protein. And both of us were trying to accentuate the point that rapamycin

00:11:34.020 acts with a co-receptor, this protein FKBP. From that point of view, it's a very unique kind of

00:11:39.060 drug where it doesn't directly bind to a protein target, but rather it first binds to one target.

00:11:44.740 And now that drug receptor complex has a new surface on it, which now, in this case, interacts

00:11:50.420 with mTOR. And we were really trying to get that point across.

00:11:54.340 But independently, you didn't realize you were both working on this.

00:11:58.020 Yeah, I had no idea that. In fact, the only point where I found out they were working on it was once

00:12:02.180 our paper had been accepted, I got, and this was pre lots of email, internet, we got a fax

00:12:08.020 from a journalist saying he was writing an article on our paper and another paper from

00:12:13.460 Stuart Shriver and actually sent us Stuart's paper, which we thought was really unethical at the time.

00:12:18.980 And so we actually at that point contacted Stuart and said, hey, we got your paper. You should know

00:12:23.540 we're working on this too. And here's our paper. So it was, yeah, I didn't know at all. And in many

00:12:28.900 ways, I was very naive, right? I was in this lab. Saul basically let us do whatever we wanted to. We

00:12:34.500 had this drug. Unbeknownst to Saul, I started working on this thing, right? And Stuart had had a history

00:12:41.220 of FK506 mechanism action. So it was a logical progression to what he was doing. Saul was not, it was

00:12:47.460 funny. He came from a world where people looked for the receptors for drugs. So if you look at his

00:12:51.620 history, he'd really looked for receptors for drugs, for small molecules, including the endorphins,

00:12:55.940 for example, that he worked with. But he wasn't big on cloning, what we call cloning a gene, which

00:13:01.380 is where you have that, get the DNA sequence. He almost thought you didn't need to do that. Once

00:13:04.580 you purified it, you could study the protein. So I was one of the first people there to actually clone

00:13:09.540 a cDNA, as we call it, in his lab. So it was a fun time because it was clear that we'd gotten this

00:13:18.020 protein. But you did this in a very short period of time because your paper, which was in Cell,

00:13:22.740 was 1994.

00:13:23.700 1994, yeah. I worked like crazy, really like crazy. And that lab, in general, worked like crazy.

00:13:31.780 It was very common to be there until 1 in the morning, and then I would usually show up at

00:13:36.260 7, 8 in the morning. You know, we would sleep in the lab a lot. And once, you know,

00:13:40.100 once things started to go, so we were purifying, I purified out of the rat, out of rat brains.

00:13:45.220 And so we killed hundreds of rats to do this. And my friends would help me kill them, take the brains 0.83

00:13:49.300 out. There's a method in biology to visualize proteins called a silver stain, which is a very 1.00

00:13:55.860 sensitive way of seeing a protein. And the first silver stain I did, where I actually saw sort of a

00:14:01.940 glimpse of mTOR on this method. I remember that really clearly, because at that point,

00:14:06.820 I knew I could do it.

00:14:08.020 Dr. Justin Marchegiani How did you know it was mTOR that you

00:14:10.020 were looking at?

00:14:10.580 Dr. Justin Marchegiani Well, I mean, I had all the controls,

00:14:12.420 and there was this band on what we call a gel that showed up just in the right place.

00:14:17.860 Dr. Justin Marchegiani I see.

00:14:18.340 Dr. Justin Marchegiani And so I was like, okay,

00:14:19.220 there is a protein here that has all the properties that I want.

00:14:22.180 Dr. Justin Marchegiani And at that time, what properties did you know?

00:14:24.660 You didn't know its size, did you?

00:14:26.180 Dr. Justin Marchegiani Didn't know its size. Well, we know it bound to FKBP rapamycin.

00:14:29.940 Dr. Justin Marchegiani But you didn't know that that

00:14:32.660 exclusively bound to it, did you?

00:14:34.260 Dr. Justin Marchegiani We didn't. But we knew that it could be competed by FK506

00:14:37.540 based on some competition type experiments. And so we had done that. So there was these features.

00:14:42.340 It was mostly the specificity that it required rapamycin to bind to FKBP. And that was crystal

00:14:47.380 clear in the early experiments. When we had FKBP by itself, there was no band on the gel. And when

00:14:53.620 we added rapamycin, there clearly was. And when we added FK506, it clearly went away.

00:14:57.700 Dr. Justin Marchegiani And so we knew that that thing had

00:15:00.420 all the right properties. But I remember very strongly feeling, okay. And at the time,

00:15:05.300 now we have very, very sensitive methods to sequence proteins, larger than mass spectrometry.

00:15:10.260 There, we didn't. And so from what I saw in that gel, to actually figuring out what its sequined

00:15:15.460 wants, I knew it was hard. But I knew it could happen. That was like a very powerful feeling.

00:15:19.460 Dr. Justin Marchegiani It was the existence principle.

00:15:20.580 Dr. Justin Marchegiani Exactly. So I knew the thing,

00:15:21.940 like kind of the enemy existed, and I could get it. But then going from that initial glimpse on

00:15:27.540 a gel to then having enough to actually sequence it, that's what took hundreds of rats to actually

00:15:32.900 get to enough that I could purify it. And eventually, we collaborated with this guy called Paul Temps

00:15:38.980 at a Memorial Sloan Kettering in New York. And he was able to sequence enough of the protein

00:15:44.260 that then through a whole variety of molecular biology tricks, we were able to clone it. And it was

00:15:48.660 a really huge cDNA, which basically is the length of the DNA sequence that encodes it.

00:15:54.340 It was very big, sort of in the eight, nine kilobases, which is very hard to work with,

00:16:00.500 particularly at the time. And I got very, very lucky in the sense that I did a bunch of tricks,

00:16:05.220 and I got the whole thing at once, which is also was kind of unheard of at the time.

00:16:08.340 Dr. Justin Marchegiani How did you do that?

00:16:09.060 Dr. Justin Marchegiani Yeah. So back in the time,

00:16:10.980 what people would do is they would get pieces, and then they would sequence them, and they would

00:16:14.660 Dr. Justin Marchegiani With overlapping.

00:16:15.620 Dr. Justin Marchegiani They would overlap and stitch them together.

00:16:17.540 Dr. Justin Marchegiani But what I did when I screened what we

00:16:21.300 called libraries at the time for these pieces, I would get some pretty big pieces, but I knew,

00:16:27.060 when I would sequence it, I knew that the front of the protein was missing, like I was missing,

00:16:32.100 and I couldn't get it. It would never, I could never get beyond a certain point of the protein.

00:16:37.700 And so then what I did, which, you know, really turned out to be like incredibly lucky. So what

00:16:44.340 we would do is we would screen libraries of phages. And so this was basically, people would take

00:16:50.500 cDNA, complementary DNA from rat brain, and they would clone it into these bacterial virus

00:16:56.980 called phages. And so now every little cDNA was in a virus, and you'd have hundreds of millions of

00:17:03.380 this library. And you would plate it out on these plates, and the phage would make little plaques.

00:17:08.820 And then you would screen those plaques. And so you'd have, you know, dozens of these plates,

00:17:12.500 each with thousands and thousands of these little dots on them. And so what I decided to do is that

00:17:18.580 I would screen this library with a piece that I knew was as far towards one end and as far toward

00:17:25.220 the other end. And so I screened it with both, and I looked for plaques that hybridized both.

00:17:31.860 And in fact, when I first did it, I got nothing. It was really disappointing. I got plaques that had one

00:17:37.380 piece, and I had plaques that had another piece, but I didn't have any plaques that had the same.

00:17:40.500 And then what I realized, and this was really key, I realized that the so-called full-length cDNA

00:17:47.460 was so big that it was going to make the phage replicate slowly. Because basically,

00:17:52.420 their genome was so much bigger now that to replicate, it would take longer.

00:17:57.380 And on what order of times?

00:17:58.580 It was probably two to three times more that it would take.

00:18:01.060 I got it. So you could have been missing it.

00:18:02.580 I could have been missing it because the plaque that would have this would be incredibly small.

00:18:06.740 And so what I did is I went back and redid it. And now I let the plaques grow longer.

00:18:12.100 And I re-screened it. And in fact, I got one plaque. It's a tiny, tiny little plaque that hybridized with

00:18:17.940 both probes. And when I looked at what was in there, it turned out to be the complete full-length

00:18:24.100 cDNA, which was amazing because it was unheard of that these libraries would give you something like

00:18:29.060 9,000 base pairs. But it was. When I sequenced it, it was literally the intact thing from one end to the

00:18:34.100 other. So I got very lucky because that would have been pretty hard to assemble at the time.

