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[00:00:10.960 --> 00:00:13.760] Hey everyone, welcome to the Drive Podcast.
[00:00:13.760 --> 00:00:15.600] I'm your host, Peter Attia.
[00:00:15.600 --> 00:00:24.160] This podcast, my website, and my weekly newsletter all focus on the goal of translating the science of longevity into something accessible for everyone.
[00:00:24.160 --> 00:00:30.960] Our goal is to provide the best content in health and wellness, and we've established a great team of analysts to make this happen.
[00:00:30.960 --> 00:00:36.000] It is extremely important to me to provide all of this content without relying on paid ads.
[00:00:36.000 --> 00:00:46.480] To do this, our work is made entirely possible by our members, and in return, we offer exclusive member-only content and benefits above and beyond what is available for free.
[00:00:46.480 --> 00:00:54.480] If you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of the subscription.
[00:00:54.480 --> 00:01:03.200] If you want to learn more about the benefits of our premium membership, head over to peteratiamd.com forward slash subscribe.
[00:01:04.480 --> 00:01:07.840] My guest this week is Feng Sheng.
[00:01:07.840 --> 00:01:18.640] Feng is a professor of neuroscience at MIT as well as an investigator at the Howard Hughes Medical Institute and a core member of the Broad Institute of MIT and Harvard.
[00:01:18.640 --> 00:01:34.400] He earned his bachelor's degree in chemistry and physics from Harvard University, after which he went on to earn his PhD in chemical and biological engineering at Stanford University, where he worked with one of our previous podcast guests, Carl Deseroth, in developing the technique of optogenetics.
[00:01:34.400 --> 00:01:47.520] From there, he returned to Harvard as a research fellow before starting his own research lab and professorship at MIT in 2011, where he subsequently contributed mightily to the development of the CRISPR-Cas9 system for gene editing.
[00:01:47.520 --> 00:01:59.440] Feng has earned numerous honors and accolades for his work, including being selected for membership in the National Academy of Sciences, the National Academy of Medicine, and the American Academy of Arts and Sciences.
[00:01:59.440 --> 00:02:03.320] He has also a fellow of the National Academy of Inventors.
[00:02:03.320 --> 00:02:10.280] In this episode, we explore the origins of CRISPR and discuss Feng's early work in optogenetics at Stanford.
[00:02:10.280 --> 00:02:17.880] We discuss the foundations of gene editing, discussing the challenges and breakthroughs in the field and how CRISPR revolutionized the process.
[00:02:17.880 --> 00:02:30.760] We talk about the practical implications of CRISPR, such as its potential to treat genetic diseases, the importance of delivery methods, and the current success and limitations in targeting cells like those in the liver and eye.
[00:02:30.760 --> 00:02:36.680] We discuss the ethical considerations of gene editing, touching on the debate surrounding germline modification.
[00:02:36.680 --> 00:02:45.880] Finally, we reflect on Feng's personal journey, the significance of mentorship and education, and where he sees potential for the future of science and genetic medicine.
[00:02:45.880 --> 00:02:51.000] So without further delay, please enjoy my conversation with Feng Sheng.
[00:02:55.480 --> 00:02:59.400] Hey, Feng, thank you so much for detouring your trip and coming through Austin.
[00:02:59.400 --> 00:03:03.560] I've been really looking forward to sitting down with you for, frankly, about a year.
[00:03:03.560 --> 00:03:16.760] So this is a topic that I don't think there's anybody who's heard this podcast who hasn't heard the term CRISPR, but I think very few people can actually explain it and explain what a powerful tool it is.
[00:03:16.760 --> 00:03:26.360] But I do think that before we get there, it would be really helpful to kind of understand a little bit more about your journey on one hand, and then the journey of gene editing as a parallel.
[00:03:26.360 --> 00:03:27.960] Let's start with yours.
[00:03:27.960 --> 00:03:32.600] You and I overlapped a little bit because, I mean, not temporarily, but you were at Stanford.
[00:03:32.600 --> 00:03:35.160] Were you a postdoc in Carl Deseroth's lab?
[00:03:35.160 --> 00:03:37.880] Well, first of all, thank you for having me be on this podcast.
[00:03:37.880 --> 00:03:46.080] I've listened to your podcast on and off, especially when I'm running around or exercising, and it's always I learn a lot from listening to the podcast.
[00:03:46.320 --> 00:03:46.720] Awesome.
[00:03:46.720 --> 00:03:48.240] Thank you for having me here.
[00:03:48.240 --> 00:03:49.760] So, back to Stanford.
[00:03:44.600 --> 00:03:51.280] I was there as a graduate student.
[00:03:51.600 --> 00:03:55.600] I was in the lab of a researcher named Carl Dyseroth.
[00:03:55.600 --> 00:03:57.040] I was there for five years.
[00:03:57.040 --> 00:03:57.520] That's right.
[00:03:57.520 --> 00:03:59.520] You did your PhD there with Carl.
[00:03:59.520 --> 00:04:02.080] Now, as you know, Carl and I were classmates.
[00:04:02.080 --> 00:04:04.080] Carl's been on this podcast.
