# Anna Greka: Molecular Sleuthing for Rare Diseases

An episode of [Ground Truths](https://invisiblenetwork.ai/podcast/1728526108), 9 Mar 2025, 49 min.

Audio: https://api.substack.com/feed/podcast/158667841/adb6aa72b796d715979558c6890c8c43.mp3

Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH. A clip from our conversation The audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube. Transcript with audio and external links Eric Topol (00:06): Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute’s Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you. Anna Greka (00:48): Oh, thank you. It's a great honor to be here and glad to chat with you, Eric. Eric Topol (00:54): Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion. A Mysterious Kidney Disease Unraveled Anna Greka (01:59): Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising. Anna Greka (02:51): After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially…

## Transcript

[0:06] SPEAKER_01: Well, hello.
[0:06] SPEAKER_01: This is Eric Topol from Ground Truths.
[0:08] SPEAKER_01: And I am really delighted to welcome today Anna Greca.
[0:13] Anna is the president of the American Society for Clinical
[0:16] Investigation this year,
[0:18] SPEAKER_01: ASCI,
[0:18] SPEAKER_01: a very prestigious organization.
[0:21] SPEAKER_01: But she's also at Mass General Brigham,
[0:25] SPEAKER_01: a nephrologist,
[0:26] SPEAKER_01: a cell biologist,
[0:27] SPEAKER_01: a physician scientist,
[0:29] SPEAKER_01: a core member of the Broad Institute and the executive leadership team,
[0:33] SPEAKER_01: Harvard,
[0:33] SPEAKER_01: MIT.
[0:35] SPEAKER_01: So she's got a lot.
[0:37] SPEAKER_01: We got a lot to talk about all these different things you do.
[0:40] SPEAKER_01: You must be pretty darn unique,
[0:41] SPEAKER_01: Adam,
[0:42] SPEAKER_01: because I don't know any cell biologists,
[0:45] SPEAKER_01: nephrologists,
[0:45] SPEAKER_01: physician scientists like you.
[0:48] SPEAKER_00: Oh, thank you.
[0:50] SPEAKER_00: It's a great honor to be here and glad to chat with you, Eric.
[0:54] SPEAKER_01: Yeah,
[0:55] SPEAKER_01: well,
[0:55] SPEAKER_01: I had the real pleasure to hear you speak at a November conference,
[1:01] SPEAKER_01: the AI for Science Forum,
[1:04] SPEAKER_01: which we'll link to your panel,
[1:07] SPEAKER_01: where I was in a different panel,
[1:09] SPEAKER_01: but you spoke about your extraordinary work in it.
[1:14] it became clear that we need to get you on ground truth so you
[1:17] can tell your story
[1:19] to everybody so i thought rather than kind of going back from
[1:22] the past where you
[1:23] were in greece and somehow migrated to boston and all that
[1:27] we're going to get to
[1:28] that but i thought what you um you gave an amazing ted talk uh
[1:33] last year and it
[1:35] SPEAKER_01: really encapsulated one of the many
[1:37] uh phenomenal stories of your work as a molecular sleuth so
[1:43] maybe if you could give
[1:44] us the synopsis and of course we'll link to that so people
[1:47] could watch the whole uh
[1:48] talk but i think that mucin one or muck one as you call it um
[1:54] discovery uh is
[1:55] SPEAKER_01: really important to kind of ground our discussion
[1:59] SPEAKER_00: Oh, absolutely.
[2:00] SPEAKER_00: Yeah, it's an interesting story.
[2:03] SPEAKER_00: In some ways,
[2:04] SPEAKER_00: you know,
[2:04] SPEAKER_00: in my TED talk,
[2:05] SPEAKER_00: I highlight one of the important families of this story,
[2:08] SPEAKER_00: a family from Utah.
[2:10] But there's also other important families that are also part of
[2:13] the story.
[2:14] SPEAKER_00: And this is also what I spoke about in London when we were together.
[2:17] And this is really a sort of a medical mystery that initially
[2:21] started on the
[2:22] SPEAKER_00: Mediterranean island of Cyprus.
[2:24] where it was found that there were many families in which in
[2:28] every generation,
[2:29] several members suffered and ultimately died from what at the
[2:32] time was a mysterious
[2:34] SPEAKER_00: kidney disease.
[2:35] SPEAKER_00: This was more than 30 years ago.
[2:38] SPEAKER_00: And,
[2:38] SPEAKER_00: you know,
[2:38] SPEAKER_00: it was clear that there was something genetic going on,
[2:40] SPEAKER_00: but it was impossible to identify the gene.
[2:43] SPEAKER_00: And then even with the advent of next gen sequencing,
[2:46] SPEAKER_00: this is what's so interesting about this story.
[2:48] SPEAKER_00: It was still hard to find the gene, which is a little surprising, right?
[2:52] SPEAKER_00: You know,
[2:52] SPEAKER_00: after we were able to sequence,
[2:54] SPEAKER_00: you know,
[2:55] SPEAKER_00: families and identify sort of monogenic mutations pretty readily,
[2:59] SPEAKER_00: this was still very resistant.
[3:01] SPEAKER_00: And then it actually took the firepower of the Broad Institute.
[3:05] SPEAKER_00: And it's actually from a scientific perspective,
[3:07] an interesting story because they had to dust off the old
[3:10] fashioned Sanger
[3:11] SPEAKER_00: sequencing in order to get this done.
[3:14] But they were ultimately able to identify this mutation in a
[3:18] VNTR region of the
[3:20] SPEAKER_00: MUK1 gene,
[3:21] SPEAKER_00: the Mucin1 gene,
[3:23] SPEAKER_00: which I call a dark corner of the human genome.
[3:25] SPEAKER_00: You know,
[3:26] SPEAKER_00: it was really,
[3:26] SPEAKER_00: it's highly repetitive,
[3:28] SPEAKER_00: very GC rich,
[3:30] so it becomes very difficult to sequence through there with
[3:33] next-gen sequencing.
[3:34] SPEAKER_00: And so ultimately, the mutation, of course, was found.
[3:37] and um it's a single cytosine insertion in a stretch of
[3:42] cytosines that sort of
[3:43] causes this frame shift mutation and an early stop codon that
[3:47] essentially results
[3:48] in a neoprotein like a toxic what i call a mangled protein that
[3:53] sort of accumulates
[3:54] inside the kidney cells and that's where my sort of adventure
[3:57] began um i was uh it
[3:59] was eric lander's group who is the founding director of the
[4:02] broad who discovered
[4:03] SPEAKER_00: the mutation
[4:04] SPEAKER_00: And then through a conversation we had here in Boston,
[4:07] SPEAKER_00: we sort of discovered that there was an opportunity to collaborate.
[4:10] SPEAKER_00: And so that's how I came to the Broad.
[4:13] SPEAKER_00: That's the beginnings of this story.
[4:14] SPEAKER_00: I think what's fascinating about this story,
[4:18] SPEAKER_00: though,
[4:18] that starts in a remote Mediterranean island and then turns out
[4:21] to be a disease
[4:22] SPEAKER_00: that you can find in every continent all over the world.
[4:25] There are probably millions of patients with kidney disease in
[4:28] whom we haven't
[4:29] SPEAKER_00: recognized the existence of this mutation.
[4:32] SPEAKER_00: What's really interesting about it,
[4:34] SPEAKER_00: though,
[4:34] SPEAKER_00: is that,
[4:34] SPEAKER_00: you know,
[4:35] what we discovered is that the mangled protein that's a result
[4:39] of this misspelling,
[4:40] SPEAKER_00: of this mutation,
[4:41] SPEAKER_00: is ultimately captured by a family of CARGO receptors.
[4:46] SPEAKER_00: They call the T-Med CARGO receptors.
[4:48] And they end up sort of grabbing these misfolded proteins and
[4:51] holding on to them so
[4:52] SPEAKER_00: tight that it's impossible for the cell to get rid of them.
[4:55] And sort of they become this growing,
[4:57] you know,
[4:58] heap of molecular trash,
[4:59] if you will,
[5:00] SPEAKER_00: that becomes really hard to manage and the cells ultimately die.
[5:04] SPEAKER_00: So in the process of doing this molecular sleuthing,
[5:08] SPEAKER_00: as I call it,
[5:09] we actually also identified a small molecule that actually
[5:12] disrupts these cargo receptors.
[5:14] SPEAKER_00: And,
[5:14] SPEAKER_00: you know,
[5:15] SPEAKER_00: as I described in my TED talk,
[5:16] it's a little bit like having these cargo trucks that
[5:18] ultimately need to go into
[5:20] SPEAKER_00: the lysosome,
[5:21] SPEAKER_00: the cell's recycling facility.
[5:23] SPEAKER_00: And this is exactly what this small molecule can do.
[5:26] SPEAKER_00: And so we were
[5:27] know it was just like a remarkable story of discovery and then
[5:31] i think the most
[5:32] exciting of all is that um these cargo receptors turn out to be
[5:38] not only relevant
[5:38] SPEAKER_00: to this one uh mangled misshapen protein but they actually handle
[5:43] completely different misshapen protein caused by different
[5:46] genetic mutation in the eye,
[5:48] SPEAKER_00: causing retinitis pigmentosa,
[5:49] SPEAKER_00: a form of blindness,
[5:50] SPEAKER_00: familial blindness.
[5:52] SPEAKER_00: We are now studying familial Alzheimer's disease that's also,
[5:56] SPEAKER_00: you know,
[5:57] SPEAKER_00: involving these CARGO receptors.