00:18:39.460 So you knew at the time, had Michael Hall's work in yeast been published yet?

00:18:44.180 It had been published sometime during this period of time.

00:18:47.700 But you didn't know anything. You didn't know even what the yeast form of this.

00:18:51.700 No, no. When we started, we weren't doing it. And in fact, when we first started getting sequenced,

00:18:54.740 there was no sequence out there. And the yeast protein, really only the kinase domain is concerned.

00:19:00.020 And so most of the peptide sequences that we had that Paul Temps had sequenced for us,

00:19:06.020 we didn't know at all where they were. And so these are kind of fun things that used to happen

00:19:10.580 in the past. You used to collaborate with a person who did protein sequencing, and they would give you

00:19:15.140 back a series of peptide sequences. But you didn't know what order they went in. So let's say he gave

00:19:20.580 you back, I think Paul gave me, Paul was amazing because he would give you, let's say, 15 peptide

00:19:25.380 sequences. He'd say, look, your protein, these 15 peptides exist in your protein.

00:19:31.220 And then-

00:19:31.620 With or without overlap in those peptides?

00:19:33.620 No overlap. These are short.

00:19:35.700 Mathematically, it's impossible to, by chance, figure out, like, you need more clues to figure

00:19:41.220 out the order because it's combinatorially impossible.

00:19:43.300 Yeah, you have no idea what the order is. And so what you end up doing is kind of a cool thing.

00:19:47.060 And Paul was really cool because he would actually, in the sequence of the peptides,

00:19:50.500 he also had uncertainty. Sometimes he'd say, this amino acid-

00:19:53.620 Plus or minus.

00:19:54.340 Could be this or it could be that. And he would tell you what he thought it was. And it turned

00:19:57.380 out he was so good that when I actually figured out the sequence, every one that he said it could

00:20:01.620 be this or that, he was right, his prediction. But so what you would do is you'd have these peptide

00:20:06.580 sequences. And what you could do now is design, we know, obviously, the code, the amino acid code.

00:20:14.100 So we can predict what the DNA sequence would encode. But as you know, the DNA sequence is degenerate,

00:20:19.460 right? So one peptide sequence can be encoded at the DNA level.

00:20:23.060 You don't know what the extrons and introns look like.

00:20:24.660 You don't know anything, right?

00:20:25.780 But each peptide could be encoded potentially by thousands of oligonucleotides.

00:20:30.420 And you don't know the order of the peptides. What you would do is you would make a degenerate

00:20:34.820 pool of oligonucleotides that had thousands of different ones. And you'd make them in both

00:20:40.500 orientations. And now you'd do PCR between them in all combinations. And you would find which

00:20:47.140 ones worked. And that would define the order of the peptides.

00:20:51.300 And this is before you had real-time PCR.

00:20:53.300 Yeah, real-time PCR are usually used for quantitation. But we had PCR. And so we would

00:20:56.820 take these oligolibraries and we'd mix and match them, all combinations in all orientations.

00:21:02.180 And if you got a band, it means that you got, you know, that you could basically figure out.

00:21:06.100 And then you could take those fragments and go and screen the libraries. And so it's funny,

00:21:09.300 because now, you know, with my students, when we discover a new protein,

00:21:12.660 all you do is you look up in the database, because we have a whole genome sequence.

00:21:16.180 I always tell my students that my paper, which was the discovery of mTOR, which at the time,

00:21:20.660 to be fair, we did not realize how important mTOR would be. My paper basically is like figure 1AB

00:21:28.740 of their papers. Because my whole paper is about discovering the protein, sequencing it,

00:21:33.540 all this kind of stuff.

00:21:34.340 Was that paper effectively your PhD?

00:21:35.940 That was my PhD.

00:21:36.980 So you went back to finish a couple years of med school, obviously decided,

00:21:41.940 not going to do a residency, I'm going to become a full-time scientist.

00:21:47.060 And then you basically have been at MIT since, or affiliated with MIT since.

00:21:51.140 Saul's lab was big, and I was very independent. So the people said,

00:21:53.380 why don't you do one of these fellows positions where you can start your own lab? And at the time,

00:21:57.140 there was only three. There was the Whitehead one, there was one at Carnegie Institute, 0.91

00:22:01.300 which is in Baltimore, and there was one at Cold Spring Harbor in New York. And I applied to all of

00:22:05.380 them. And I got accepted pretty quickly, although after I graduated to Cold Spring Harbor and to

00:22:12.820 Carnegie. But I didn't hear anything from the Whitehead, like nothing. And only like basically 0.67

00:22:18.020 once I graduated, and I was kind of unemployed at that point. I was technically a postdoc in

00:22:22.660 Saul's lab. But I hadn't taken like the boards, which Hopkins, you know, but Hopkins didn't make

00:22:27.140 you take the boards, the medical boards to graduate, which was a nice thing. My mother was

00:22:31.060 like, you're going to starve, you don't have a job, you can't do residency now because you didn't apply,

00:22:34.900 you didn't take the boards. And then I got a call from Whitehead actually inviting me to interview,

00:22:40.340 and I did. And then it took, again, a lot of time to like hear back. And I remember they called me

00:22:46.660 and said, look, we're going to offer you a position, but you need to understand you will never ever stay

00:22:51.060 here as a faculty member, ever. I was like, okay. I realized I was applying for this Whitehead fellow 0.99

00:22:56.500 position, not a faculty position. But then eventually I came and eventually I did stay. And when I look

00:23:02.020 actually of history, it's, they do keep, you know, about a third of the people who come through,

00:23:06.500 but they give you this sort of speech that you will never ever stay.

00:23:09.300 Set the expectation.

00:23:10.740 Yeah. And incidentally, many of the people named David have stayed. So it's actually a good thing

00:23:14.500 to be named David. Actually, our current director was a Whitehead fellow. His name is David. One of 0.80

00:23:18.180 the other faculty members, his name is David. So I didn't know at the time, but now I realized that

00:23:22.420 David was a, was a big advantage.

00:23:24.740 So how has your work evolved? I mean, you came here in the late nineties, right?

00:23:28.180 In the late nineties, exactly.

00:23:29.220 Rapamycin would go on to be approved by the FDA in 1999 as a frontline treatment as part of the

00:23:36.100 double or triple cocktail for patients.

00:23:38.420 As rap immune, right?

00:23:39.620 Right. As rap immune, along with often prednisone, cyclosporine or MMF.

00:23:44.980 So now you're here. And I mean, we're going to get into much more detail, but effectively,

00:23:50.740 you've never looked back. You've never really left this space.

00:23:53.300 I got here and I was incredibly naive. I realized at this point how

00:23:57.940 I thought, you know, I knew a lot. I thought I knew how to run a lab. I had been very independent

00:24:02.500 on my own. That doesn't mean that I was sort of independent from like running a lab. You know,

00:24:06.180 behind the scenes in Saul's lab, I was, it was the entire finances. I had written grants and things,

00:24:10.340 entire finances, organization, but there was a lot. Like I could be independent, me, but then a lab is a

00:24:16.100 different thing. And so that was a hard transition to run, even though it was a small lab, to run a lab.

00:24:20.660 And it was clear that at that time, I felt that this field had kind of plateaued. There had been

00:24:27.300 the discovery of mTOR, but we weren't getting very far. People were using rapamycin to look at

00:24:34.020 lots of different things. And mTOR, by implication through rapamycin, was being connected to lots of

00:24:38.980 different things. But one of the things that was obvious to me, and I think to others as well,

00:24:43.540 was that mTOR had to act with partner proteins. And so we set about trying to identify what we now know

00:24:50.500 are these mTOR containing complexes, mTOR 1 and mTOR 2, mTOR complex 1 and 2. But that was,

00:24:56.820 it was really hard. We failed for years. It was, again, one of, this field has had a series of just

00:25:02.500 like little things that until you figure them out, you make no progress. And so we would purify mTOR,

00:25:08.740 and we'd look for other proteins. We would continue to work with Paul Thompson. We just wouldn't find

00:25:11.940 anything.

00:25:12.260 To be clear, you knew that you had discovered the gene for TOR.

00:25:17.220 Right.

00:25:17.540 You suspected that this thing exists in different complexes.