[00:04:04.080 --> 00:04:14.160] And so, maybe folks who either didn't listen to that podcast or who did but have forgotten, give us kind of a quick summary of the type of work that you and Carl did.
[00:04:14.160 --> 00:04:19.360] When I was working with Carl Dyseroth, we developed a technology called optogenetics.
[00:04:19.360 --> 00:04:30.240] And it's a way of studying brain cells in the brain, how they are connected together, and how they mediate memory, mediate different types of physiological function.
[00:04:30.240 --> 00:04:42.080] The way it works is that we took a gene from a green algae, and this is a gene that senses light and converts it into electrical current in a cell.
[00:04:42.080 --> 00:04:52.640] So, we can put this gene from the green algae right into the brain cells in a mouse, and we can shine blue light or yellow light and control the brain activity in these mice.
[00:04:52.640 --> 00:05:08.880] So, for example, if you wanted to study sleep, you can put this gene into different groups of cells in the brain and stimulate them, and you can find out which ones of these are controlling wakefulness or which ones are causing the mouse to become more sleepy.
[00:05:08.880 --> 00:05:31.400] So, if you do this systematically, one by one, from one type of cell to another type of cell, you can gradually start to put together a picture of how the brain is wired together, and then also what are the different components that govern all sorts of behaviors from sleep and wakefulness to thirst and hunger to memory and even to motivation and happiness.
[00:05:29.840 --> 00:05:34.040] So, it was really fun to be at Stanford and working with Carl.
[00:05:34.280 --> 00:05:38.120] To me, the thing that always stood out about the technique was just the resolution.
[00:05:38.120 --> 00:05:46.840] I don't know what a great analogy would be, but it was resolution at the level of the word rather than the page, if you were thinking about a book, for example.
[00:05:46.840 --> 00:05:47.480] Right.
[00:05:47.480 --> 00:05:53.080] What is incredible about these algal proteins is that they are very, very fast.
[00:05:53.080 --> 00:06:00.280] So, you can show the way the brain cells are able to signal to each other at the action potential level.
[00:06:00.280 --> 00:06:09.240] So, action potentials are these individual signals that they're basically like the phonemes of the speech that one neuron speaks with another neuron.
[00:06:09.240 --> 00:06:12.600] And you can control it at every single phoneme level.
[00:06:12.600 --> 00:06:14.360] And that is pretty cool.
[00:06:14.360 --> 00:06:22.440] And since we're going to be talking a lot about gene editing, what was the technique that you guys used to insert those algal genes into the brains?
[00:06:22.440 --> 00:06:27.240] The way that you would put a gene into the brain is usually by using a virus.
[00:06:27.240 --> 00:06:40.360] So, this is a virus that exists in nature, but we have engineered it by removing everything that is pathogenic about the virus and then replacing those pathogenic genes with the gene that we're trying to put into the brain.
[00:06:40.360 --> 00:06:43.480] So, in this case, it's the gene from the green algae.
[00:06:43.480 --> 00:06:55.480] And by injecting the virus into a brain area that you want to study, the virus will infect all the cells in that region and then make those cells begin to produce this algal protein.
[00:06:55.480 --> 00:07:00.200] So, once the neuron starts to carry this algal protein, it becomes light-sensitive.
[00:07:00.200 --> 00:07:03.960] So, you can turn blue light on it and be able to stimulate it.
[00:07:03.960 --> 00:07:05.320] Yeah, amazing.
[00:07:05.320 --> 00:07:07.320] What year did you finish your PhD?
[00:07:07.320 --> 00:07:08.760] In 2009.
[00:07:08.760 --> 00:07:12.520] Okay, and then you went to MIT or you went to the Broad?
[00:07:12.680 --> 00:07:13.640] Then I went to Harvard.
[00:07:13.640 --> 00:07:13.960] Okay.
[00:07:13.960 --> 00:07:18.640] I was there for about a year, and then I went to MIT and Broad right after that.
[00:07:14.920 --> 00:07:21.200] Okay, and then your attention there sort of turned.
[00:07:21.360 --> 00:07:25.680] You obviously worked some on optogenetics, but what else did you pivot into?
[00:07:26.000 --> 00:07:44.080] As I was working on optogenetics, and especially toward the end of graduate school, I began to realize that one of the biggest bottlenecks facing optogenetics is our ability to insert the algo gene into specific places in the genome.
[00:07:44.080 --> 00:07:53.200] And the reason for that is because in order for us to study different types of brain cells, we need to have very precise targeting of different types of brain cells.
[00:07:53.200 --> 00:07:54.800] Brain cells are not just one type.
[00:07:54.800 --> 00:07:56.160] Neurons is not a single type.
[00:07:56.160 --> 00:07:59.360] There are probably hundreds of different types of brain cells.
[00:07:59.360 --> 00:08:03.520] The way that they're defined is based on their molecular property.
[00:08:03.520 --> 00:08:10.320] So each brain cell, even though they all share the same genome, they have different sets of genes that are turned on.