[5:58] SPEAKER_00: And there are other mangled misshapen proteins in the liver,
[6:01] SPEAKER_00: in the lung that we're now studying.
[6:03] SPEAKER_00: So this becomes what I call a node,
[6:06] like a nodal mechanism that can be targeted for the benefit of
[6:11] many more patients
[6:12] SPEAKER_00: than we had previously thought possible,
[6:14] SPEAKER_00: which has been,
[6:14] SPEAKER_00: I think,
[6:14] SPEAKER_00: the most satisfying part about this story of molecular sleuthing.
[6:20] SPEAKER_01: Yeah, and it's pretty extraordinary.
[6:22] SPEAKER_01: We'll put the figure from your classic cell paper,
[6:26] SPEAKER_01: 2019,
[6:26] where you have a small molecule that targets the cargo receptor
[6:32] called T-Med9,
[6:33] SPEAKER_01: I guess.
[6:34] SPEAKER_01: And what's amazing about this,
[6:36] SPEAKER_01: of course,
[6:37] SPEAKER_01: is the potential to reverse this toxic protein disease.
[6:42] SPEAKER_01: And as you say, it may have applicability well beyond
[6:46] SPEAKER_01: of this MUC1 kidney story,
[6:49] but rather eye disease with retinosis pigmentosa and the
[6:54] familial Alzheimer's and
[6:55] SPEAKER_01: who knows what else.
[6:57] SPEAKER_01: And what's also fascinating about this is how,
[7:00] SPEAKER_01: as you said,
[7:01] SPEAKER_01: there were these limited number of families with the kidney disease,
[7:06] SPEAKER_01: and then you found another one,
[7:08] SPEAKER_01: Uromedulin,
[7:09] SPEAKER_01: I guess.
[7:10] SPEAKER_01: Yes.
[7:10] SPEAKER_01: So there's now...
[7:12] you know as you say thousands of families um that gets me to
[7:17] part of your sleuth
[7:18] work is not just you know hardcore science you started an
[7:22] entity called the ladders
[7:24] to cure accelerate tell us about that because this is really um
[7:31] pulling together
[7:32] all the forces which includes the patient advocacy groups and
[7:36] how we're going to
[7:37] SPEAKER_01: move forward like this
[7:39] SPEAKER_00: Absolutely.
[7:40] SPEAKER_00: I think,
[7:40] SPEAKER_00: you know,
[7:41] SPEAKER_00: the goal of the Ladders to Cures Accelerator,
[7:43] SPEAKER_00: which is a new initiative that we started at the Broad,
[7:47] but it really encompasses many colleagues across Boston and now
[7:50] increasingly it's
[7:51] SPEAKER_00: becoming sort of a national.
[7:53] We even have some international collaborations and it's only
[7:55] two years that it's
[7:56] SPEAKER_00: been in existence.
[7:57] SPEAKER_00: So we're certainly in a growth mode.
[7:59] But the inspiration was really some of this molecular sleuthing
[8:04] work where I basically thought
[8:06] SPEAKER_00: Well,
[8:06] SPEAKER_00: for starters,
[8:07] SPEAKER_00: it cannot be that there's only one molecular node,
[8:09] SPEAKER_00: these T-Med-Gargo receptors that we discovered.
[8:11] SPEAKER_00: There's got to be more, right?
[8:12] SPEAKER_00: And so there's a need,
[8:14] SPEAKER_00: you know,
[8:14] SPEAKER_00: to systematically go and find more nodes because obviously,
[8:18] SPEAKER_00: you know,
[8:18] SPEAKER_00: as anyone who works in rare genetic diseases will tell you,
[8:21] SPEAKER_00: The problem for all of us is that we do what I call hand-to-hand combat.
[8:25] Like we start with the disease with one mutation and we try to
[8:29] uncover the
[8:29] SPEAKER_00: mechanism and then try to develop therapies.
[8:32] SPEAKER_00: And that's wonderful, but of course it's slow, right?
[8:34] SPEAKER_00: And if we consider the fact that there are,
[8:37] SPEAKER_00: you know,
[8:37] SPEAKER_00: 30 million patients in the United States,
[8:40] SPEAKER_00: in every state,
[8:41] SPEAKER_00: everywhere in the country who suffer from a rare genetic disease,
[8:46] SPEAKER_00: most of them,
[8:46] SPEAKER_00: more than half of them are children.
[8:49] SPEAKER_00: then we can appreciate the magnitude of the problem.
[8:53] SPEAKER_00: Out of more than 8,000 genes that are involved in rare genetic diseases,
[8:58] SPEAKER_00: we barely have something that looks like a therapy for maybe 500 of them.
[9:04] So there's a huge mismatch in the unmet need and magnitude of
[9:08] the problem.
[9:09] So the Ladder Secures Accelerator is here to address this and
[9:13] to do this with the
[9:13] SPEAKER_00: most modern tools available.
[9:15] SPEAKER_00: And to your point,
[9:17] SPEAKER_00: Eric,
[9:17] SPEAKER_00: to bring patients along,
[9:19] SPEAKER_00: not just as the recipients of whatever we discover,
[9:23] SPEAKER_00: but also as partners in the research enterprise,
[9:25] SPEAKER_00: because it's really important to bring their perspectives and,
[9:28] SPEAKER_00: of course,
[9:28] SPEAKER_00: their partnerships in things like developing appropriate biomarkers,
[9:32] SPEAKER_00: for example,
[9:32] SPEAKER_00: for what we do down the road.
[9:35] SPEAKER_00: But from a fundamental scientific perspective,
[9:37] this is basically a project that aims to identify every
[9:42] opportunity for nodes
[9:44] SPEAKER_00: underlying all rare genetic diseases as quickly as possible.
[9:49] SPEAKER_00: And this was one of the reasons I was there at the AI for Science Forum,
[9:54] SPEAKER_00: because,
[9:55] SPEAKER_00: of course,
[9:56] SPEAKER_00: when one undertakes a project in which you're basically
[10:00] SPEAKER_00: This is what we're trying to do in the Ladder Secures Accelerator,
[10:02] introduce dozens of thousands of missense and nonsense human
[10:07] mutations that cause
[10:09] SPEAKER_00: genetic diseases,
[10:10] SPEAKER_00: simultaneously introduce them into multiple human cells,
[10:14] SPEAKER_00: and then use modern scalable technology tools,
[10:17] SPEAKER_00: things like...
[10:19] SPEAKER_00: CRISPR screens,
[10:20] SPEAKER_00: massively parallel CRISPR screens,
[10:22] SPEAKER_00: to try to interrogate all of these diseases in parallel,
[10:25] SPEAKER_00: identify the nodes,
[10:27] SPEAKER_00: and then develop,
[10:28] SPEAKER_00: of course,
[10:28] SPEAKER_00: therapeutic programs based on the discovery of these nodes.
[10:32] SPEAKER_00: This is a massive data generation project that is much needed.
[10:37] And in addition to the fact that it will help hopefully
[10:40] accelerate our approach to
[10:43] SPEAKER_00: all rare diseases,
[10:44] SPEAKER_00: genetic diseases,
[10:45] it is also a highly controlled cell perturbation data set that
[10:49] will require the
[10:50] SPEAKER_00: most modern tools in AI
[10:52] SPEAKER_00: not only to extract the data and understand the data of this data set,
[10:57] SPEAKER_00: but also because this,
[10:58] SPEAKER_00: again,
[10:59] SPEAKER_00: extremely controlled,
[11:00] SPEAKER_00: well-controlled cell perturbation data set can be used to train models,
[11:05] SPEAKER_00: train AI models,
[11:06] SPEAKER_00: so that in the future,
[11:08] SPEAKER_00: and I hope this doesn't sound too futuristic,
[11:09] SPEAKER_00: but I think that we're all aiming for that.
[11:11] Cell biologists,
[11:12] for sure,
[11:12] dream of this moment,
[11:14] I think,
[11:14] when we can actually have
[11:17] SPEAKER_00: in silico,
[11:18] the opportunity to make predictions about what cell behaviors
[11:22] are going to look
[11:22] SPEAKER_00: like based on a new perturbation that was not in the training set.
[11:26] SPEAKER_00: So an experiment that hasn't yet been done on a cell,
[11:29] SPEAKER_00: a perturbation that has not been made on a human cell,
[11:32] SPEAKER_00: what if,
[11:32] SPEAKER_00: like a new drug,
[11:33] SPEAKER_00: for example,
[11:34] SPEAKER_00: or a new kind of perturbation,
[11:36] SPEAKER_00: a new chemical perturbation,
[11:38] SPEAKER_00: how would it affect the behavior of the cell?
[11:39] SPEAKER_00: Can we make a predictive model for that?
[11:42] SPEAKER_00: This doesn't exist today,
[11:43] SPEAKER_00: but I think this is something,
[11:44] SPEAKER_00: you know,
[11:44] SPEAKER_00: the cell prediction model is a big...
[11:47] SPEAKER_00: question for biology for the future.
[11:49] And so I'm very energized by the opportunity to both address
[11:53] this problem of rare
[11:56] monogenic diseases that remains an unmet need and help as many
[11:59] patients as possible,
[12:01] SPEAKER_00: while at the same time advancing biology as much as we possibly can.
[12:05] So,
[12:06] you know,
[12:07] it's kind of like a win-win lifting all boats type of enterprise,
[12:10] hopefully.