00:25:21.300 And I already knew that there was other proteins. Because when I was doing the mTOR,

00:25:25.220 original purification, the way that I was following mTOR was with a kind of a funky

00:25:31.220 cross-linking assay, where I was cross-linking a radioactive FKBP to the putative target. And there

00:25:37.620 was always two bands on the gels. There was the protein mTOR, which I eventually purified. But

00:25:42.420 there was a smaller one, which I could never get. Either because it was just low abundance,

00:25:46.580 I couldn't detect, I don't know what. But that little protein, which at the time I called RAF2,

00:25:52.820 basically remained unidentified. So I knew that there was something.

00:25:56.100 So basically, the first version of mTOR complex 1 was TOR, and the version of mTOR complex 2 was RAF2.

00:26:02.020 No, no, no. That protein, actually, now that we found it, turns out to be in both complexes.

00:26:05.940 Oh, I see. But what I knew was that there was an associated protein with mTOR. I knew from,

00:26:10.900 I didn't know what its identity was. But it was very clear on all my experiments that there was

00:26:15.140 a small, mTOR is very big, it's around 300 kilolons, which is a big protein. This was a

00:26:19.860 little protein, it was around 30. So it was about 10 times smaller. So from a technical point of view,

00:26:25.460 it's about 10 times harder to get, because there's about 10 times less peptides in that protein.

00:26:30.660 So I failed to get it. So when I got here to the Whitehead, I knew there was another protein to find.

00:26:35.620 And we kept trying to go after this protein. And others, we knew it had to work. You know,

00:26:40.660 it's a really big protein. Big proteins work with friends. And it turned out, this is again,

00:26:46.340 these little things. It turned out that the detergent, so when you work with mammalian cells,

00:26:50.900 you have to lyse them. You have to break open their membranes. You typically use a detergent.

00:26:55.060 Turns out the detergent that we were using, which is by far the most common detergent that every lab

00:26:59.460 in the world use, breaks apart these complexes.

00:27:01.860 Just by bad luck.

00:27:02.740 Just bad luck. And I had a postdoc, his name was Dostarbasov, who figured this out. And he

00:27:09.220 found this other detergent called CHAPS that kept them together. When you think back your career,

00:27:14.580 and you're like, well, what are like these key inflection points? His discovery of that detergent

00:27:18.180 was key. Because once we did that, we purified all the interacting proteins. And that eventually led

00:27:24.580 to mTORC1 and mTORC2. It eventually led to all the proteins associated with those. Basically,

00:27:29.540 that was the key to all the biochemistry. There was like several years of nothing.

00:27:34.980 And he found that. And then everything has sort of, from that point on, we've sort of marched along

00:27:40.660 in figuring out all the components of this pathway. We still don't know why things are sensitive to

00:27:45.940 triton. We don't know why they're incentive to this other detergent. But it's the kind of happenstance

00:27:50.340 of science that, I guess, makes it interesting. So when, roughly by year, where are we when we

00:27:56.660 have a, we meaning the world as a result of your discoveries in the lab, where are we when we sort

00:28:02.260 of know that now we have mTORC1 around Raptor, mTORC2 around Richter? This is?

00:28:08.660 It's around 2002, right? So when we're doing that around 2001, published around 2002. It's in that range.

00:28:14.820 It's in the early 2000s. Although, as I said, we knew there was complexes even back in 94.

00:28:20.500 And at this point in time, your thought was these two complexes control

00:28:27.860 what or sense what or are important for what? Right. So it was very clear early on that mTORC1

00:28:34.980 was doing most of the things that we had connected before to mTOR. So, you know, we'd had rapamycin.

00:28:40.100 And so rapamycin, in a way, had allowed us to know a lot about mTORC1, we now realize,

00:28:46.980 than otherwise we would have known. Because we didn't have really genetics. We didn't have

00:28:50.500 easy ways of modulating mTOR, but we had rapamycin. And so there was a body of knowledge acquired by

00:28:56.260 many different investigators about what was so-called downstream of mTOR. What did mTOR do? We had some

00:29:01.620 ideas. It was a growth regulator, it regulated translation, it regulated autophagy, right? It

00:29:06.500 regulates many, many metabolic pathways, it regulates cell size. We knew that largely through

00:29:12.980 the use of rapamycin. And so now when we discovered mTORC1, which, you know, the first part we discovered

00:29:18.820 was this protein called Raptor, we now could go and say, well, does Raptor matter for all those things?

00:29:24.420 And it turned out it did. So it was very clear that mTORC1 must be doing the things that we ascribe

00:29:29.300 to rapamycin. mTORC2, therefore, remained very mysterious for a long period of time,

00:29:34.820 because it wasn't doing those other things. And only later did we find that it was actually

00:29:40.020 part of the PI3 kinase pathway in a regular AKT. And that clarified lots of things. And in many ways,

00:29:47.460 mTORC2, you could actually even say, and we've written papers arguing this, that it's almost like

00:29:52.580 upstream of mTORC1, because the PI3 kinase pathway is one of the inputs into mTORC1. In many ways,

00:29:59.060 mTORC2 is less important than mTORC1. I mean, you can modulate it more and still survive more.

00:30:05.460 So we've really focused largely on mTORC1. And when I first got here, you sort of asked me,

00:30:10.980 okay, well, what did you end up doing, right? And I was pretty worked up when I got here,

00:30:15.460 and I had to realize I was sort of running a lab and unclear exactly what I was going to do.

00:30:20.020 And I ended up working on mTORC1, or mTOR, I should say, largely because I didn't know anything.

00:30:26.020 So I basically had to work on something. And I remember some people here

00:30:30.340 were pretty critical of me working on rapamycin. They were like,

00:30:33.540 why are you working on that silly molecule? Okay, now you have the target. And the truth was, 0.94

00:30:38.100 that's what I knew how to do.

00:30:39.140 Even at the time, you didn't appreciate what you do now, which is that effectively,

00:30:44.820 mTORC1 sits at the center of the universe for at least some of the things that we care a lot about,

00:30:52.180 including potentially longevity.

00:30:53.940 We did not.

00:30:54.980 When did that become clear to you?

00:30:56.740 That became clear. You know, we tried, when we started to understand the connection to nutrients,

00:31:02.260 and the fact that caloric restriction had been connected to longevity, we started thinking,

00:31:06.900 okay, we actually tried doing experiments on worms at the time with rapamycin. It turns out

00:31:10.500 rapamycin doesn't get into worms. But there was really some, it was an important paper in worms,

00:31:16.180 where there was a mutant in the C. elegans version of mTORC1 that had longevity effects.

00:31:21.780 I would say that was sort of the key paper. And this is unrelated to DAF2?

00:31:26.260 Unrelated to DAF2. Although, interestingly, in the screens that gave the DAF mutants,

00:31:31.540 one of the DAF mutants, in retrospect, one of the ones actually had never been identified what

00:31:35.460 the gene was. It was simply a mutant that had a mutation. It turns out to be a raptor.

00:31:40.420 I think it's DAF15. I don't quite remember.

00:31:42.660 Okay, not 16, I'm sure.

00:31:44.980 I don't remember. We could look it up. We'll look that up, yeah.

00:31:47.060 But so it was interesting. There was all these DAF mutants that had these interesting

00:31:51.060 phenotypes. And once we found raptor, someone went back and found that one of the DAF mutants 1.00

00:31:56.980 was actually raptor. So that connected again to mTORC1. Now, not only were there mutations in mTORC1 itself,

00:32:03.460 the C. elegans mTORC1, but also in the C. elegans raptor that connected it to it.

00:32:08.660 We did not realize that, you know, of course, our paper was published in Cells.

00:32:12.020 Stuart Freiber's paper was published in Nature. I remember Nature wrote in News and View.

00:32:15.300 So people appreciated that the finding of mTORC1 mattered. But I think more from,

00:32:20.180 okay, this is a new signaling pathway. This is a new component. I don't think we realized that it

00:32:26.740 really, we certainly didn't, at the center of so many important processes as we do now.

00:32:32.420 People sometimes joke and say, well, you know, mTOR does everything, right? So if something does

00:32:36.740 everything at some point, okay, how interesting is it, right? And so it's a funny line.

00:32:42.020 Not a lot of people studying oxygen these days.

00:32:43.860 Exactly. Or like from the ribosome. We all appreciate the ribosome makes proteins,

00:32:47.620 and so it's important for everything. But you don't study it as a sort of a something that's

00:32:52.500 regulatable. Although now we realize the ribosome is a very regular channel.