[00:08:10.320 --> 00:08:17.680] That's why brain cells that control pain sensation versus brain cells that are involved in Parkinson's disease are different.
[00:08:17.680 --> 00:08:26.160] So the way that you would target one or another type of brain cell is by figuring out what are the molecular signatures of that cell.
[00:08:26.160 --> 00:08:38.160] So if you know that gene A is turned on in that brain cell and not in another type of brain cell, then you can insert this algo gene into the region that's controlling gene A.
[00:08:38.160 --> 00:08:42.400] That way it will only get turned on in the first type of neuron.
[00:08:42.400 --> 00:08:49.280] And the way to insert this gene into that precise place in the genome required gene editing.
[00:08:49.280 --> 00:08:51.840] And it was really hard to do at the time.
[00:08:51.840 --> 00:09:00.000] And so I thought maybe if I wanted to get optogenetics to become even more powerful and useful, we need to make gene editing more easy to use.
[00:09:00.840 --> 00:09:09.640] So by the time I went to Harvard, I began to focus more on trying to figure out how do you more easily be able to modify the genome.
[00:09:09.640 --> 00:09:19.080] Yeah, so this is to me where the story gets so interesting because you stumble across a problem that you're trying to solve because of your passion.
[00:09:19.080 --> 00:09:21.320] You've already alluded to why this is so difficult.
[00:09:21.320 --> 00:09:25.640] And I guess maybe just go back and explain something so that the listener understands.
[00:09:25.640 --> 00:09:39.880] Why was it easy to do what you did in Carl's lab, relatively speaking, where you're putting an entire gene into presumably an adenovirus and letting the adenovirus infect the neurons and stick a whole gene in?
[00:09:39.880 --> 00:09:44.360] Why is that a different problem than the one you just described that you started to solve at Harvard?
[00:09:44.360 --> 00:09:46.360] Make sure everybody understands that distinction.
[00:09:46.360 --> 00:09:53.640] The work that I was doing when I was a graduate student with Carl Dysroth is that we were simply trying to insert a gene into brain cells.
[00:09:53.640 --> 00:09:56.680] Meaning you don't get to care where it goes specifically.
[00:09:57.000 --> 00:10:02.360] We can get it into the rough area in the brain, but there are many different types of cells there.
[00:10:02.360 --> 00:10:07.400] And so we weren't as precise in our ability to target those cells.
[00:10:07.400 --> 00:10:19.160] And we also developed some tricks to be able to get it into a specific type of cell, but that was only limited to mice because we can genetically modify mice and it will take a long time.
[00:10:19.160 --> 00:10:27.720] It will take a year or two years to be able to make those mice available to engineer them, but it wasn't generally applicable.
[00:10:27.720 --> 00:10:41.240] And so especially if you think about how to turn optogenetics into a therapeutic to use in the human, we certainly couldn't go in and use those transgenic technologies to make it work in the human brain.
[00:10:41.240 --> 00:10:43.480] So, this was a major problem.
[00:10:43.800 --> 00:10:44.440] Okay.
[00:10:44.440 --> 00:10:54.240] So, let's keep that story on the side for a moment and let's tell in parallel another story, a story that started long before you were even in college, right?
[00:10:54.240 --> 00:10:55.520] When you were a young kid.
[00:10:55.520 --> 00:10:56.080] Right.
[00:10:56.080 --> 00:11:08.400] And it stems from an observation that grew out of a discovery in sequences that existed in bacterial DNA, a certain type of repeating structure that had all these interesting characteristics.
[00:11:08.400 --> 00:11:11.280] So, let's go back to the 80s and tell a little bit of that story.
[00:11:11.280 --> 00:11:19.040] And obviously, I wouldn't be going through this if it wasn't going to quickly converge with your life, change the direction of your life, but let's go back to the 80s.
[00:11:19.040 --> 00:11:33.120] I was born in the 80s, but what was really interesting that you're pointing out is that back in the 80s, there was a group of Japanese researchers who were just looking at DNA sequences of bacteria, and they were looking at E.
[00:11:33.120 --> 00:11:33.840] coli.
[00:11:33.840 --> 00:11:42.080] And what they found is that within some of the genomes, the DNA sequences of these bacteria, there are these regions that are very repetitive.
[00:11:42.080 --> 00:11:44.880] So it will just repeat over and over and over again.
[00:11:44.880 --> 00:11:50.560] Normally, genome sequences are not repetitive because they encode genes and different genes.
[00:11:50.560 --> 00:11:59.440] But here, they found that there are these repeat sequences that are all grouped together, so they're clustered, and they are not tandem repeats.
[00:11:59.440 --> 00:12:06.560] So, it's not repeat one next to each other, but they're interspaced by a short fixed-length gap.
[00:12:06.560 --> 00:12:13.600] And so it's basically A, B, A, C, A, D, A, E, A, G.
[00:12:13.920 --> 00:12:20.720] And so it just continues to repeat itself, but in this regularly spaced pattern.
[00:12:20.720 --> 00:12:24.560] And when they first found it, they had no idea what this sequence was all about.