[12:11] Yeah,
[12:12] well,
[12:12] there's many things to get to to unpack what you've just been reviewing.
[12:15] SPEAKER_01: So one thing for sure is that of these 8,000 monogenic diseases,
[12:21] SPEAKER_01: they have relevance to the polygenic common diseases,
[12:24] SPEAKER_01: of course.
[12:24] SPEAKER_01: Of course.
[12:25] SPEAKER_01: And then also the fact that the people,
[12:29] SPEAKER_01: the patient family advocates,
[12:32] SPEAKER_01: they are great at scurrying the world internet,
[12:35] SPEAKER_01: finding more people,
[12:37] SPEAKER_01: bringing together communities with each of these,
[12:40] SPEAKER_01: as you point out aptly.
[12:42] SPEAKER_01: these rare diseases cumulatively are a high,
[12:44] SPEAKER_01: very high proportion,
[12:45] SPEAKER_01: you know,
[12:46] SPEAKER_01: 10% of Americans or more.
[12:48] SPEAKER_01: So they're not so rare when you think about the overall.
[12:52] SPEAKER_00: Collectively.
[12:53] SPEAKER_01: Yeah.
[12:53] Now,
[12:55] and of course,
[12:55] as this toxic proteinopathies,
[12:58] there's at least 50 of these.
[13:00] SPEAKER_01: And,
[13:00] SPEAKER_01: you know,
[13:01] SPEAKER_01: the point that people have been thinking until now that,
[13:03] SPEAKER_01: oh,
[13:04] SPEAKER_01: we found the mangled protein.
[13:05] But what you've zeroed in on is that,
[13:08] hey,
[13:08] you know,
[13:08] it's not just a mangled protein.
[13:10] It's how it gets stuck in the cell and that it can't get to the
[13:14] lysozymes to get rid of it.
[13:15] SPEAKER_01: There's no waste system.
[13:17] SPEAKER_01: And so this is such fundamental work.
[13:20] Now,
[13:21] that gets me to the virtual cell story,
[13:24] kind of what you're getting into.
[13:27] SPEAKER_01: I just had a conversation with Charlotte Vaughn and Steve Quake,
[13:32] SPEAKER_01: who published a paper in December on the virtual cell.
[13:36] SPEAKER_01: And of course, that's...
[13:37] SPEAKER_01: many years off.
[13:39] SPEAKER_01: But of course,
[13:40] SPEAKER_01: it's a big,
[13:41] SPEAKER_01: bold,
[13:42] SPEAKER_01: ambitious project to be able to say,
[13:44] SPEAKER_01: as you just summarized,
[13:46] SPEAKER_01: if you had cells in silico and you could do perturbations in silico.
[13:52] SPEAKER_01: And of course,
[13:53] they were validated by actual experiments or bi-directionally
[13:58] that the experiments,
[13:59] SPEAKER_01: the real ones helped validate the virtual cell.
[14:02] SPEAKER_01: But then you could get
[14:04] A true acceleration of your understanding of cell biology,
[14:08] your field,
[14:09] of course.
[14:10] SPEAKER_01: So what you described, is it the same as a virtual cell?
[14:15] SPEAKER_01: Is it kind of a precursor to it?
[14:17] SPEAKER_01: How do you conceive this?
[14:18] SPEAKER_01: Because this is such a complex,
[14:20] SPEAKER_01: I mean,
[14:21] SPEAKER_01: it is the fundamental unit of life,
[14:24] SPEAKER_01: but it's also so much more complex than a protein or an RNA because it's
[14:30] SPEAKER_01: not only all the things inside the cell,
[14:33] SPEAKER_01: inside all these organelles and nucleus,
[14:35] SPEAKER_01: but then there's all the outside interaction.
[14:37] SPEAKER_01: So this is a bold challenge, right?
[14:41] SPEAKER_00: Oh,
[14:42] SPEAKER_00: my God,
[14:42] SPEAKER_00: it's absolutely,
[14:44] SPEAKER_00: you know,
[14:45] SPEAKER_00: from a biologist perspective,
[14:46] SPEAKER_00: it's the challenge of a generation,
[14:48] SPEAKER_00: for sure.
[14:49] SPEAKER_00: You know,
[14:50] SPEAKER_00: we think,
[14:50] SPEAKER_00: you know,
[14:52] SPEAKER_00: taking humans to Mars,
[14:53] SPEAKER_00: you know,
[14:53] SPEAKER_00: I mean,
[14:53] SPEAKER_00: that's an aspirational sort of big,
[14:55] SPEAKER_00: you know,
[14:57] SPEAKER_00: big,
[14:57] SPEAKER_00: ambitious goal.
[14:58] SPEAKER_00: I think this is the,
[14:59] SPEAKER_00: if you will,
[14:59] SPEAKER_00: the Mars shot for biology,
[15:02] SPEAKER_00: you know,
[15:02] SPEAKER_00: like being able to,
[15:04] SPEAKER_00: you know,
[15:05] SPEAKER_00: Whether you,
[15:06] SPEAKER_00: you know,
[15:06] SPEAKER_00: the terminology,
[15:07] SPEAKER_00: whether you call it a virtual cell,
[15:09] SPEAKER_00: I like the idea of sort of saying that this,
[15:13] SPEAKER_00: to state it as a problem,
[15:14] SPEAKER_00: you know,
[15:14] SPEAKER_00: the way that people who think about it from a mathematics perspective,
[15:18] SPEAKER_00: for example,
[15:19] SPEAKER_00: would think about it.
[15:19] I think stating it as the cell prediction problem appeals to me
[15:24] because it actually
[15:25] SPEAKER_00: forces us biologists to think about...
[15:28] SPEAKER_00: setting up the way that we would do these cell perturbation data sets,
[15:33] SPEAKER_00: the way we would generate them to set them up to serve predictions.
[15:38] SPEAKER_00: So,
[15:38] SPEAKER_00: for example,
[15:39] SPEAKER_00: you know,
[15:39] SPEAKER_00: the way that I would think about this would be,
[15:42] SPEAKER_00: you know,
[15:42] can I in the future have so much information about how cell
[15:47] perturbations work that
[15:49] I can train a model so that it can predict when I show it a
[15:52] picture of another cell?
[15:54] under different conditions that it hasn't seen before but it
[15:57] can still tell me ah
[15:59] this is a neuron in which you know you you know perturbed the
[16:03] mitochondria for
[16:04] example and now this is sort of the outcome that we that you
[16:07] would expect to see
[16:08] SPEAKER_00: right and so to be able to have this ability to have
[16:12] a model that can have the ability to predict in silico what
[16:16] cells would look like
[16:18] SPEAKER_00: after a perturbation.
[16:19] SPEAKER_00: I think that's sort of the way that I think about this problem.
[16:22] SPEAKER_00: It is very far away from anything that exists today.
[16:25] SPEAKER_00: But I think that the beginning starts,
[16:27] SPEAKER_00: and this is one of the unique things about my institute,
[16:30] SPEAKER_00: if I can say,
[16:31] SPEAKER_00: you know,
[16:32] SPEAKER_00: We have,
[16:34] SPEAKER_00: you know,
[16:34] SPEAKER_00: a place where,
[16:35] SPEAKER_00: you know,
[16:36] SPEAKER_00: cell biologists,
[16:37] SPEAKER_00: geneticists,
[16:38] SPEAKER_00: mathematicians,
[16:39] SPEAKER_00: machine learning experts,
[16:42] SPEAKER_00: we all come together,
[16:42] SPEAKER_00: you know,
[16:43] SPEAKER_00: in the same place to really think and grapple with these problems.
[16:47] SPEAKER_00: And of course,
[16:47] SPEAKER_00: we're very outward facing,
[16:48] SPEAKER_00: interacting with scientists all across the world as well.
[16:51] SPEAKER_00: But there's this sort of idea of bringing people into one institute where
[16:55] We can just think creatively about these big aspirational
[16:59] problems that we want to solve.
[17:00] I think this is one of the unique things about the ecosystem at
[17:03] the Broad Institute,
[17:03] SPEAKER_00: which I'm proud to be a part of.
[17:06] And it is this kind of out of the box thinking that will
[17:09] hopefully get us to
[17:12] generate the kinds of data sets that will serve the needs of
[17:15] building these kinds
[17:16] SPEAKER_00: of models with predictive capabilities down the road.
[17:20] SPEAKER_00: you know,
[17:20] SPEAKER_00: as you astutely said,
[17:21] SPEAKER_00: you know,
[17:22] SPEAKER_00: AlphaFold,
[17:24] SPEAKER_00: of course,
[17:24] SPEAKER_00: was based on the protein database existing,
[17:27] SPEAKER_00: right?
[17:27] SPEAKER_00: And that was
[17:29] a wealth of available information in which one could train
[17:31] models that would
[17:32] SPEAKER_00: ultimately be predictive,
[17:34] as we have seen this miracle that Demis Hassabis and John
[17:38] Jumper have given to humanity,
[17:40] SPEAKER_00: if you will.
[17:42] SPEAKER_00: But,
[17:42] SPEAKER_00: you know,
[17:43] SPEAKER_00: as Demis and John will also,
[17:45] SPEAKER_00: you know,
[17:47] SPEAKER_00: would also say,
[17:47] SPEAKER_00: I believe,
[17:48] SPEAKER_00: is,
[17:49] SPEAKER_00: as I have discussed with them,
[17:50] SPEAKER_00: in fact,
[17:51] SPEAKER_00: this self-prediction problem is really
[17:53] a bigger problem because we do not have a protein data bank to
[17:56] go to right now,
[17:57] SPEAKER_00: but we need to create it to generate these data.