00:32:56.580 Exactly. So it starts to fall into that category. But luckily, we have enough of these sort of

00:33:00.660 regulatory systems that clearly shows us it's a very regular process in the cell.

00:33:04.340 But today, mTOR, and by extension, rapamycin and its analogs are really interesting,

00:33:11.700 not just in your world, but in mine. So the plebs over here out in the peanut gallery, 1.00

00:33:18.500 this is super interesting, right? This is potentially a molecule that could make people live longer,

00:33:26.820 at least if what it does in yeast, flies, worms, and mammals is any indication.

00:33:33.540 So why is it that rapamycin, or asked another way, why is it that the inhibition of mTOR,

00:33:43.220 or specifically mTOR complex one, as you'll probably elaborate on, can extend life?

00:33:47.380 I find that a very interesting question, and it's a question that I'm often asked. And I think,

00:33:52.100 we should say up front, we don't know the answer to that question.

00:33:54.900 One way of addressing it is that you can eliminate many of the things that mTOR1 does,

00:34:02.740 and then ask, well, now why inhibit mTOR1? Do I still get lifespan effects?

00:34:06.900 If you do that and look at many different processes, probably you'd vote autophagy

00:34:11.060 is the most important thing that it regulates, which as you know, autophagy is this self-eating

00:34:14.900 process where the cell breaks down some of its own components, and presumably has to remake them.

00:34:20.260 And so, in a kind of naive way, you might imagine that what you're doing is throwing

00:34:25.060 out the old and making new. And again, naively, you might think, well, that's going to rejuvenate

00:34:29.060 a cell, although none of that is, of course, proven. So that would be a simple answer,

00:34:33.620 but it clearly is not the whole answer. So my answer to your question, why mTOR modulation has

00:34:39.700 these longevity effects, and yet many other pathways that in some ways are as complicated

00:34:45.940 and as important for a variety of other things don't. And this is the way I think about it.

00:34:51.620 I think about it, like I try to analogize it a little bit to like a building, right? So if I

00:34:55.780 wanted to take a building like this one and make it younger, rejuvenate it, you know, I can't just get

00:35:03.220 a plumber or electrician or a painter, right? Or a carpenter. Because the building has many

00:35:10.180 different features of which all of them have aged. What you really need is a general contractor,

00:35:15.940 right? Who's going to then bring in all of those subcontractors and fix all the subsystems. We look

00:35:21.780 at an old building. An old building has lots of things that are messed up from it, from the electrical

00:35:26.020 systems to the windows to everything. And to some extent, mTOR is like the general contractor for the

00:35:31.220 cell. I don't know of any other pathway that does as many things, right? mTOR basically has a finger

00:35:38.580 in every major process in the cell. And so I think another way of thinking about your question is,

00:35:45.780 what's the simplest way to manipulate a cell so that lots of things are changed? And the answer

00:35:52.020 to that is to modulate mTOR. Because all these other pathways will, you know, maybe some of them

00:35:56.100 will regulate transcription. Maybe some will do translation. Some are going to

00:36:00.580 change the shape of the cell. But if you've got to do all those things, plus more,

00:36:05.060 the only way of doing it with like a single hit is to go after mTOR. It is like the thing. It's like

00:36:10.740 the brain of the cell, which then has all these subroutines that do all these things. And so to me,

00:36:16.340 that's the simple answer, is that to impact the state of a cell, to rejuvenate it, to slow the aging

00:36:23.380 process, you can't do one thing. You can't do two things. You can't do three things. You can't do 10

00:36:27.060 things. You probably have to do 100 things. And the only way you can do all of those things with

00:36:31.300 one button is to go after mTOR. Now, in biology, that tends to be a two-edged sword,

00:36:38.100 right? Because presumably, if you have one switch that controls so much, you know, if you have the

00:36:46.580 wrong general contractor, or if the general contractor does the wrong thing, the effect

00:36:51.780 is much more noticeable. So when did it become apparent to you, or how is it apparent to you that

00:36:58.340 this isn't just a linear relationship between signal and response?

00:37:02.820 This is a very good point, right? So you could say, well, as a general contractor, there's a lot

00:37:06.500 of things. And so not only is anti-aging one of the things it does, but how you sort of, for example,

00:37:13.380 sperm production, which is a potential target heart function, right? All these things require it.

00:37:18.100 And so, okay, you might get the anti-aging effects, but you're also going to get all the downsides.

00:37:22.100 And I think that is certainly true. And that's the major issue with targeting mTOR.

00:37:26.740 And so the-

00:37:27.080 Because at the time you really kicked your efforts off here, people thought of rapamycin and mTOR

00:37:32.260 as a one-trick pony, which was you give this drug every day, your immune system, specifically your

00:37:38.000 cellular immune system, doesn't work as well. And at least for that subset of patients who had

00:37:42.800 foreign organs in their body, that's a reasonable thing to have. 0.99

00:37:46.140 And incidentally, you know, there is now, so funny, rapamycin started as an immunosuppressant.

00:37:51.820 The interest in mTOR in the immune system pretty much was unexistent. And now there's an entire

00:37:57.700 field of so-called immunometabolism, of which mTOR is probably 50% of that whole field. And so it's

00:38:02.720 mTORC1, mTORC2 in different immune cells, Tregs, right, T-helpers.

00:38:06.380 How much of this came out of the Novartis work from three years ago? Did this precede that or-

00:38:11.680 Well, it's precede that. I mean, the Novartis work was the first sort of work in humans,

00:38:15.420 right? They clearly showed modulation, beneficial modulation in the immune system. But in terms

00:38:19.400 of studying which immune cells are most affected by rapamycin and what the role of mTORC1, that's

00:38:24.480 come out of the academic world by a number of groups that were heavily enabled by the discovery

00:38:30.040 of Raptor and Richter because now you could genetically inhibit each of those. And one of the things that my

00:38:35.760 lab we've really tried to do is to put our mice out there. And so people use, for example, our raptor

00:38:40.440 mouse or Phlox, so-called Phlox raptor mouse a lot. But this question of, in a way, what you're saying

00:38:46.560 is how much can we sort of tolerate of mTOR modulation for beneficial effects versus non-beneficial

00:38:51.800 ones? And again, I don't think we have the answer to that. To some extent, rapamycin is not a complete mTORC1

00:38:59.040 inhibition. We know that. And complete mTORC1 inhibition is probably not tolerated. And so rapamycin might be as

00:39:05.560 good as you can get. You get some modulation. Well, I'll say a little bit more about that. So you're

00:39:09.560 saying if we could wave a magic wand, Bobby was very eloquently spoke about why inhibition of mTORC1

00:39:15.820 leads to inhibition of mTORC2 and what the temporal relationship of that might be. But I don't think

00:39:21.180 we got into this issue, which is if I could wave a magic wand and completely inhibit mTORC1 complex 1,

00:39:28.320 not lay a hand on complex 2, why wouldn't that be a good thing? Because mTORC1 is probably required

00:39:35.380 for the growth of any normal cell. So for a cell to basically make its organelles, to make its proteins,

00:39:43.300 to divide, mTORC1 is probably an essential. So at that level, it would start to mimic a crude

00:39:50.220 chemotherapeutic agent that modulates cells. It becomes 5-FU at a ridiculous dose, or something that's

00:39:58.360 going to basically slough off epithelial cells. Exactly. It's going to cause basically atrophy

00:40:02.740 of everything, anti-growth, and probably cell death. And in fact, in many tissues where you

00:40:07.380 delete raptor, it can be quite bad. That's the phenotype? Yeah. Like an epithelium in the gut,

00:40:12.740 at least when we've looked, that's what I'm saying. So I don't think there's two issues going on here.

00:40:17.080 As Bobby Shirley told you, rapamycin will also, with a longer time point, inhibit mTORC2,

00:40:22.120 and that is potentially bad for glucose homeostasis. The other issue is that rapamycin doesn't fully

00:40:28.280 inhibit mTORC1. So in an ideal world, you'd like to have, and what I mean by that is that mTORC1

00:40:33.820 probably has dozens of substrates. And rapamycin only effectively inhibits some of them and not

00:40:39.060 others. Including, for example, autophagy is relatively weakly modulated by rapamycin.

00:40:44.800 Why is that? Because the substrate, what rapamycin basically does is sort of occlude

00:40:50.060 the substrate binding channel in mTORC1. And it's basically, physically occluding. And depending,

00:40:57.040 probably, we don't, you know, this is somewhat hand-waving, but there's some evidence for this.