[00:12:24.880 --> 00:12:33.560] And there's something else about those repeating segments that was quite interesting as well, which is in each direction, whichever way you read them, they were the same.
[00:12:33.560 --> 00:12:34.040] Right.
[00:12:34.040 --> 00:12:35.880] So they're called palindromes.
[00:12:35.880 --> 00:12:37.320] DNA is double-stranded.
[00:12:37.320 --> 00:12:39.560] So there's a top strand and there's a bottom strand.
[00:12:39.560 --> 00:12:42.360] And so this is why they look like a double helix.
[00:12:42.360 --> 00:12:43.960] And so they twist and turn.
[00:12:43.960 --> 00:12:51.400] What is interesting is that when you read these repeat sequences from the top and you read in the reverse way on the bottom, they're almost the same.
[00:12:51.400 --> 00:12:53.080] So they're a palindrome.
[00:12:53.080 --> 00:12:58.920] So you had these palindromic clustered repeats that were interspaced.
[00:12:58.920 --> 00:13:02.600] And if you say that in the right way, you get a few letters.
[00:13:02.600 --> 00:13:07.160] C R I S P R CRISPR.
[00:13:07.160 --> 00:13:12.920] So the name CRISPR, that term was actually coined nearly 40 years ago, right?
[00:13:12.920 --> 00:13:15.240] So CRISPR is really a brilliant acronym.
[00:13:15.240 --> 00:13:21.160] And so C-R-I-S-P-R stands for exactly how these repeats look.
[00:13:21.160 --> 00:13:27.640] Clustered, regularly, interspaced, short, palindromic, repeat.
[00:13:27.640 --> 00:13:30.920] So CRISPR is really brilliant and it's very catchy.
[00:13:30.920 --> 00:13:35.960] But this name wasn't the name that was given to these repeats back in the 80s.
[00:13:35.960 --> 00:13:38.280] In fact, it had many different names.
[00:13:38.280 --> 00:13:40.440] Did that name not come along until the 90s?
[00:13:40.440 --> 00:13:42.200] It didn't come until the early 2000s.
[00:13:42.200 --> 00:13:42.600] Oh, really?
[00:13:42.600 --> 00:13:42.920] Okay.
[00:13:42.920 --> 00:13:43.480] Yeah.
[00:13:43.480 --> 00:13:46.040] Before Francesco Mojito.
[00:13:46.200 --> 00:13:51.080] Mojica came up with that name, but I mean, early 20s, 2000s, yeah.
[00:13:51.080 --> 00:14:01.880] Now, if my reading of the history is accurate, understandably, scientists in the 80s and 90s focused on the repeating segments.
[00:14:01.880 --> 00:14:06.600] Obviously, any good scientist, I think, would look at that and realize this is a very interesting observation.
[00:14:06.600 --> 00:14:08.040] How do we figure out what it is?
[00:14:08.040 --> 00:14:16.400] But the focus was on the repeating segments, what you described as the A in the A, B, A, C, A, D.
[00:14:16.720 --> 00:14:28.640] And I believe it was Francesco who was the first to observe that actually what's interesting is not the repeating segments, it's the seemingly uninteresting segments in between them that are different.
[00:14:28.960 --> 00:14:30.000] Exactly.
[00:14:30.000 --> 00:14:35.520] So, these CRISPR repeats, they have this conserved A sequence.
[00:14:35.520 --> 00:14:38.160] And this A, of course, repeats many, many times.
[00:14:38.160 --> 00:14:40.000] So, it's the most obvious thing.
[00:14:40.000 --> 00:14:44.320] And usually, when we are looking at things, we look for things that have the strongest signal.
[00:14:44.320 --> 00:14:50.080] So, when you have 20, 30 repeats of the same sequence, that's the strongest signal there is.
[00:14:50.080 --> 00:14:53.040] But it turns out that is not an interesting part.
[00:14:53.040 --> 00:15:00.080] The interesting part is actually the non-repeating sequences that's interspaced between pairs of these repeats.
[00:15:00.080 --> 00:15:03.520] So, for Francisco Mojica, he's a Spanish researcher.
[00:15:03.520 --> 00:15:08.720] He's been looking at bacteria and looking at sort of weird sequences for a long time.
[00:15:08.720 --> 00:15:20.240] And what he did back in the early 2000s is that he took these nine-repeat sequences and he just searched against viruses in the bacterial world.
[00:15:20.240 --> 00:15:24.480] A few of them he found matched virus sequences.
[00:15:24.480 --> 00:15:34.000] And so, that was really a breakthrough because it started to highlight that maybe these non-repeating sequences are foreign to the bacteria.
[00:15:34.000 --> 00:15:35.920] They came from somewhere else.
[00:15:35.920 --> 00:15:40.480] And somehow, bacteria acquire them into these repeat patterns.
[00:15:40.800 --> 00:15:56.320] And that really started to launch the CRISPR revolution because that observation and that inference allowed people to start to realize that maybe this has something to do with how bacteria and the viruses are interacting with each other.
[00:15:56.320 --> 00:16:06.600] Yeah, what's interesting when you again look at that story is that when he tried to publish that finding, it was rejected by virtually every significant journal out there.