[18:00] And so My Ladder Secures Accelerator is here to basically
[18:04] provide some part of the
[18:06] SPEAKER_00: answer to that problem,
[18:07] create this kind of well-controlled database that we need for
[18:10] cell perturbations,
[18:11] SPEAKER_00: while at the same time maximizing our learnings
[18:15] about these fully penetrant coding mutations and what their
[18:19] downstream sequelae
[18:20] SPEAKER_00: would be in many different human cells.
[18:23] SPEAKER_00: And so in this way,
[18:24] SPEAKER_00: I think we can both advance our knowledge about these monogenic diseases,
[18:29] SPEAKER_00: build models,
[18:30] SPEAKER_00: hopefully,
[18:31] SPEAKER_00: with predictive capabilities.
[18:32] And to your point,
[18:34] a lot of what we will learn about this biology,
[18:36] if we think that it involves
[18:38] SPEAKER_00: 8,000 or more out of the 20,000 genes in our genome,
[18:42] SPEAKER_00: it will,
[18:42] SPEAKER_00: of course,
[18:42] SPEAKER_00: serve our understanding of polygenic diseases ultimately as well.
[18:45] SPEAKER_00: You know,
[18:45] SPEAKER_00: as we,
[18:46] SPEAKER_00: you know,
[18:46] go deeper into this biology and we look at the combinatorial
[18:50] aspects of what,
[18:51] SPEAKER_00: you know,
[18:51] SPEAKER_00: different mutations do to human cells.
[18:53] SPEAKER_00: And so it's a huge aspirational problem for a whole generation,
[18:59] SPEAKER_00: but it's a good one to work on,
[19:00] SPEAKER_00: I would say.
[19:01] SPEAKER_01: Oh, absolutely.
[19:02] SPEAKER_01: Now, I think
[19:03] You already mentioned something that's quite well,
[19:06] two things from what you just touched on.
[19:09] SPEAKER_01: One,
[19:09] SPEAKER_01: of course,
[19:09] how vital it is to have this inner or transdisciplinary
[19:14] capability because you do
[19:16] SPEAKER_01: need expertise across these vital areas.
[19:18] SPEAKER_01: But the convergence,
[19:20] SPEAKER_01: I mean,
[19:20] I love your term nodal biology and the fact that there's these
[19:24] all these diseases
[19:25] SPEAKER_01: like you were talking about,
[19:27] SPEAKER_01: they do converge and nodal is a good term to to highlight that.
[19:31] SPEAKER_01: But
[19:32] SPEAKER_01: It's not,
[19:33] SPEAKER_01: of course,
[19:33] SPEAKER_01: as you mentioned,
[19:35] SPEAKER_01: we have genome editing,
[19:36] SPEAKER_01: which allows to look at lots of different genome perturbations,
[19:41] like the single letter change that you found in one pathogenic
[19:47] critical mutation.
[19:49] SPEAKER_01: There's also the AI world, which is blossoming like I've never seen.
[19:55] SPEAKER_01: In fact, I had a science this week about
[20:00] learning the language of life with AI and how there's been like
[20:04] 15 new foundation models,
[20:07] SPEAKER_01: DNA,
[20:08] SPEAKER_01: proteins,
[20:08] SPEAKER_01: RNA,
[20:09] SPEAKER_01: ligands,
[20:10] all their interactions and the beginning of the cell story too
[20:13] with the human.
[20:14] SPEAKER_01: So this is exploding.
[20:16] SPEAKER_01: And so as you said,
[20:17] the expertise in computer science and then this whole idea that
[20:23] you could take
[20:26] SPEAKER_01: these powerful tools
[20:28] SPEAKER_01: and do, as you said, which is the need to accelerate.
[20:31] We just can't sit around here when there's so much discovery
[20:35] work to be done with
[20:37] SPEAKER_01: the scalability.
[20:39] Even though it might take years to get to this artificial
[20:42] intelligence virtual cell,
[20:44] SPEAKER_01: which I have to agree,
[20:47] SPEAKER_01: everyone in biology would say that's the holy grail.
[20:49] SPEAKER_01: And as you remember,
[20:50] SPEAKER_01: at our conference in London,
[20:52] SPEAKER_01: Demis Hassabis said,
[20:53] SPEAKER_01: that's what we'd like to do now.
[20:55] SPEAKER_03: Really?
[20:57] SPEAKER_01: It has the attention of leaders in AI around the world,
[21:02] obviously in the science and the biomedical community like you
[21:05] and many others.
[21:06] So it is an extraordinary time where we just can't sit still
[21:12] with these tools that
[21:13] SPEAKER_01: we have,
[21:13] SPEAKER_01: right?
[21:14] SPEAKER_00: Sure.
[21:15] SPEAKER_00: Absolutely.
[21:16] SPEAKER_00: And I think this is going to be,
[21:17] SPEAKER_00: you know,
[21:18] SPEAKER_00: you mentioned the ACI presidency in the beginning of our call.
[21:21] SPEAKER_00: You know,
[21:21] SPEAKER_00: this is going to be,
[21:23] SPEAKER_00: you know,
[21:23] the president gets to to give an address at the annual meeting
[21:27] in Chicago.
[21:28] SPEAKER_00: This is going to be one of the points I make,
[21:30] SPEAKER_00: you know,
[21:30] SPEAKER_00: no matter what field in biomedicine we're in.
[21:33] SPEAKER_00: We live in,
[21:34] SPEAKER_00: I believe,
[21:35] SPEAKER_00: a golden era,
[21:36] and we have so many tools available to us that we can really
[21:40] accelerate our ability
[21:41] SPEAKER_00: to help more patients.
[21:42] SPEAKER_00: And, of course, this is our mandate.
[21:44] SPEAKER_00: You know,
[21:44] the most important stakeholders for everything that we do as
[21:47] physician scientists
[21:48] SPEAKER_00: are our patients,
[21:49] SPEAKER_00: ultimately.
[21:50] SPEAKER_00: So...
[21:51] I feel very hopeful for the future and our ability to use these
[21:55] tools and to really
[21:58] SPEAKER_00: make good on the promise of research is a public good.
[22:02] And I really hope that we can advance our knowledge for the
[22:05] benefit of all.
[22:06] SPEAKER_00: And this is really an exciting time,
[22:09] SPEAKER_00: I think,
[22:09] SPEAKER_00: to be in this field and hopefully for the younger colleagues,
[22:13] a time to really get excited about getting in there and getting
[22:17] involved and asking
[22:19] SPEAKER_00: the big questions.
[22:20] SPEAKER_00: Um,
[22:21] SPEAKER_01: so you,
[22:22] SPEAKER_01: you are the prudent type for this and,
[22:24] SPEAKER_00: you know,
[22:24] SPEAKER_01: an inspiration to everyone,
[22:26] SPEAKER_01: really.
[22:26] SPEAKER_01: And I'm sure to your,
[22:28] SPEAKER_01: uh,
[22:28] SPEAKER_01: your lab group,
[22:29] SPEAKER_01: which you highlighted,
[22:30] SPEAKER_01: you know,
[22:30] SPEAKER_01: in the Ted talk and many other things that you do.
[22:33] SPEAKER_01: Um, now I want to spend a little bit of time about your career.
[22:37] SPEAKER_01: Um,
[22:39] SPEAKER_01: I think it's fascinating that you grew up in Greece and your father's a
[22:43] SPEAKER_01: nephrologist and your mother's a pharmacologist.
[22:46] SPEAKER_01: Uh, so you had two physicians to model, but I guess you decided, um,
[22:50] SPEAKER_01: to go after nephrology,
[22:52] which is an area in medicine that I kind of liken it to Rodney Dangerfield.
[22:58] SPEAKER_01: It doesn't get any respect.
[22:59] SPEAKER_01: No, you don't see many people that go into nephrology.
[23:04] SPEAKER_01: But before we get to your decision to do that,
[23:07] SPEAKER_01: somehow or other,
[23:07] SPEAKER_01: you came from Greece to Harvard for your undergrad.
[23:11] SPEAKER_01: How did you make that happen?
[23:14] SPEAKER_01: connect to start your college education.
[23:20] SPEAKER_01: And then subsequently, of course, you stayed in Boston.
[23:22] SPEAKER_01: You never left Boston, I think.
[23:24] SPEAKER_00: I never left.
[23:25] SPEAKER_00: Yeah, this is coming into 31 years now in Boston.
[23:30] SPEAKER_00: Yeah, I started as a Harvard undergraduate and I'm now a full professor.
[23:33] SPEAKER_00: It's kind of a long but wonderful road.
[23:39] SPEAKER_00: Well, you know, I actually I would credit my parents.
[23:44] You mentioned that,
[23:45] you know,
[23:45] my father,
[23:45] they're both physician scientists.
[23:47] My father is now both retired,
[23:49] but my father is a nephrologist and my mother is a pathologist.
[23:52] SPEAKER_00: Actually, they were both academics.
[23:54] SPEAKER_00: And so when we were very young,
[23:56] SPEAKER_00: we lived in England when my parents were doing postdoctoral work.
[24:00] That was actually a wonderful gift that they gave me because I
[24:03] became bilingual.
[24:04] SPEAKER_00: It was a very young age.