00:41:01.800 Probably the size of the substrate, if it's smaller, it might get easier, and it's not occluded. If

00:41:06.020 it's bigger, it's going to get blocked. And so probably the key substrates in the autophagy pathway

00:41:09.840 simply are not as affected because they get into the kinase domain of mTORC1 still.

00:41:14.440 By the way, is this issue different for any of the rapalogs?

00:41:18.100 No. They're all basically producing the same effect as rapamycin.

00:41:22.980 Some people might argue differently from that. But in my experience of them,

00:41:27.380 they are basically like rapamycin with maybe different PK, PD properties. But from a mechanistic

00:41:33.400 point of view, I wouldn't expect differences. And I haven't seen those differences. But so in an ideal

00:41:39.640 world, you might want a molecule that would inhibit all the substrates of mTORC1, not touch mTORC2.

00:41:45.320 But not do it constitutively.

00:41:47.800 Not do it constitutively. But also maybe not to 100% inhibition. So I'm not sure I would use that

00:41:52.320 molecule to wipe out mTORC1. I would use it to bring down all the mTORC1 activity of all towards

00:41:58.600 our surface to some extent, leaving mTORC2 intact. I think that's going to be very hard to do by

00:42:04.620 targeting mTORC1 itself. Because mTORC1, mTORC2 share the same kinase domain. And so you can't go for

00:42:11.440 the ATP binding site, which is most kinase inhibitors. mTORC is a kinase, a protein kinase,

00:42:16.900 like Levec, for example. They all go for the ATP binding site. So we're probably not going to do

00:42:21.280 it for that. And so our view is that the way to accomplish that is not to go after mTORC1 itself,

00:42:26.420 but to go after its upstream regulators. And the big benefit, in my view, of doing that

00:42:31.840 is that you should be able to have something now that modulates all mTORC1 substrate.

00:42:35.540 And you can also start to get tissue specificity because these regulators vary in importance across

00:42:41.140 tissues. The aspect of this pathway that's kept our attention for two decades at this point is

00:42:47.460 that mTORC1 is basically regulated by everything. Anything I do to the cell, whether I change nutrients,

00:42:53.180 oxygens, pH, growth factors, osmotic... What's the direct effect of glucose and or insulin on mTORC1?

00:43:00.900 It obviously plays an enormous role on complex II. It seems to activate them, right? So through

00:43:06.180 independent pathways, there seems to be a pathway through which insulin acts, and there seems to

00:43:09.900 be a pathway through which glucose acts. And even the glucose pathway probably has several sub-branches

00:43:14.220 to it. I see. Which, again, teleologically makes sense because if it's a nutrient sensor, it should be

00:43:20.360 activated by nutrients. But it becomes very complicated now because you have the same nutrient

00:43:26.260 acting in completely different areas. Right. And that's probably because you're looking at...

00:43:32.420 In the cells that we use in culture, we can get both of these sensing systems. We're probably in

00:43:36.320 vivo. There's tissues that are going to care more about the insulin arm. There's tissues that are

00:43:39.460 going to care much more about the glucose arm. And there's some that are going to care about both.

00:43:42.800 Right. So if you think about being a peripheral tissue, let's say you're a cell somewhere in your

00:43:47.680 leg. And you need to make a decision. A muscle cell. Let's say a muscle cell. You need to decide whether

00:43:52.040 you're in an anabolic state or a catabolic one. So clearly there's things of use and all that.

00:43:56.460 But let's say just in response to nutrition, you kind of want two pieces of information, right?

00:44:01.220 One, you want to know that the organism that you live in as a whole is in a fed state. You want to

00:44:07.140 be a good member of the community. And that is reflected by things like insulin, which basically

00:44:12.480 tells you the pancreas. It's a global metric. Right. Pancreas, saw glucose, we sent out insulin. Yep.

00:44:17.820 And the other one is you actually want to know that you have the nutrient that you need.

00:44:21.940 You could have like central command telling you, hey, I got glucose. But if you don't have glucose,

00:44:27.040 you can't do anything. It's a local issue.

00:44:28.760 And so you really want like the central signal and you want the local signal.

00:44:33.100 So I think one can interpret that the pathway senses both the nutrient.

00:44:37.000 So the amino acid can be a local, the glucose molecule itself.

00:44:40.820 Itself is a local one. For sure. We know it is.

00:44:43.140 Whereas the larger peptide can be sort of the central command.

00:44:46.400 And now you can extrapolate that to, there are many signals that are secreted in response to food,

00:44:51.480 right? Insulin just being one of them. And then there are many local nutrients. And now you can

00:44:55.040 start to see the enormous complexity of the problem, right? And now you add a temporal component to it.

00:45:00.120 And now you actually add a concentration. Now you add a tissue, yeah.

00:45:02.140 And then you make things tissue specific. So our view has been, if we can find the sensors of the

00:45:08.940 nutrients, and that's what we focused on. So we focus a lot on amino acids, but we're also working on

00:45:13.380 glucose. If we could find those sensors, by definition, they'll have small molecule binding

00:45:18.600 pockets, right? Because they bind nutrients, which are small molecules. Although they're small,

00:45:22.280 small, small molecules compared to drugs. We should build a drug.

00:45:25.960 So in 2015, in the fall, you had these two papers that came out that looked at leucine, of course,

00:45:32.380 huge interest, but also arginine. Leucine and arginine can get into a cell very easily.

00:45:37.560 Do they passively diffuse in?

00:45:39.340 There's transporters.

00:45:40.100 Relatively straightforward transporters.

00:45:41.340 But they're high-capacity transporters.

00:45:42.680 Okay. In the cytosol, these amino acids bind to receptors that then downstream result in the

00:45:50.780 activation of TOR, specifically mTOR complex 1. People have long talked about how branched-chain

00:45:58.720 amino acids are important for building muscle. Specifically to be consumed in a workout was always

00:46:05.280 sort of the rhetoric, presumably because that's a very catabolic time for muscle. It now seems that

00:46:11.520 that makes sense, at least in the presence of what leucine's doing. Do we think that the other

00:46:15.240 two branched-chain amino acids are having any effect?

00:46:17.600 In our... At least when we look at the receptor we found for leucine, and then we look at the

00:46:22.740 concentrations at which it might bind the other branched-chain amino acids, we don't think those

00:46:26.820 affinities are relevant. Particularly valine is way too low. Isoleucine maybe in some situations

00:46:32.480 could act through the receptor, but unlikely. So in our hands, again, looking in a very molecular

00:46:38.260 point of view, it really seems like leucine is the key one. And I would think, you know,

00:46:42.680 from talking to bodybuilders and looking at bodybuilding products out there, it does seem

00:46:47.400 like leucine is the one that people have focused on more than individual ones.

00:46:52.400 Yeah. And tell me, the difference between leucine and arginine then with respect to the signaling

00:46:56.640 is what?

00:46:57.160 One way of sort of conceptualizing Amateur Kwan is it wants to drive anabolism. And what

00:47:02.840 its goal is to detect when something's missing for that. So we tend to think of the pathway

00:47:08.000 like when we turn it on, but probably its really key function is to turn off when something

00:47:12.380 is missing, right? Let's say you're building a house. All of a sudden you'd run out of concrete.

00:47:15.720 You want to turn off. All of a sudden you run out of wood. You want to turn off. And so this

00:47:18.740 pathway...

00:47:18.940 So the default is on?

00:47:20.560 The default, when everything is there, is on. But it's built, it's organized in such a way

00:47:25.100 that the removal of anything can turn it off.

00:47:28.320 Efficiently turns off.

00:47:29.620 Now, this is going to vary, obviously, between different tissues. And so the pathway evolved

00:47:34.040 that it needs to detect leucine and it needs to detect arginine, at least in most tissues.

00:47:40.460 Now, why is that? They're both amino acids. If you think about this during the course of

00:47:44.540 evolution, you're an animal that ate on other animals. You ate its muscle. You got protein.

00:47:49.740 Why do you need to sense two different amino acids? And they're very structurally different,

00:47:53.100 right? They're about as structurally different as you could get in terms of amino acids.

00:47:57.460 We don't have an answer to that. Why did evolution do that? Pick these two amino acids.

00:48:02.860 I mean, that's a phenomenal question. I don't know enough about amino acids to know

00:48:08.120 what the evolution of amino acids looks like. I mean, a billion years ago, I assume we didn't

00:48:13.180 have the same amino acids.

00:48:14.440 No, I think we did.

00:48:15.280 We did.

00:48:15.880 So most all forms of life have problems.