[00:16:06.600 --> 00:16:10.040] They viewed this as either incorrect, uninteresting, whatever.
[00:16:10.040 --> 00:16:17.480] It was ultimately published, although I don't recall the name of the journal that first published that finding, but it was many rungs below nature science, etc.
[00:16:17.800 --> 00:16:20.120] The irony of science sometimes.
[00:16:20.120 --> 00:16:29.000] Maybe folks, we can remind them just how the human immune system works, because just like bacteria, we are also encountering viruses all the time.
[00:16:29.000 --> 00:16:33.240] Viruses do nothing good for us, just as they do nothing good for bacteria.
[00:16:33.240 --> 00:16:35.880] We have a pretty negative relationship with viruses.
[00:16:35.880 --> 00:16:40.840] All they want to do is use us as hosts to replicate their genomic material, and in the process, they tend to make us sick.
[00:16:40.840 --> 00:16:47.800] So obviously, when a virus infects a bacteria, it's just using its genetic machinery to replicate, and it's going to kill the bacteria.
[00:16:47.800 --> 00:16:50.600] So, as you said, the bacteria needs a tool to fight back.
[00:16:50.600 --> 00:16:57.320] Now, we do it through the creation of antibodies, but how did this story continue to unfold?
[00:16:57.320 --> 00:16:59.400] What were the bacteria doing?
[00:16:59.400 --> 00:17:08.280] Or more to the point, what did Francesco realize was happening with this artifact left behind of viral DNA?
[00:17:08.280 --> 00:17:15.080] In the early days of CRISPR research, there were actually several different converging lines of work.
[00:17:15.080 --> 00:17:21.640] So, there's Francisco Mojica, who's looking at these repeat sequences within the bacterial genome.
[00:17:21.640 --> 00:17:27.240] But then there were also other researchers who began to zero in on a group of genes.
[00:17:27.240 --> 00:17:31.960] So, these are things that are telling the bacteria to make certain types of protein.
[00:17:31.960 --> 00:17:35.400] There are these certain genes that are right next to the repeats.
[00:17:35.400 --> 00:17:43.880] And it took a while for people to begin to associate the genes and the repeat to be together as one single system.
[00:17:43.880 --> 00:17:50.080] But the people who are studying the genes realized that these genes were carrying nucleases.
[00:17:50.080 --> 00:17:56.400] So nucleases are proteins that usually go and cut up either DNA or RNA.
[00:17:56.400 --> 00:18:01.360] And so they initially thought that maybe these genes were involved in DNA repair.
[00:18:01.360 --> 00:18:12.560] So for example, Eugene Kunan, who is a really brilliant bioinformatician, he's been studying these genes and he really started to zero in on what biologically these genes may be doing.
[00:18:12.560 --> 00:18:18.000] But the linkage with these repeats took a little while longer to get associated.
[00:18:18.000 --> 00:18:48.320] But it was really when the discovery that there are these viral sequences in the repeats and that there are these genes that are associated with the repeat that are involved in, say, DNA cleavage, that started to really put together a framework for thinking that maybe this is a system where these viral sequences are working together with the DNA cleaving proteins to go and recognize viral sequences and try to cleave viral sequences.
[00:18:48.640 --> 00:18:53.360] And we can put some names on these things so that people can start to see where the thing is going.
[00:18:53.360 --> 00:19:08.080] So you talked about these genes that were near but at a distance from both the palindromic repeats and the inner spaced segments that we now realize were copies of viral DNA.
[00:19:08.080 --> 00:19:16.000] And as you said, they coded for proteins or enzymes called nucleases, which cut DNA.
[00:19:16.000 --> 00:19:18.160] And similarly, we have helicases.
[00:19:18.160 --> 00:19:22.160] So you have certain enzymes that can unwind the DNA so that they can go in and cut.
[00:19:22.160 --> 00:19:25.840] And these were referred to as CRISPR-associated proteins, correct?
[00:19:25.840 --> 00:19:28.560] Which is abbreviated as CAS.
[00:19:28.880 --> 00:19:33.800] So was CAS1 and CAS2 the first that were identified in that regard?
[00:19:34.120 --> 00:19:35.160] That's exactly right.
[00:19:35.160 --> 00:19:40.200] So these genes that are right next to the CRISPR repeats, they're called CAS proteins.
[00:19:40.200 --> 00:19:45.960] Although they probably underwent many, many different renaming over the course of two decades.
[00:19:45.960 --> 00:19:57.640] But eventually, the community researchers who were studying CRISPR proteins and CRISPR RNA came together and started to really curate these different genes.
[00:19:57.640 --> 00:20:07.640] And so Cas1, Cas2, Cas3, CAS4, et cetera, these are things that are kind of numbered based on, in part, the order that they were discovered.
[00:20:07.640 --> 00:20:12.760] So the most popular protein or the most widely used protein now is called Cas9.
[00:20:12.760 --> 00:20:18.920] And this is one of the Cas proteins that are found among an array of many, many different CRISPR proteins.