[24:05] SPEAKER_00: And so that allowed me to have this advantage of being fluent in English.
[24:12] And then when we moved back to Greece,
[24:14] where I grew up,
[24:15] I went to an American school.
[24:17] SPEAKER_00: And from that time, this is actually an interesting story in itself.
[24:21] SPEAKER_00: I'm very proud of this school.
[24:22] SPEAKER_00: It's called Anatolia.
[24:24] And it was founded by American missionaries from Williams
[24:26] College a long time ago,
[24:29] SPEAKER_00: 150 and more years ago.
[24:32] SPEAKER_00: But it is in Thessaloniki, Greece, which is my hometown.
[24:36] SPEAKER_00: And it's a wonderful institution,
[24:37] SPEAKER_00: which gave me a lot of gifts as well,
[24:39] SPEAKER_00: preparing me for coming to college in the United States.
[24:44] SPEAKER_00: And,
[24:44] SPEAKER_00: you know,
[24:44] SPEAKER_00: of course,
[24:45] SPEAKER_00: I was a good student in high school,
[24:47] SPEAKER_00: but,
[24:47] SPEAKER_00: you know,
[24:48] what really was catalytic was that I was lucky enough to get a
[24:51] scholarship to go to Harvard.
[24:52] SPEAKER_00: And that was really the,
[24:54] SPEAKER_00: you know,
[24:55] SPEAKER_00: you could say the catalyst that propelled me from,
[24:58] SPEAKER_00: you know,
[24:58] SPEAKER_00: a teenager who was dreaming about a career as a physician scientist,
[25:01] SPEAKER_00: because I certainly was,
[25:03] SPEAKER_00: you know,
[25:03] SPEAKER_00: for as far back as I remember,
[25:04] SPEAKER_00: in fact.
[25:06] SPEAKER_00: But then,
[25:06] SPEAKER_00: you know,
[25:06] SPEAKER_00: to make that a reality,
[25:08] SPEAKER_00: you know,
[25:08] SPEAKER_00: I found myself on the Harvard campus initially for college.
[25:12] SPEAKER_00: And then I was in the combined Harvard-MIT program for my MD-PhD.
[25:17] SPEAKER_00: And,
[25:18] SPEAKER_00: you know,
[25:18] then I trained in Boston at Mass General in Brigham and then
[25:22] sort of started my
[25:23] SPEAKER_00: academic career.
[25:24] SPEAKER_00: And that sort of brings us to today.
[25:25] But it is an unlikely story and one that I feel still very
[25:31] lucky and blessed to
[25:32] SPEAKER_00: have had these opportunities.
[25:33] SPEAKER_00: So for sure, it's been...
[25:35] We're the ones lucky that you came here and set up shop and
[25:40] your productivity and
[25:43] SPEAKER_01: discovery work has been incredible.
[25:47] I do think it's interesting,
[25:49] too,
[25:49] because when you did your PhD,
[25:51] it was in neuroscience.
[25:53] SPEAKER_00: Ah, yes.
[25:53] SPEAKER_01: And then you switched gears.
[25:56] SPEAKER_01: So tell us about that.
[25:57] SPEAKER_00: This is interesting.
[25:58] SPEAKER_00: And actually, I encourage more colleagues to think about it this way.
[26:02] SPEAKER_00: So I have always been driven by the science.
[26:05] SPEAKER_00: And I think that it seems a little backward to some people,
[26:07] SPEAKER_00: but I did my PhD in neuroscience because I was interested in
[26:11] in understanding something about these ion channels that were
[26:14] newly discovered at
[26:15] SPEAKER_00: the time,
[26:16] SPEAKER_00: and they were most highly expressed in the brain.
[26:18] So here I was doing work in the brain in the neuroscience
[26:22] program at Harvard,
[26:24] But then once I completed my PhD and I was in the middle of my
[26:27] residency training,
[26:28] SPEAKER_00: actually,
[26:29] SPEAKER_00: at Mass General,
[26:29] I distinctly remember that there was a paper that came out that
[26:33] implicated the same
[26:34] family of ion channels that I had spent my time understanding
[26:37] in the brain.
[26:38] SPEAKER_00: It turned out to be a channelopathy that causes kidney disease.
[26:42] SPEAKER_03: Hmm.
[26:43] SPEAKER_00: So that was the light bulb.
[26:44] SPEAKER_00: And,
[26:45] SPEAKER_00: you know,
[26:45] SPEAKER_00: it made,
[26:46] SPEAKER_00: you know,
[26:46] it made me realize that maybe what I really wanted to do is
[26:49] just like follow this thread.
[26:50] And like my scientific curiosity basically led me into studying
[26:54] the kidney.
[26:55] SPEAKER_00: And then it seemed practical,
[26:56] SPEAKER_00: therefore,
[26:57] SPEAKER_00: to get done with my clinical training as efficiently as possible.
[27:00] SPEAKER_00: So I finished residency.
[27:02] SPEAKER_00: I did nephrology training.
[27:04] SPEAKER_00: And then there I was in the lab, like trying to understand the
[27:07] SPEAKER_00: you know, the biology around this channelopathy.
[27:10] And that sort of led us into,
[27:12] you know,
[27:12] the early projects in my young lab.
[27:15] SPEAKER_00: And in fact,
[27:15] SPEAKER_00: it's interesting,
[27:16] SPEAKER_00: we didn't talk about that work,
[27:17] but that work in itself actually has made it all the way to
[27:20] phase two trials in patients.
[27:23] SPEAKER_00: This was a paper we published in Science in 2017.
[27:28] SPEAKER_00: And in following to that work,
[27:29] SPEAKER_00: there was,
[27:30] SPEAKER_00: you know,
[27:31] SPEAKER_00: an opportunity to build this into a real drug,
[27:33] SPEAKER_00: you know,
[27:34] targeting one of these ion channels that has made it into phase
[27:37] two trials.
[27:37] SPEAKER_00: And, you know, we'll see what happens next.
[27:39] SPEAKER_00: But it's this idea of sort of...
[27:42] SPEAKER_00: following your scientific curiosity,
[27:44] SPEAKER_00: which I also talked about in my TED talk,
[27:46] SPEAKER_00: because you don't know to what wonderful places it will lead you.
[27:49] SPEAKER_00: And quite interestingly,
[27:50] now my lab is back into studying familial Alzheimer's and
[27:54] retinitis pigmentosa in
[27:56] SPEAKER_00: the eye and the brain.
[27:57] SPEAKER_00: So I tell people,
[27:59] Do not limit yourself to whatever someone says your field is or
[28:04] should be.
[28:06] SPEAKER_00: Just follow your scientific curiosity.
[28:08] SPEAKER_00: And usually that takes you a lot more interesting places.
[28:11] SPEAKER_00: And so that's certainly been a theme for my career, I would say.
[28:14] SPEAKER_01: Well, I think that's perfect.
[28:16] SPEAKER_01: Curiosity-driven science is not the term.
[28:19] SPEAKER_01: You often hear hypothesis-driven.
[28:21] SPEAKER_01: Or now with AI, you hear more AI exploratory science.
[28:25] SPEAKER_01: But no, that's great.
[28:29] SPEAKER_01: I want to get a little back to the AI story because it's so fascinating.
[28:34] SPEAKER_01: You use lots of different types of AI,
[28:39] SPEAKER_01: such as cellular imaging,
[28:42] SPEAKER_01: fusion models and drug discovery.
[28:45] SPEAKER_01: I mean, you've had drug discovery for different pathways.
[28:49] SPEAKER_01: You mentioned, of course, the ion channel.
[28:52] SPEAKER_01: And then also, as we touched on with your cell paper, the whole idea of
[28:57] of targeting the cargo receptor with a small molecule and then
[29:03] things in between.
[29:04] SPEAKER_01: So can you kind of,
[29:06] SPEAKER_01: you discussed this,
[29:07] SPEAKER_01: of course,
[29:08] SPEAKER_01: at the London panel,
[29:09] but maybe you just give us the skinny on the different ways
[29:12] that you incorporate AI
[29:14] SPEAKER_01: in the state of the art science that you're doing.
[29:17] SPEAKER_00: Sure.
[29:18] SPEAKER_00: Yeah, thank you.
[29:19] SPEAKER_00: I think there are many ways in which,
[29:21] SPEAKER_00: you know,
[29:21] SPEAKER_00: even for quite a long time,
[29:23] SPEAKER_00: before AI became such a,
[29:24] SPEAKER_00: you know,
[29:26] SPEAKER_00: well-known kind of household term,
[29:28] SPEAKER_00: if you will,
[29:28] the concept of machine learning in terms of image processing is
[29:31] something that has
[29:32] SPEAKER_00: been around for some time.
[29:34] And so this is actually a form of AI that we use in order to
[29:38] kind of process
[29:38] SPEAKER_00: millions of images.
[29:39] SPEAKER_00: You know,
[29:40] my lab has by now produced probably more than 20 million images
[29:43] over the last few years,
[29:45] SPEAKER_00: maybe five to six years.
[29:47] SPEAKER_00: And so if you can imagine,
[29:48] it's impossible for any human to process this many images and
[29:51] make sense of them.
[29:52] SPEAKER_00: So, of course, we've been using machine learning
[29:55] that is becoming increasingly more and more sophisticated and
[29:58] advanced in terms of
[29:59] SPEAKER_00: being able to do analysis of images,
[30:02] SPEAKER_00: which is a lot of what we cell biologists do,
[30:04] SPEAKER_00: of course,
[30:05] SPEAKER_00: you know.