00:48:18.940 So basically, from the beginning of when we had DNA to RNA to protein, we had the exact

00:48:24.160 same amino acids. So then it's even more of a mystery. Why in the heck did we...

00:48:28.440 Why are some things not?

00:48:29.340 Part of people in the lab that I'm sort of encouraging to look at other organisms, because

00:48:33.520 the sensing part of the system is probably evolving quite quickly because different organisms

00:48:38.420 live in different environments. And so for example, flies, we know already, don't care

00:48:43.160 about arginine. They care about leucine, and it turns out they care about a whole bunch

00:48:46.500 of other amino acids that we don't care about.

00:48:47.680 What about yeast?

00:48:49.000 So yeast, in many ways, is the most mysterious, because yeast... So we don't know any sensors

00:48:53.700 in yeast, and none of the sensors we have found are in yeast. And that's because yeast

00:48:58.080 can make amino acids. To a certain extent, yeast is very primitive. You give it nitrogen,

00:49:02.700 you give it carbon, it's going to make every amino acid. So things like leucine, which

00:49:06.360 are essential to us, are not essential to yeast. They can make it.

00:49:08.880 In what state do yeast cease to activate TOR, only in the absence of the essential elements?

00:49:16.760 So regulation of TOR is not as well studied in yeast, because it's harder to detect the

00:49:20.700 output. And so typically what people do is they change the nitrogen source, or they change

00:49:24.620 the carbon source. And so my view is that yeast has to have a sensor of nitrogen, whatever

00:49:29.580 that means, right? It's not so easy to understand what that means. And a sensor of carbon. But not

00:49:34.840 a sensor of individual amino acids. And as we find more sensors... So we now have... We've now

00:49:39.340 connected the path to methionine sensing. And we have a receptor for that. That yeast doesn't have

00:49:45.000 that either. And so I've actually also tried to encourage people in the lab to look for what might

00:49:49.740 be a nitrogen sensor in yeast. For example, ammonia, which is a simple form of nitrogen. Maybe that's

00:49:55.400 what's sensed. Maybe acetate is what's sensed for carbon. But we don't know.

00:50:00.020 So say more about methionine, because in the protein restriction literature, certainly one

00:50:05.860 argument is that methionine restriction specifically could be beneficial if one believes that low IGF

00:50:12.200 is beneficial. And we could talk about whether that's causally the case or not, not even getting

00:50:17.960 into the IGF binding proteins. Where does methionine fit into TOR?

00:50:21.960 Right. So methionine actually is a very interesting one. As you said, there's extensive literature on

00:50:26.200 what's so-called methionine restriction having quite beneficial effects from glucose homeostasis,

00:50:30.880 actually to quite very reasonable lifespan extension effect, as good as caloric restriction.

00:50:35.620 And there are some papers in flies, genetic papers, that suggest that some of the methionine

00:50:41.440 restriction effects go through the TOR pathway in flies. We got to this basically through the protein.

00:50:48.440 We found a protein of unknown function, and we tried to figure out what it did. And it turned out to

00:50:52.260 be a sensor of this metabolite called SAM, S-adenosylmethionine, which is basically made

00:50:57.420 by methionine. So it's actually quite interesting.

00:50:59.420 And people supplement with the variant of SAM, SAM-E.

00:51:02.100 Exactly, right. SAM actually has some pretty interesting clinical effects. Actually, some

00:51:05.820 quite convincing data on antidepressive effects of SAM out there. So the sensor here is interesting,

00:51:13.040 because the other sensors we have directly bind leucine, directly bind arginine. This one doesn't

00:51:17.360 bind directly to methionine. It binds to a metabolite made by methionine, which is SAM, which SAM, many

00:51:23.020 things can feed into SAM. So it actually can integrate lots of signals. So this sensor basically

00:51:28.360 behaves like the other ones. As soon as methionine levels go down, SAM levels go down, this sensor

00:51:35.120 therefore inhibits this pathway.

00:51:38.260 And so SAM would not be a longevity agent by the oversimplification that excess SAM would be

00:51:43.620 akin to excess methionine, would be akin to failing to inhibit TOR.

00:51:48.420 Exactly. So methionine restriction could be presumably rescued by giving SAM, right? And we

00:51:54.980 actually know in the pathway that we've built in cells that that's true. You can bypass methionine

00:52:00.220 simply by giving SAM. So a molecule that could basically trick this sensor into thinking that SAM was

00:52:06.300 not there would be a quite interesting one. I think methionine is probably the most interesting

00:52:11.680 of these amino acids because if you fast an animal, methionine is the amino acid that drops the

00:52:16.860 most. And the reason for this... And you looked at all of the amino acids and that's... In mice.

00:52:22.000 Okay. So we should do some of this in humans. But it kind of makes sense. I can volunteer if you want.

00:52:27.380 Well, we could definitely profile. Yeah. The reason probably is that arginine, you can make some,

00:52:32.380 right? Your liver can make it. And then leucine is an amino acid that's an essential amino acid,

00:52:37.040 but to some extent, it's only used to make protein. That's it. So when you fast, you start to break down

00:52:41.800 your muscle and release leucine. Methionine is not only an essential amino acid that you use to make

00:52:46.940 protein. And remember, the first amino acid of all proteins is the methionine. So by definition,

00:52:51.900 every single protein has methionine. But it's also incredibly metabolically active through SAM and the

00:52:58.340 so-called methionine cycle. So when you fast, you probably just can't generate enough methionine by breaking

00:53:04.000 down your proteins to keep up with methionine demand while you can for leucine. So if you look

00:53:09.380 at the blood of an animal that's fasting, methionine is the number one dropped amino acid.

00:53:13.820 Do we think that's true in autophagy in general? What do you mean in autophagy?

00:53:16.580 If we put an animal into a state that induces autophagy independent of caloric restriction,

00:53:22.040 so for example, would we see the drop in methionine as a readout?

00:53:28.160 You know, you might expect it to go up actually, right? Because autophagy is going to break down protein

00:53:31.760 and you might methionine. Yeah, if you're not recycling. If you're not recycling. And it depends

00:53:34.920 if you induce in the state, for example, post-exercise. I don't know what we know about

00:53:40.260 the use of methionine and SAM, right? Are you doing a lot? So SAM is used for methylation reactions,

00:53:45.460 right? And there are hundreds of methylation reactions. SAM is the second most common cofactor

00:53:51.180 in enzymes after ATP, right? Everyone knows about ATP and ATP is energy. And then it's used in many,

00:53:58.060 many, many reactions for phosphorylation. But SAM is the second most common one.

00:54:01.300 So there are literally hundreds of proteins that use SAM. So maybe after exercise, a lot of SAM is

00:54:07.040 used. I don't know. It's an interesting question, right? But with fasting, methionine definitely

00:54:11.940 plummets. SAM definitely plummets. And so we're now generating the right animal models to ask whether

00:54:18.320 the sensor we have is involved in the effects of methionine restriction. So we can basically knock

00:54:23.600 it out and then do methionine restriction. And if the animal doesn't have the health benefits of

00:54:28.740 methionine restriction, it means that this sensor and by extension, mTORC1 are the key mediators of

00:54:33.580 methionine restriction. So we'll see.

00:54:34.480 So coming back to rapamycin specifically and all of its limitations. So we've established that you

00:54:40.340 can't just take rapamycin all day, every day because that experiment's been done. That's the

00:54:45.080 clinical utilization of it. Certainly the animal data have suggested and the human data have suggested

00:54:51.240 that an intermittent dosing of rapamycin could produce a beneficial phenotype with respect to

00:54:56.600 longevity specifically and also with respect to immune function.

00:54:59.100 So if you had to guess based on triangulating these data, assuming no new drug came along that

00:55:07.480 was going to selectively do some of the things that we've discussed, how would one dose in an animal

00:55:15.060 or a human for that matter, rapamycin to increase the odds in favor of longevity and against harmful

00:55:23.340 side effects, which presumably the most obvious ones would be immune suppression and or glucose,

00:55:28.660 homeostasis disruption.

00:55:30.260 Yeah. And also epithelial sort of toxicity, right? Particularly the GI epithelium.

00:55:34.700 So I think the intermittent approach is definitely the one that makes sense because if you buy the

00:55:39.160 idea that you want to induce autophagy, which, you know, a lot of people, of course, like yourself,

00:55:44.020 who studied the effects of fasting also view that that's one of the goals of fasting is to induce

00:55:48.400 autophagy. So if we basically want to chemically induce autophagy without fasting, I think the

00:55:53.800 intermittent dose is what makes sense is you basically let, have an induction autophagy,

00:55:58.500 a relatively weak one with rapamycin, but then let the system rebuild. It's clear that both

00:56:03.160 mTOR, you need just right amounts, right? You can't have too little. It's toxic. You have too much.