[00:20:18.920 --> 00:20:22.600] So yeah, so these CAS proteins work with the CRISPR RNA.
[00:20:22.600 --> 00:20:30.440] And CRISPR-RNA refers to these repeats, which are encoded in DNA, but they are made by bacteria into RNA.
[00:20:30.440 --> 00:20:33.160] And those RNA are called CRISPR RNA.
[00:20:33.160 --> 00:20:34.840] They don't encode protein.
[00:20:34.840 --> 00:20:43.880] They simply are a short guide sequence that directs the CAS protein to find the target virus sequence.
[00:20:43.880 --> 00:20:51.480] So CAS protein, CRISPR-RNA, they together form a complex that goes and provides a defense function for the bacteria.
[00:20:51.800 --> 00:21:11.320] And whereas our defense against a virus is going to be making an antibody and/or activating another type of immune cell with an antigen receptor on it called a T cell, the defense of the bacteria is simply to cut the genetic material of the virus to kill the virus.
[00:21:11.320 --> 00:21:14.200] Obviously, a much more directed approach.
[00:21:14.200 --> 00:21:28.960] Let's walk through two scenarios: first infection, re-infection, and explain the difference between how the bacteria defends itself with special attention to the use of the CRISPR system and the Cas9 enzyme.
[00:21:28.960 --> 00:21:30.800] First infection now, out of the gate.
[00:21:30.800 --> 00:21:31.440] You're an E.
[00:21:31.440 --> 00:21:32.160] coli.
[00:21:32.160 --> 00:21:33.440] I'm a bacteriophage.
[00:21:33.440 --> 00:21:34.880] You've never seen me before.
[00:21:34.880 --> 00:21:35.680] I come along.
[00:21:35.680 --> 00:21:39.760] I've just injected my viral DNA into you.
[00:21:39.760 --> 00:21:42.480] So CRISPR is an adaptive immune system.
[00:21:42.480 --> 00:21:50.800] So it means this system can evolve with the bacteria to be able to accommodate many, many virus infections.
[00:21:50.800 --> 00:21:59.760] So when the virus, which is called a bacteriophage, first infects the bacteria, it will inject its genetic information into the bacteria.
[00:21:59.760 --> 00:22:03.280] The virus is usually very powerful and very potent.
[00:22:03.280 --> 00:22:07.120] It will probably wipe out most of the bacterial population.
[00:22:07.120 --> 00:22:23.840] But for a very small number of cells, maybe one out of a million, the CRISPR system will successfully recognize a piece of the DNA of that virus and begin to insert it into this repeat area in the CRISPR system.
[00:22:23.840 --> 00:22:25.600] And how long a piece is that typically?
[00:22:25.600 --> 00:22:27.280] How many nucleotides in that piece?
[00:22:27.280 --> 00:22:34.400] It's usually 30 letters long, and that is enough for the bacteria to uniquely recognize the virus.
[00:22:34.400 --> 00:22:37.760] So during the first infection, most bacteria die.
[00:22:37.920 --> 00:22:44.400] Very, very small number of them begin to acquire a snippet of the genetic information of this virus.
[00:22:44.400 --> 00:22:46.640] And it inserts it into the CRISPR system.
[00:22:46.640 --> 00:22:52.480] So those bacteria that survived have now acquired immunity against these viruses.
[00:22:52.800 --> 00:23:00.000] And in the process of surviving, what do they have to rapidly do to fend off that first viral infection?
[00:23:00.440 --> 00:23:04.200] So the bacteria has many different defense systems in addition to CRISPR.
[00:23:04.200 --> 00:23:08.040] So in fact, CRISPR is not the first line of defense.
[00:23:08.040 --> 00:23:14.280] There are other things that are also very powerful technologies now that are called restriction endonucleases.
[00:23:14.280 --> 00:23:17.800] These are proteins or enzymes that bacteria use.
[00:23:17.800 --> 00:23:23.480] They don't adapt, so they don't evolve, but they recognize fixed letter sequences.
[00:23:23.480 --> 00:23:28.360] And sometimes these will always get activated first and try to fend off the virus.
[00:23:28.360 --> 00:23:32.360] But if it doesn't, then there are a host of other defense systems.
[00:23:32.360 --> 00:23:34.680] One of these will eventually work.
[00:23:34.680 --> 00:23:41.080] But unfortunately for the bacterial population, these things don't come in quick enough, and that's why most of the cells die.
[00:23:41.080 --> 00:23:49.240] But the few where these things were able to keep up with the virus, that's what allows the CRISPR systems to begin to acquire the genetic information.
[00:23:49.240 --> 00:23:51.720] So now let's talk about that subsequent infection.
[00:23:51.720 --> 00:23:56.840] So we've obviously, through a very Darwinian mechanism, selected a subset of the E.
[00:23:56.840 --> 00:24:08.200] coli in this case that indeed are able to not just survive with their first and second line defense against the bacteriophage, but now they've also developed the memory, so to speak, the way we would use the term.