[30:06] And so there's multiple different kinds of perturbations that
[30:08] we do to cells,
[30:09] SPEAKER_00: whether we're using
[30:10] SPEAKER_00: CRISPR or base editing to make,
[30:13] SPEAKER_00: you know,
[30:14] SPEAKER_00: for example,
[30:15] SPEAKER_00: genome wide or genome scale perturbations or small molecules,
[30:19] SPEAKER_00: as we have done as well in the past.
[30:21] These are all ways in which we are then using machine learning
[30:23] to read out the
[30:24] SPEAKER_00: effects in images of cells that we're looking at.
[30:29] SPEAKER_00: So that's one way in which machine learning is used in our daily work.
[30:34] SPEAKER_00: Of course,
[30:35] because we study misshapen mangled proteins and how they are
[30:39] recognized by these
[30:40] SPEAKER_00: cargo receptors,
[30:41] SPEAKER_00: we also use AlphaFold pretty much every day in my lab.
[30:45] SPEAKER_00: And this has been catalytic for us as a tool because we...
[30:49] SPEAKER_00: You know,
[30:50] we really are able to accelerate our discoveries in ways that
[30:52] were even just,
[30:54] SPEAKER_00: you know,
[30:54] SPEAKER_00: three or four years ago,
[30:55] SPEAKER_00: completely impossible.
[30:57] SPEAKER_00: So it's been incredible to to see how,
[31:00] SPEAKER_00: you know,
[31:00] the young people in my lab are just so excited to use these
[31:03] tools and they're
[31:03] SPEAKER_00: becoming extremely savvy,
[31:05] SPEAKER_00: you know,
[31:05] SPEAKER_00: in using these tools.
[31:06] SPEAKER_00: Of course, this is a new generation of scientists.
[31:10] SPEAKER_00: And so we use AlphaFold.
[31:12] SPEAKER_00: all the time.
[31:12] SPEAKER_00: And this also has a lot of implications,
[31:15] SPEAKER_00: of course,
[31:16] SPEAKER_00: for some of the interventions that we might think about.
[31:18] SPEAKER_00: So,
[31:18] SPEAKER_00: you know,
[31:19] SPEAKER_00: where in this,
[31:20] SPEAKER_00: you know,
[31:20] SPEAKER_00: cargo receptor complex that we study,
[31:22] SPEAKER_00: for example,
[31:23] might we be able to fit a drug that would disrupt the complex
[31:26] and lead the cargo
[31:27] SPEAKER_00: trucks into the lysosome for degradation,
[31:29] SPEAKER_00: for example.
[31:30] So,
[31:31] you know,
[31:31] there's many ways in which AI can be used for all of these functions.
[31:35] SPEAKER_00: So I would say that if we were to organize our thinking around it
[31:39] One way to think about the use of machine learning AI is around
[31:42] what I would call
[31:44] SPEAKER_00: understanding biology in cells and what,
[31:47] SPEAKER_00: you know,
[31:48] SPEAKER_00: in sort of more kind of drug discovery terms,
[31:50] SPEAKER_00: you would call target identification,
[31:53] like trying to understand the things that we might want to
[31:55] intervene on in order to
[31:57] SPEAKER_00: have a benefit for disease.
[31:59] SPEAKER_00: So target ID is one area in which I think,
[32:01] SPEAKER_00: you know,
[32:01] SPEAKER_00: machine learning and AI will have a catalytic effect as they already are.
[32:05] SPEAKER_00: The other,
[32:05] SPEAKER_00: of course,
[32:06] SPEAKER_00: is in the actual,
[32:07] SPEAKER_00: you know,
[32:08] SPEAKER_00: development of the appropriate drugs in a rational way.
[32:11] So like rational drug design is incredibly enabled by AlphaFold
[32:15] and all these
[32:16] advances in terms of understanding protein structures and how
[32:20] to fit drugs into
[32:21] SPEAKER_00: them of all different modalities and kinds.
[32:23] SPEAKER_00: And I think an area that we are not yet harnessing in my group,
[32:26] SPEAKER_00: but I think the Ladder Secures Accelerator hopes to build on,
[32:30] SPEAKER_00: is really patient data.
[32:31] I think that there's a lot of opportunity for AI to be used to
[32:35] make sense of our
[32:38] SPEAKER_00: medical records,
[32:38] SPEAKER_00: for example,
[32:39] and how we extract information that would tell us that this
[32:42] cohort of patients is a
[32:44] SPEAKER_00: better cohort to enroll in your trial versus another.
[32:46] SPEAKER_00: There are many ways in which we can make use of these tools.
[32:51] SPEAKER_00: Not all of them are there yet, but
[32:53] SPEAKER_00: I think it's an exciting time for being involved in this kind of work.
[32:58] SPEAKER_01: Oh, no question.
[33:00] SPEAKER_01: Now,
[33:00] it must be tough when you know the mechanism of these families'
[33:04] disease and you
[33:06] SPEAKER_01: even have a drug candidate,
[33:08] SPEAKER_01: but that it takes so long to go from that to helping these families and
[33:14] SPEAKER_01: What are your thoughts about that?
[33:15] I mean,
[33:16] are you thinking also about genome editing for these,
[33:19] some of these diseases?
[33:20] SPEAKER_01: Or are you thinking to go through the route of,
[33:23] SPEAKER_01: you know,
[33:23] SPEAKER_01: here's a small molecule,
[33:24] SPEAKER_01: here's the tox data in animal models,
[33:28] SPEAKER_01: and here's phase one and on and on?
[33:30] SPEAKER_01: Where do you think,
[33:31] SPEAKER_01: because when you know so much,
[33:33] SPEAKER_01: and then these people are suffering,
[33:36] SPEAKER_01: how do you bridge that gap?
[33:39] SPEAKER_00: Yeah, I think that's an excellent question.
[33:41] SPEAKER_00: Of course,
[33:42] having patients as our partners in our research is incredible
[33:48] as a way for us to
[33:49] SPEAKER_00: understand the disease,
[33:50] SPEAKER_00: to build biomarkers.
[33:51] SPEAKER_00: But it is also exactly creating this kind of emotional conflict,
[33:55] SPEAKER_00: if you will,
[33:56] SPEAKER_00: because,
[33:56] SPEAKER_00: of course,
[33:57] SPEAKER_00: you know,
[33:58] SPEAKER_00: to me,
[33:58] SPEAKER_00: honesty is the best policy,
[33:59] SPEAKER_00: if you will.
[34:00] SPEAKER_00: And so I'm always very honest with patients and their families.
[34:03] SPEAKER_00: I welcome them to the lab so they can see just how
[34:07] SPEAKER_00: you know, long it takes to, you know, get some of these things done.
[34:11] SPEAKER_00: Even today,
[34:11] SPEAKER_00: with all the tools that we have,
[34:13] SPEAKER_00: of course,
[34:13] SPEAKER_00: there are certain things that,
[34:14] SPEAKER_00: you know,
[34:15] SPEAKER_00: are still quite slow to do.
[34:17] SPEAKER_00: And,
[34:17] SPEAKER_00: you know,
[34:17] even if you have a perfect drug that looks like it fits into
[34:20] the right pocket,
[34:21] SPEAKER_00: there may still be some toxicity,
[34:22] SPEAKER_00: there may be other setbacks.
[34:24] And so I try to be very honest with patients about the road
[34:27] that we're on.
[34:28] SPEAKER_00: The small molecule path for the toxic proteinopathies is on its way now.
[34:34] It's partnered with a pharmaceutical company,
[34:35] so it's on its way,
[34:37] hopefully,
[34:37] to patients.
[34:39] SPEAKER_00: Of course, again, this is an unpredictable road.
[34:41] SPEAKER_00: Things can happen,
[34:42] SPEAKER_00: as you very well know,
[34:43] SPEAKER_00: but I'm at least glad that it's sort of making its way there.
[34:46] SPEAKER_00: But to your point,
[34:48] and I'm in an institute where CRISPR was discovered and base
[34:52] editing and prime
[34:52] SPEAKER_00: editing were discovered by my colleagues here.
[34:55] So we are,
[34:56] in fact,
[34:57] looking at every other modality that could help with these diseases.
[35:02] We have several hurdles to overcome because in contrast to the
[35:07] liver and the brain,
[35:10] SPEAKER_00: the kidney,
[35:10] SPEAKER_00: for example,
[35:11] SPEAKER_00: is not an organ in which you can easily deliver nucleic acid therapies.
[35:16] SPEAKER_00: But we're making progress.
[35:18] SPEAKER_00: I have a whole subgroup within the bigger group that's focusing on this.
[35:23] SPEAKER_00: It's actually...
[35:25] SPEAKER_00: you know,
[35:25] organized in a way where they're running kind of independently
[35:29] from the cell
[35:29] SPEAKER_00: biology group that I run.
[35:30] And it's headed by a person who came from industry so that she
[35:35] has the opportunity
[35:36] SPEAKER_00: to really drive the project the way that it would be run,
[35:38] SPEAKER_00: you know,
[35:39] SPEAKER_00: milestone driven,
[35:40] SPEAKER_00: if you will,
[35:40] SPEAKER_00: in a way that it would be run as a therapeutics program.
[35:44] And we're really trying to go after all kinds of different
[35:47] nucleic acid therapies
[35:49] that would target the mutations themselves rather than the
[35:51] CARGO receptors.