00:56:08.420 It's toxic. The same thing with autophagy. If you remove autophagy, it's really toxic. If you have

00:56:12.780 too much autophagy, it's really toxic.

00:56:14.940 Cycling, anabolism, catabolism might be the single most important thing to do.

00:56:19.660 It might be, right? And I think it's hard for us to know, but those intermittent

00:56:24.160 dosing strategies, every other day feeding strategies, all point to that. And the genetics

00:56:31.320 where too much is bad and too little is bad also point to that, right? So if you genetically inhibit

00:56:36.700 this pathway by deleting raptor, if you genetically activate it by deleting these repressors called the

00:56:42.360 tuberous sclerosis complex, both are bad. Both, in fact, in many tissues like the muscle give the

00:56:46.900 same output. They get muscular dystrophy. Yeah, I was just about to say, there's an

00:56:49.920 overlap with muscular dystrophy here, isn't there? Yeah, exactly.

00:56:52.540 So this may be a theoretical question, but when we think about the life-extending properties of

00:56:59.720 rapamycin, do we believe that it is a result of delaying the clinical onset of disease? Let's use a

00:57:09.280 disease where that tends to be more binary, like cancer. But obviously, cancer spends probably 70% to 80%

00:57:15.220 of its time undetectable, but due to just the law of growth, it becomes detectable only at the end.

00:57:21.140 So do we think that in as much as, say, taking these agents would allow you to live longer by

00:57:26.340 not dying from cancer at the same period of time, does it delay the time it takes for cancer to become

00:57:31.800 clinically detectable and or delay the demise of the animal once it has that cancer?

00:57:38.960 Yeah, I think specifically, you know, in the case of cancer, rapamycin is, there's some situations

00:57:43.800 where it has some decent activity. But in general, it's not a cytotoxic agent, right? It's not going

00:57:47.980 to kill a cancer cell. It's really going to... Once an organism has cancer, do we know if it's doing

00:57:52.220 anything to prevent the development of cancer? We don't know that well. And the only, there actually

00:57:56.780 has been some epidemiological data where people have compared cancer rates in transplant patients.

00:58:02.580 Identical patients who are with and without rapamycin.

00:58:04.080 FK506 versus rapamycin. And it's actually quite interesting because, as you know, immunosuppression

00:58:09.480 in general is associated with higher cancer rates, right? The idea that you have less immune

00:58:13.920 surveillance, that's not seen with rapamycin. So it is seen with FK506. It's not seen with rapamycin.

00:58:19.720 And the argument has been that rapamycin itself has cancer cell autonomous...

00:58:25.180 Independent of the immune modulation problem.

00:58:28.180 So you're presumably getting less immune surveillance because it's immunosuppressant, although, of course,

00:58:32.480 that's not proven. But you're mitigating that by now directly targeting the cancer.

00:58:36.680 And they've canceled each other out.

00:58:37.740 They've canceled each other out.

00:58:38.340 And you know the size of the effect from the FK506 cohort.

00:58:40.920 Exactly. And other immunosuppressants, I think, cyclosporine, have also been looked at that.

00:58:45.160 So my bet would be that in the case of cancer, you're not going to...

00:58:50.120 You're not going to cure cancer once you've got it, but you probably...

00:58:52.880 I don't think you're going to modulate the incidence, like the mutational frequencies that

00:58:57.060 are giving you cancer, right? So if you think of... Cancer, in a way, is easier to think

00:59:01.260 about when it starts because you'd say, well, it starts when you have a cell that has all

00:59:05.780 the requisite mutations to be a cell that has uncontrolled growth.

00:59:11.000 So if that's the point it starts, I think we're not going to affect that.

00:59:15.640 But once that cell exists and now has to start growing and also escaping the immune system,

00:59:21.320 I do think that's probably what you're going to affect.

00:59:24.800 In other diseases, like, for example, cardiovascular disease, where you could imagine things like

00:59:29.100 autophagy could be quite modulatory, I think you can imagine that you're also being and affecting

00:59:35.360 the incidence at the exact point at which you'd say, okay, this is an atherosclerotic plaque or not.

00:59:40.680 What do we know about rapamycin and TOR in the brain, especially with respect to neurodegeneration?

00:59:47.640 Yeah, that's a really interesting one. And that probably is a really important question for the

00:59:52.560 future. So we know autophagy matters a lot in the brain. If you delete autophagy, and really,

00:59:56.940 I think Mitsushima was the person who kind of made autophagy interesting to lots of people.

01:00:01.720 And it was awarded the Nobel Prize.

01:00:03.160 No, no, he wasn't.

01:00:04.040 Oh, he wasn't.

01:00:04.560 Well, Shumi was for original...

01:00:05.520 Oh, he didn't share.

01:00:06.680 He didn't know that, which I think was a bit of an oversight in my view. But anyhow, he basically

01:00:11.020 studied autophagy in the brain, made mutations, showed you got neurodegeneration, right? So that

01:00:14.880 was a really important finding. Connects up to lysosomal storage diseases, which, you know,

01:00:19.520 autophagy, basically the autophagosome fuses with a lysosome, so now you have that connection.

01:00:23.060 So I think, like in all tissues, it's a bit of a double-edged sword. You clearly need mTORC1

01:00:30.160 activity to maintain healthy synapses, certainly during brain growth. If you make mutations around

01:00:36.200 a growing animal, you basically don't have a cortex, right? On the other hand, you clearly

01:00:41.940 need to be able to modulate mTORC1 to have some level of autophagy to keep the system healthy.

01:00:49.280 Now, you could debate, is that in neurons? Is that in glia? It's probably in both. People

01:00:53.400 have made mutants in, certainly in neurons, which suggests it's both, but then some of

01:00:57.400 those promoters are a little bit dirty. But the real question in the brain is, what modulates

01:01:01.120 mTORC1? Because it's not probably nutrients.

01:01:04.240 Because they're so constant, you mean?

01:01:05.820 Exactly. Your brain, your body...

01:01:08.080 Yeah, your brain prioritizes nutrients in the brain over it.

01:01:10.320 It basically protects your body. So if you take an animal and you fast it for two days,

01:01:14.460 a mouse, it loses a lot of weight, 25% of its weight. And now you take every single tissue

01:01:19.420 and you weigh it, every tissue has shrunk. Some, like the thymus, have shrunk ridiculously.

01:01:24.600 The kidney shrinks, which you wouldn't expect. The heart shrinks. The brain, nothing. Now,

01:01:29.300 clearly, probably if you... In a mouse, you can't do that extrema fast. And so the body protects

01:01:34.000 the brain from a nutrient point of view, yet mTORC1 activity is high there. Clearly, we know

01:01:38.500 that we have to modulate autophagy. So something must be inhibiting mTORC1.

01:01:43.160 By the way, this is my peripheral argument for why, and I'm in the huge minority here,

01:01:47.960 I do not think the brain is really the appetitive center. I think it's the modulator. But I,

01:01:52.920 for that exact reason, think it wouldn't make sense for evolution to put our appetite center

01:01:58.140 in our brain. It should be in the periphery. It should be in the liver, I think. I think the

01:02:02.200 liver should be the...

01:02:02.580 Yeah, but people argue that the things of the hypothalamus are in the periphery, right?

01:02:05.360 Because they're not protected. There are parts of your brain, like the hypothalamus,

01:02:08.500 the point is, I think it has to be, your appetite center needs to be regulated to something that

01:02:13.400 senses very rapid change.

01:02:15.160 The outside of it, for sure.

01:02:16.400 Yeah, yeah.

01:02:16.800 For sure. And exactly where it is, and the bottom line is probably...

01:02:20.200 But I never thought of it through the lens that you just explained it, which was

01:02:22.980 the implication of that for TOR is enormous.

01:02:25.560 Yeah.

01:02:25.900 So does TOR look different in the brain? Or, I mean, obviously the protein won't, but

01:02:30.100 do the cofactors around it look different?

01:02:32.340 So really, you know, we keep talking. We have never done, for example, biochemistry out of the brain.