[00:24:08.200 --> 00:24:25.080] So now it's a month later, the same phage comes along, infects you, inserts its genetic material, but now you actually have interspersed between your CRISPR repeats, you actually have the 30 nucleotides that match a sequence within that virus.
[00:24:25.080 --> 00:24:29.000] So now, how do you spring into action to resist this infection?
[00:24:29.000 --> 00:24:35.080] So after first infection, in a way, the bacteria has been vaccinated against this virus.
[00:24:35.080 --> 00:24:41.160] So the second time when this virus comes around, it will inject its genetic information into the bacteria.
[00:24:41.160 --> 00:24:47.440] But now the bacteria in the CRISPR repeat area has a signature of this virus.
[00:24:47.760 --> 00:25:01.120] So the repeat area will get turned on and it will start to make CRISPR RNA that carry a 30-base pair long or 20-base pair long guide that's able to recognize the incoming virus.
[00:25:01.120 --> 00:25:05.040] So the Cas protein will bind to this CRISPR RNA.
[00:25:05.040 --> 00:25:10.560] They will go and try to search along all the DNA sequences in the bacteria.
[00:25:10.560 --> 00:25:17.920] When it finds a match in the virus's DNA, it will activate the nuclease and it will cut the DNA.
[00:25:17.920 --> 00:25:21.280] And approximately how many base pairs are in viral DNA?
[00:25:21.280 --> 00:25:22.400] Depends on the virus.
[00:25:22.400 --> 00:25:28.160] They can be small, maybe 10,000 letters, or they can be long, 100,000 or even longer.
[00:25:28.160 --> 00:25:32.400] Which, again, just for context, so people understand, we have about 3 billion.
[00:25:32.400 --> 00:25:36.640] So we're still talking about tiny, tiny, tiny amounts of DNA.
[00:25:36.640 --> 00:25:40.560] And by the way, does it differ if it's an RNA virus versus a DNA virus?
[00:25:40.560 --> 00:25:41.920] Is the process identical?
[00:25:41.920 --> 00:25:43.440] It's very similar.
[00:25:43.760 --> 00:25:58.880] So the re-infection, they tend to make pretty quick work because now, once the bacteria is able to make the Cas protein, which it can make really quickly, in between the CRISPR segment, it makes the RNA segment to guide it and match it.
[00:25:58.880 --> 00:26:06.400] And really, that's kind of a CRISPR RNA plus a truncated other version of an RNA that holds the RNA in the Cas protein.
[00:26:06.560 --> 00:26:08.640] We call that whole thing the guide RNA.
[00:26:08.640 --> 00:26:23.760] How quickly can that Cas9 enzyme holding the guide RNA, how quickly in actual time, does it go through the entire sequence of viral DNA until it finds its place to land and cleave?
[00:26:23.760 --> 00:26:25.360] That process is probably pretty fast.
[00:26:25.360 --> 00:26:36.840] I don't know exactly how quick it is, but also it's important to recognize that in a single bacteria, there are many, many copies of the Cas9 along with the guide RNA.
[00:26:37.160 --> 00:26:47.480] And so that means once the virus comes in, there are many, many copies of Cas9 that are simultaneously in parallel searching against the virus DNA to see whether or not there's a match.
[00:26:47.480 --> 00:26:58.200] And this is why the system is so powerful, because it's able to very quickly, in a parallel fashion, find the match and then inactivate the virus within minutes.
[00:26:58.200 --> 00:27:04.280] And there's something else that is pretty unique about where those cuts take place.
[00:27:04.280 --> 00:27:11.080] When we bring in the viral memory, it always begins with three particular nucleotides.
[00:27:11.080 --> 00:27:12.680] What's the significance of that?
[00:27:12.680 --> 00:27:13.400] Right.
[00:27:13.400 --> 00:27:21.400] So what you're talking about is what CRISPR scientists call PEM sequence, PAM, or proto-spacer JSON motif.
[00:27:21.560 --> 00:27:22.680] Just a jargon.
[00:27:22.680 --> 00:27:32.680] What is significant about that is that it is a sequence that is only found in the bacterial virus's genome, but not in the bacteria's genome.
[00:27:32.680 --> 00:27:43.800] When the bacteria acquires a piece of the virus's sequence and sticks it into its own genome, one question is: couldn't the CRISPR system go and recognize the bacteria's genome?
[00:27:43.800 --> 00:27:49.400] It's sort of like, couldn't you turn the weapon on yourself and cut your own DNA and kill yourself?
[00:27:49.400 --> 00:27:50.200] Exactly.
[00:27:50.200 --> 00:27:55.720] Yeah, so how does it avoid this self-targeting or autoimmunity against itself?
[00:27:55.720 --> 00:27:57.720] And this is where the PAM sequence comes in.
[00:27:57.720 --> 00:28:09.560] The PAM sequence is in the viral genome, is right next to the sequence that is acquired into the CRISPR system, but it itself is not acquired into the bacterial genome.
[00:28:09.560 --> 00:28:16.720] And so, what you see in the bacterial genomes, CRISPR repeat, is just the recognition sequence, but no PIM.
[00:28:14.200 --> 00:28:21.120] And so, Cas9 requires the PIM to activate recognition and cleavage.