[35:53] SPEAKER_00: And so there's,
[35:54] SPEAKER_00: you know,
[35:55] SPEAKER_00: ASO,
[35:55] SPEAKER_00: siRNA technologies,
[35:57] SPEAKER_00: and then also actual gene editing technologies that we are investigating.
[36:01] SPEAKER_00: But I would say that, you know, some of them are closer than others.
[36:05] SPEAKER_00: And again,
[36:05] SPEAKER_00: to your question about patients,
[36:07] SPEAKER_00: I tell them honestly when a project looks to be more promising.
[36:13] And I also tell them when a project looks to have hurdles and
[36:16] that it will take
[36:16] long and that sometimes I just don't know how long it will take
[36:19] before we can get there.
[36:21] SPEAKER_00: So the only thing that I can promise patients,
[36:23] SPEAKER_00: in any of our projects,
[36:24] SPEAKER_00: whether it's Alzheimer's,
[36:26] SPEAKER_00: blindness,
[36:27] SPEAKER_00: kidney disease,
[36:30] all I can promise is that we're working the hardest we possibly
[36:32] can on the problem.
[36:35] SPEAKER_00: And I think that is often reassuring, I have found, to patients.
[36:40] And it's best to be honest about the fact that these things
[36:43] take a long time.
[36:45] SPEAKER_00: But I do think that they find it reassuring that someone is on it
[36:48] SPEAKER_00: essentially, and that there will be some progress as we move forward.
[36:52] SPEAKER_00: And we've made progress.
[36:54] SPEAKER_00: In the very first discovery that came out of my lab,
[36:56] SPEAKER_00: as I mentioned to you,
[36:57] SPEAKER_00: we've made it all the way to phase two trials.
[36:59] SPEAKER_00: So I have seen the trajectory be realized,
[37:04] and I'm eager to make it happen again and again as many times
[37:09] as I can within my
[37:10] SPEAKER_00: career to help as many people as possible.
[37:13] SPEAKER_01: I have no doubts that you'll be doing this many times in your career.
[37:17] SPEAKER_01: No, there's no question about it.
[37:19] SPEAKER_01: It's extraordinary, actually.
[37:22] SPEAKER_01: There's a couple of things there I want to pick up on.
[37:26] SPEAKER_01: Physician scientists, as you know, are a rarefied species.
[37:31] You have actually so nicely told the story about when you have
[37:40] a physician scientist,
[37:41] SPEAKER_01: you're caring for the patients that you're researching,
[37:44] SPEAKER_01: which is so most of the time we have scientists,
[37:47] SPEAKER_01: nothing wrong with them,
[37:48] SPEAKER_01: of course.
[37:49] SPEAKER_01: But you have this hinge point,
[37:51] SPEAKER_01: which is really important because you're really hearing the stories and
[37:55] SPEAKER_01: experiencing the patients.
[37:57] SPEAKER_01: And as you say,
[37:57] communicating about the likelihood of being able to come up
[38:00] with a treatment or the progress.
[38:05] SPEAKER_01: What are we going to do to get more physician scientists?
[38:07] SPEAKER_01: Because this is a this is a huge problem.
[38:10] SPEAKER_01: It has been for decades, but the numbers just keep going lower and lower.
[38:15] SPEAKER_00: I think you're absolutely right.
[38:17] SPEAKER_00: And this is,
[38:17] SPEAKER_00: again,
[38:18] SPEAKER_00: something that in my leadership of the ACI,
[38:22] SPEAKER_00: I have made sort of a cornerstone of our efforts.
[38:27] SPEAKER_00: I think that it has been well documented as a problem.
[38:31] I think that the pressures of modern clinical care are really
[38:35] antithetical to the
[38:36] SPEAKER_00: needs of research.
[38:38] protected time to really be able to think and be creative and
[38:42] even have the funding
[38:43] SPEAKER_00: available to be able to pursue one's program.
[38:47] I think those pressures are becoming so heavy for investigators
[38:54] that many of them
[38:55] SPEAKER_00: kind of choose one or the other route,
[38:57] SPEAKER_00: most often the clinical route,
[38:58] SPEAKER_00: because that tends to be,
[39:00] SPEAKER_00: of course,
[39:01] SPEAKER_00: where they can support their families better.
[39:03] SPEAKER_00: And so this has been...
[39:05] kind of the conundrum in some ways that we take our best and
[39:08] brightest medical
[39:10] SPEAKER_00: students who are interested in investigation,
[39:12] SPEAKER_00: we train them and invest in them in becoming physician scientists,
[39:16] SPEAKER_00: but then we sort of drop them at the most vulnerable time,
[39:19] which is usually after one completes their clinical and
[39:23] scientific training and
[39:24] SPEAKER_00: they're embarking on that early phases of one's careers.
[39:27] It has been found to be a very vulnerable point when a lot of
[39:30] people are now
[39:32] SPEAKER_00: you know,
[39:32] SPEAKER_00: in their mid-30s or even kind of late 30s,
[39:36] SPEAKER_00: perhaps with some family to take care of,
[39:38] SPEAKER_00: you know,
[39:39] SPEAKER_00: other burdens,
[39:40] SPEAKER_00: you know,
[39:41] SPEAKER_00: of sort of adulthood,
[39:43] SPEAKER_00: if you will.
[39:43] SPEAKER_00: And I think when it becomes very difficult to sustain a career where,
[39:48] SPEAKER_00: you know,
[39:49] SPEAKER_00: one salary is very limited due to the kind of the research component,
[39:52] SPEAKER_00: right?
[39:52] SPEAKER_00: And so I think we have to invest in our youngest, you know, people.
[39:57] SPEAKER_00: And it is...
[39:59] you know,
[39:59] a real issue that there's no good mechanism to do that at the
[40:03] present time.
[40:04] SPEAKER_00: So I was actually really hoping that there would be,
[40:08] SPEAKER_00: you know,
[40:08] SPEAKER_00: an opportunity with leadership at the NIH to really think about this.
[40:12] It's also been discussed at the level of the National Academy
[40:16] of Medicine,
[40:16] SPEAKER_00: where I had some role in,
[40:19] SPEAKER_00: you know,
[40:20] SPEAKER_00: discussing
[40:22] uh the recent report that they put out on the you know the
[40:24] biomedical enterprise in
[40:25] the united states and you know it's kind of interesting to see
[40:28] that there is a note
[40:30] made there about this issue and the fact that there needs to be
[40:32] i think more
[40:33] generous investment in the careers of a few select physician
[40:36] scientists that we can
[40:37] support so if you look at the numbers currently out of the
[40:40] entire physician
[40:41] workforce a physician scientist comprised less than one percent
[40:44] it's probably
[40:45] closer to 0.8 percent at this point and so that's really not
[40:49] enough i think to
[40:49] SPEAKER_00: maintain um
[40:51] SPEAKER_00: you know,
[40:51] SPEAKER_00: the enterprise and the kind of the,
[40:54] SPEAKER_00: if you will,
[40:55] SPEAKER_00: this incredible,
[40:57] SPEAKER_00: you know,
[40:57] SPEAKER_00: innovation economy that the United States has had,
[41:01] SPEAKER_00: you know,
[41:01] SPEAKER_00: this miracle engine,
[41:02] SPEAKER_00: if you will,
[41:03] SPEAKER_00: in biomedicine that has been fueled in large part
[41:07] SPEAKER_00: by physician investigators.
[41:08] SPEAKER_00: Of course,
[41:09] our colleagues who are non-physician investigators are equally
[41:12] important partners
[41:13] SPEAKER_00: in this journey,
[41:14] SPEAKER_00: but we do need a few of the physician scientists investigators,
[41:17] SPEAKER_00: I think,
[41:17] SPEAKER_00: as well.
[41:18] If you really think about the fact that I think 70% of people
[41:23] who run R&D programs
[41:25] SPEAKER_00: in all the big pharmaceutical companies are physician scientists,
[41:27] SPEAKER_00: right?
[41:28] SPEAKER_00: And so we need people like us to be able to work on these big problems.
[41:33] SPEAKER_00: And so
[41:34] SPEAKER_00: You know,
[41:35] SPEAKER_00: more investment,
[41:36] SPEAKER_00: you know,
[41:36] SPEAKER_00: I think that the government,
[41:37] SPEAKER_00: the NIH has a role to play there,
[41:38] SPEAKER_00: of course,
[41:39] SPEAKER_00: you know,
[41:39] SPEAKER_00: and this is important from both an economic perspective,
[41:42] a competition perspective with other nations around the world
[41:46] who are actually
[41:47] SPEAKER_00: heavily investing in the physician scientist workforce.
[41:51] And I think it's also important to do so through our kind of
[41:56] smaller scale efforts at the AACI.
[41:58] So one of the things that I have been involved in as a council
[42:02] member and now as
[42:03] president is the creation of an awards program for those early
[42:08] career investigators.
[42:09] SPEAKER_00: So we call them the Emerging Generation Awards Awards.
[42:12] SPEAKER_00: And we also have the Young Physician Scientist Awards.
[42:15] And these are really to recognize people who are making that
[42:17] transition from being
[42:19] kind of a trainee and a postdoc and have finished their
[42:23] clinical training into
[42:24] SPEAKER_00: becoming an independent assistant professor.
[42:27] SPEAKER_00: And so those are small awards,
[42:29] SPEAKER_00: but they're kind of a symbolic tap on the shoulder,
[42:32] SPEAKER_00: if you will,
[42:32] SPEAKER_00: that the AACI
[42:34] SPEAKER_00: sees you, you're talented, stay the course.