01:02:36.420 And it's something that would be very interesting to go and do now. I think now it's something

01:02:41.320 we talk quite a bit as a lab to do. We haven't quite done it at all. But then what actually

01:02:46.220 regulates it? It's very clear that neuronal activity does. But are there, like as you're

01:02:51.220 suggesting, maybe neuronal specific factors to regulate? I think that's a completely open

01:02:55.460 area. I've tried to get some of my students interested in that. My brother's a neuroscientist.

01:02:58.920 He's argued we should really do some work there. We just haven't. Maybe when we run out of

01:03:03.460 sensors in the periphery, we'll go to the brain. And that's where I purified mTOR, was

01:03:09.400 out of the brain. So there's a ton of mTOR in the brain. And I did that not because I

01:03:12.900 was like, whatever. I basically measured how much there was. And it was clear the brain

01:03:17.220 had the most.

01:03:17.780 One of the challenges of studying biology in humans is that you can't do the same experiments

01:03:23.760 you can do in animals. If we had a Gedanken experiment where you could take a sufficiently

01:03:29.680 large number of human subjects and divide them into groups. So you had a control group.

01:03:33.840 These guys were going to do everything that the standard American does. You had a group

01:03:38.280 that you could give rapamycin to in any way, shape, or form, you decide. And then you had

01:03:43.180 a group in which you could manipulate their behaviors. And they would behave as animals.

01:03:49.660 They would do anything you want with respect to how they would eat or how much or when, exercise,

01:03:53.540 whatever you like. First question is, how would you design arms two and three to have the

01:03:58.040 best outcome with respect to longevity? And then I'm very curious to know what you think the

01:04:02.220 difference between groups two and three would look like.

01:04:04.200 So I think as we spoke before, I mean, the mTOR modulator arm would probably be an intermittent

01:04:07.880 type dosing one where hopefully we'd have biomarkers of that. And you and I have spoken

01:04:11.560 in the past, a biomarker for autophagy, for example, a biomarker for mTOR activity. You have

01:04:15.580 to decide what tissues you care about. Probably the muscle would be one that you'd want to focus

01:04:19.340 quite a bit on and perhaps the liver. Now, I don't think that mTOR modulation on its own

01:04:26.020 is going to give you all the benefits of good lifestyle modulation, right? So it might give

01:04:30.780 you lots of the benefits of the dietary manipulations, the fasting manipulation,

01:04:35.240 although clearly there's differences there. But I'm not sure if I'm going to give you all the

01:04:38.700 benefits of the exercise modulation, right? And so if on the lifestyle side, which you obviously

01:04:43.520 know better than almost anyone what you'd exactly want to do, there'd clearly be an exercise

01:04:48.060 component to it on top of a dietary component. I think mTOR modulation will give you a subset of that.

01:04:54.580 I see. So let's simplify the experiment then. Let's assume that everything but food is the same in

01:05:00.680 the groups, and the RAPA group gets the intermittent dose as you see fit, and the other group now can

01:05:06.960 fast or do any sort of CR mimicry that you want. Do you think that normalizes the playing field?

01:05:13.360 I think it gets a lot closer for a simple reason. So if you give an mTOR modulator versus a fast,

01:05:19.180 remember there's one really important difference, is that nutrients in the mTOR modulation case are

01:05:23.820 actually still high because the person is not fast. In fact, if you actually look in cells,

01:05:27.380 they can actually even be higher because the cell thinks it's starving. So it does all this-

01:05:31.600 So it shuts down processes that would accumulate them.

01:05:33.260 Yeah, and it upregulates more accumulation. And so we've looked in cells, so actually they tend to go

01:05:36.460 up versus a fast where things are going to be lower. On the other hand, if all those nutrients are

01:05:43.120 eventually doing their stuff by communicating through mTOR and you've sort of inhibited downstream,

01:05:49.620 to those downstream processes, things look the same. It doesn't matter. We have a lot of nutrients

01:05:53.040 here and very low nutrients here. So the modulation of mTOR is what matters. And so I think to answer

01:05:58.600 that question, we really need to understand whether all these nutrients, which are still there in the

01:06:03.220 fed state, have a lot of other signaling effects. And it would be naive to think that they don't.

01:06:09.760 They do. We know they do. Now, do they matter? And do they matter a lot? I don't think we know.

01:06:15.940 And I think a lot of the genetics and pharmacology would argue that within the range that we can

01:06:21.280 actually manipulate lifespan, it could be that those fasting regimens and rapamycin are somewhat

01:06:26.160 similar. And certainly in the mice, it appears to be at least similar, if not better, in favor of

01:06:34.200 rapamycin. Exactly. And that's why I'm particularly excited about the methionine restriction work,

01:06:38.600 because caloric restriction is not only hard to do in people, it's hard to do in animals too.

01:06:42.640 It's really hard. You have to weigh the food, pairwise feeding. It's a real pain. And it's a

01:06:47.820 real restriction to doing lots of experiments. While methionine restriction is a lot easier.

01:06:51.800 Yeah, just buy methionine and free chow.

01:06:53.240 Yeah, not totally free, but lower, right? And so there we can do these kind of experiments where

01:06:58.740 you could do methionine restriction plus rapamycin, right? And actually ask, do you get synergy? Do you

01:07:03.180 not? So I think that's going to be an intervention that's going to be a lot easier for us to play with.

01:07:08.680 So if resources weren't constrained, what are the sort of dream experiments or what's a dream

01:07:15.100 experiment that has been on your mind that you want to do, but it's either technically

01:07:19.340 we're not there yet, or it's just economically it poses a challenge?

01:07:23.460 Yeah. I think what I want to know, and this is, I think, the challenge for anyone who does what we

01:07:28.540 call signal transduction in a dish like we've done for a long time, is to really try to understand

01:07:33.540 in each different tissue, in a temporal fashion, in response to a variety of different diets and

01:07:39.360 nutritional states, what those tissues are actually doing. Right now we have these

01:07:44.100 little time points in the liver, in the muscle. We don't really have a deep sort of kinetic

01:07:51.440 understanding of what the actual physiology is doing, right? We'd really like to know.

01:07:55.820 Because even in the mouse, you know phosphorylation in one moment, you don't have an integral.

01:08:01.540 In one tissue. We don't have that, and we don't even have, it's just they're expensive and hard

01:08:06.000 experiments to do. Let's say I was really wanting to take mice, fast them, and in all different

01:08:11.500 tissues, and ideally, you know, tissues are complex, right? Now with all the single cell sequencing,

01:08:15.980 we're seeing much more complexity. So even in tissues like the liver that we tend to take a chunk

01:08:20.780 and sort of say it's liver, we know that's an amazing complexity, right? And so in ideal world,

01:08:25.600 we'd like to have a description of what all these different tissues are doing over time.

01:08:30.400 And then you'd like to do it under different diets, under whether they were obese mice,

01:08:34.640 whether they were exercise mice, and so that the matrix becomes ridiculous at that point.

01:08:39.440 But I think that's the future of signal transduction. People like me have done a good job of finding all

01:08:44.580 the pieces in some random cell line in a dish. And clearly, the systems have all these pieces,

01:08:50.260 because it allows them to communicate in vivo to many, many different upstream signals. And now

01:08:54.700 the challenge is, how do we go back and actually see that happening? And that's going to teach us,

01:08:59.900 okay, which tissues actually matter? We've talked a lot about longevity. Do you need to impact all

01:09:04.120 tissues? Is it the muscle? Is it the liver? Is it the brain maybe that you need to impact? You know,

01:09:08.540 people debate still how much rapamycin gets in the brain. Are you actually affecting the brain?

01:09:12.600 I think those are open questions to some extent. So it would be a complete description of what

01:09:19.060 these systems are doing over time, across many tissues, under many different states.

01:09:24.780 Well, David, we're pretty much out of time. But is there anything else that we should at least

01:09:28.480 take advantage of while you're here?

01:09:30.140 I think we already touched upon it when we talked about targeting mTORC1 or other things. And so I

01:09:35.340 think to me, and this is why we've sort of had commercial interest in this regard, how do we go and

01:09:41.060 target other things upstream that might be more amenable to giving us sort of more of this dream

01:09:45.560 molecule of a pan mTORC1 inhibitor and no mTORC2 activity?

01:09:50.980 David, thank you very much.

01:09:51.900 All right. Thank you very much.

01:09:52.460 This was a pleasure.

01:09:53.340 Thank you.

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The Peter Attia Drive - August 13, 2018

#09 - David Sabatini, M.D., Ph.D.： rapamycin and the discovery of mTOR — the nexus of aging and longevity？

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