[42:37] SPEAKER_00: We want you to become a future member.
[42:40] SPEAKER_00: Don't give up.
[42:40] SPEAKER_00: You know, keep on fighting.
[42:43] SPEAKER_00: I think that can take us only so far.
[42:45] SPEAKER_00: I mean,
[42:45] SPEAKER_00: unless there's like a real investment,
[42:47] SPEAKER_00: of course,
[42:47] SPEAKER_00: you know,
[42:48] SPEAKER_00: still it will be hard to maintain people in the pipeline.
[42:51] SPEAKER_00: But this is just one way in which we have tried to,
[42:54] SPEAKER_00: you know,
[42:54] these programs that the ACI offers have been very successful
[42:58] over the last few years.
[43:00] We create a cohort of investigators who are clearly recognized
[43:03] by members of the
[43:05] SPEAKER_00: ACI as being promising young colleagues,
[43:07] and we give them longitudinal training as part of like a cohort
[43:11] where they learn
[43:12] SPEAKER_00: about how to write a grant,
[43:13] SPEAKER_00: how to write a paper,
[43:14] SPEAKER_00: leadership skills,
[43:15] SPEAKER_00: how to run a lab.
[43:16] SPEAKER_00: And there's sort of like a buddy system as well.
[43:19] So they know that they're in it together rather than feeling
[43:22] isolated and sort of
[43:23] SPEAKER_00: struggling to get their careers going.
[43:25] SPEAKER_00: And so we've seen a lot of success.
[43:28] SPEAKER_00: One way that we measure that is conversion into ACI membership.
[43:32] SPEAKER_00: And so, you know, we're encouraged by that.
[43:35] SPEAKER_00: And we hope that, you know, the program can continue.
[43:37] And of course,
[43:38] as president,
[43:39] I'm going to be fundraising for that as well.
[43:41] SPEAKER_00: It's part of the role.
[43:43] SPEAKER_00: But it is a really worthy cause,
[43:45] SPEAKER_00: because to your point,
[43:45] SPEAKER_00: we have to somehow make sure that our younger colleagues stay the course,
[43:50] SPEAKER_00: you know,
[43:50] SPEAKER_00: that we can at least maintain,
[43:52] SPEAKER_00: if not bolster our numbers within the work,
[43:55] SPEAKER_00: the scientific workforce.
[43:57] SPEAKER_01: Well, you outlined some really nice strategies and plans.
[44:01] SPEAKER_01: It's a formidable challenge,
[44:04] SPEAKER_01: of course,
[44:05] SPEAKER_01: and we'd like to see billions of dollars to support this.
[44:09] SPEAKER_01: And maybe someday we will,
[44:10] SPEAKER_01: because as you say,
[44:11] if we could relieve the financial concerns of people who have
[44:16] curiosity driven ideas,
[44:18] SPEAKER_01: Exactly.
[44:19] We could do a lot to replenish and build a big physician
[44:24] scientist workforce.
[44:25] Now,
[44:26] the last thing I want to get to is you have great communication skills.
[44:31] SPEAKER_01: Obviously,
[44:32] SPEAKER_01: anybody who is listening or watching this,
[44:36] SPEAKER_01: which is another really important part of being a scientist,
[44:39] SPEAKER_01: no less a physician or the hybrid of the two.
[44:43] SPEAKER_01: But I wanted to just go to the backstory because your TED Talk,
[44:48] SPEAKER_01: which has been watched by hundreds of thousands of people,
[44:50] SPEAKER_01: and I'm sure there's hundreds of thousands more that will watch it.
[44:54] But the TED organization is famous for making people come to
[45:00] the place a week ahead.
[45:02] This is like Vancouver,
[45:03] used to be in LA,
[45:05] Los Angeles area,
[45:07] and making them rehearse the talk.
[45:09] SPEAKER_01: rehearse, rehearse, rehearse.
[45:11] SPEAKER_01: And which seems crazy.
[45:12] SPEAKER_01: I mean, you could train the people there to how to give a talk.
[45:15] SPEAKER_01: Did you have to go through that?
[45:17] SPEAKER_01: Um,
[45:21] SPEAKER_00: Not really.
[45:21] I mean,
[45:22] I did rehearse once on stage before I actually delivered the
[45:26] talk live.
[45:29] SPEAKER_00: And I was very encouraged by the fact that,
[45:31] SPEAKER_00: you know,
[45:31] SPEAKER_00: the TED folks who are,
[45:32] SPEAKER_00: of course,
[45:32] SPEAKER_00: very well calibrated said,
[45:34] SPEAKER_00: just like that.
[45:35] SPEAKER_00: You know, it's great.
[45:36] SPEAKER_00: Just like that.
[45:38] SPEAKER_01: Because a lot of people that do these talks, they have to do it 10 times.
[45:42] SPEAKER_01: So that, Jenna, was another metric.
[45:44] But what I don't like about that is it just because these
[45:48] people almost have to
[45:49] SPEAKER_01: memorize their talks from having given it so much and all this coaching,
[45:53] SPEAKER_01: it comes across,
[45:54] SPEAKER_01: you know,
[45:54] SPEAKER_01: kind of stilted and unnatural and
[45:58] SPEAKER_01: You're just a natural great communicator added to all your other things.
[46:02] SPEAKER_00: Well, I think that it's interesting.
[46:04] SPEAKER_00: Actually,
[46:04] SPEAKER_00: I would say,
[46:05] SPEAKER_00: if I may,
[46:06] SPEAKER_00: that I credit,
[46:08] SPEAKER_00: of course,
[46:09] SPEAKER_00: you know,
[46:09] SPEAKER_00: I actually think that it's important to
[46:14] for us physician scientists,
[46:15] again,
[46:15] science and research is a public good.
[46:17] SPEAKER_00: And being able to communicate to the public what it is that we do,
[46:20] SPEAKER_00: I think,
[46:21] SPEAKER_00: is kind of an obligation for the fact that we are,
[46:23] SPEAKER_00: you know,
[46:24] SPEAKER_00: funded by the public to do this kind of work,
[46:26] SPEAKER_00: right?
[46:26] SPEAKER_00: And so I think that's important.
[46:27] And I always wanted to cultivate those communication skills for
[46:30] the benefit of
[46:31] SPEAKER_00: communicating simply and clearly what it is that we do in our labs.
[46:35] SPEAKER_00: But also, I would say, as part of my story, I mentioned that I
[46:39] SPEAKER_00: I had the opportunity to attend a special school growing up in Greece,
[46:43] SPEAKER_00: Anatolia,
[46:43] SPEAKER_00: which was an American school.
[46:46] One of the interesting things about that is that there was an
[46:48] oratory competition.
[46:51] SPEAKER_00: I got very early exposure entering that competition.
[46:55] SPEAKER_00: And if you won the first prize, it was in the...
[46:59] SPEAKER_00: kind of in the ancient Rome way, first among equals.
[47:02] SPEAKER_00: And so that was the prize.
[47:05] SPEAKER_00: And I was lucky to have this early exposure.
[47:08] SPEAKER_00: This is when I was 14,
[47:11] SPEAKER_00: 15,
[47:16] in front of an
[47:17] SPEAKER_00: audience and sort of compete with other kids who were doing the same.
[47:19] 16 years old that I was training to give these oratory speeches
[47:20] SPEAKER_00: I think these are just wonderful gifts that a school can give a student.
[47:25] that have stayed with me for life,
[47:27] you know,
[47:27] and I think that's a wonderful...
[47:30] SPEAKER_00: Uh,
[47:30] SPEAKER_00: yeah,
[47:30] SPEAKER_00: it's been just,
[47:31] SPEAKER_00: uh,
[47:32] SPEAKER_00: you know,
[47:32] SPEAKER_00: I,
[47:33] SPEAKER_00: I credit,
[47:33] SPEAKER_00: I credit that experience for a lot of my,
[47:36] SPEAKER_00: uh,
[47:36] SPEAKER_00: subsequent capabilities in this,
[47:38] SPEAKER_00: uh,
[47:39] SPEAKER_00: in this area.
[47:40] SPEAKER_01: Oh, that's fantastic.
[47:41] SPEAKER_01: Well, this has been such a enjoyable, uh, conversation.
[47:45] Uh,
[47:45] and I,
[47:46] did I miss anything that we need to bring up or do you think we
[47:49] have it covered?
[47:50] SPEAKER_00: Not at all.
[47:51] SPEAKER_00: No, this was wonderful.
[47:52] SPEAKER_00: And I thoroughly enjoyed it as well.
[47:54] SPEAKER_00: Um, I'm very honored, um,
[47:57] SPEAKER_00: seeing how many other incredible colleagues you've had on the show,
[48:00] SPEAKER_00: you know,
[48:01] SPEAKER_00: it's just a great honor to be a part of this.
[48:03] SPEAKER_00: So thank you for having me.
[48:05] Well,
[48:05] you really are such a great inspiration to all of us in the
[48:08] biomedical community.
[48:09] SPEAKER_01: And we'll be, you know, cheering for your continued success.
[48:13] SPEAKER_01: And thanks so much for joining today.
[48:16] SPEAKER_01: And I look forward to the next time we get a chance to visit.
[48:20] SPEAKER_00: Absolutely.
[48:20] SPEAKER_00: Thank you, Eric.

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