[00:28:21.440 --> 00:28:26.160] And so, without a PEM, it doesn't cleave itself, but it's still able to target a virus.
[00:28:26.480 --> 00:28:40.880] So, Hang, does this bring us more or less up to speed with the state of the art when you turned your attention to how I can create a finer resolution for gene editing for my optogenetics problem?
[00:28:40.880 --> 00:28:43.280] Is this about where the state of the art was?
[00:28:43.280 --> 00:28:49.200] Yeah, so I started to work at MIT and the Broad Institute in 2011.
[00:28:49.200 --> 00:28:55.040] And so, when I first started, I went to a scientific presentation and they were talking about CRISPR.
[00:28:55.040 --> 00:28:58.000] And they mentioned that CRISPRs are nucleases.
[00:28:58.000 --> 00:29:04.400] Because I was thinking about nucleases and gene editing at the time, when I heard that word, it just got me interested.
[00:29:04.400 --> 00:29:08.640] And so, I went on to Wikipedia and looked at what CRISPR is.
[00:29:08.640 --> 00:29:17.920] And Francis Mohica and Simon Moiniu and Rodal Berengu, they had just published the very early studies on the Cas9 system.
[00:29:17.920 --> 00:29:20.080] Back then, it was still called Cas5.
[00:29:20.080 --> 00:29:22.800] It was only later on renamed to be Cas9.
[00:29:22.800 --> 00:29:27.680] But there were all these papers that if you read them, there aren't too many of them.
[00:29:27.680 --> 00:29:35.520] You can piece together the information and you can get a sense that this is an RNA-guided DNA targeting and cleaving enzyme.
[00:29:35.520 --> 00:29:44.800] And at the time, there were other gene editing systems that were being worked on by researchers in the field, something called zinc finger nuclease or talon.
[00:29:44.800 --> 00:29:50.720] And these are systems that use proteins to recognize DNA, but not using RNA to recognize DNA.
[00:29:51.040 --> 00:29:57.040] Let's talk a little bit about both of those, because they were the state of the art up until 15 years ago.
[00:29:57.040 --> 00:30:04.040] And I want to understand both how they work and why they may not have been sufficient for your application.
[00:30:04.280 --> 00:30:08.040] In other words, why were you looking for something beyond two techniques that already existed?
[00:30:08.360 --> 00:30:16.360] When I first became interested in gene editing in the late 2000s, there were people already developing gene editing technologies.
[00:30:16.360 --> 00:30:21.080] In fact, there were multiple iterations of technologies that came around.
[00:30:21.080 --> 00:30:24.360] There's something called mega nuclease, which was very, very early.
[00:30:24.360 --> 00:30:27.800] And then what got me excited was a New York Times article.
[00:30:27.800 --> 00:30:30.040] I think it was 2008 or 2009.
[00:30:30.040 --> 00:30:33.160] It talked about a system called zinc finger nuclease.
[00:30:33.160 --> 00:30:45.640] There's a company in California that's called Sangamo Biosciences, and they were already developing zinc finger nucleases for gene therapy to be able to go and edit DNA and treat disease.
[00:30:45.640 --> 00:30:56.440] But zinc finger was a really challenging system for scientists to adapt and use because it required very sophisticated protein engineering.
[00:30:56.440 --> 00:31:00.280] The way it works is that zinc fingers are protein domains.
[00:31:00.280 --> 00:31:03.000] They're just one glob of protein.
[00:31:03.000 --> 00:31:07.960] Each finger, each zinc finger, can recognize three letters of DNA.
[00:31:07.960 --> 00:31:09.560] And they occur in nature.
[00:31:09.560 --> 00:31:15.480] So you can find them in naturally occurring DNA binding proteins called transcription factors.
[00:31:15.480 --> 00:31:26.840] And they allow transcription factors to go and recognize different genes in the genome to modulate their activity, either turn them on or turn them off or change how much they are expressed in the cell.
[00:31:26.840 --> 00:31:31.240] But how do you get specificity when you are only recognizing three nucleotides?
[00:31:31.240 --> 00:31:36.840] The probability of those three nucleotides showing up seems pretty likely across the genome.
[00:31:36.840 --> 00:31:37.400] Exactly.
[00:31:37.400 --> 00:31:41.720] So, nature has solved this problem by forming zinc finger arrays.
[00:31:41.720 --> 00:31:44.560] So, they tether multiple fingers together.
[00:31:44.560 --> 00:31:47.920] Ah, and all of them have to hit their target of three nucleotides.
[00:31:44.280 --> 00:31:48.800] Correct, that's right.
[00:31:49.040 --> 00:31:53.200] So, if you have an array of three fingers, they recognize nine letters.
[00:31:53.280 --> 00:31:56.000] If you have an array of six fingers, then that's 18 letters.
[00:31:56.000 --> 00:32:02.560] So, in a complicated genome, our genome with 3 billion letters, 18 would give us uniqueness.
[00:32:02.560 --> 00:32:07.200] So, 18 can allow you to find or define just a single position.
[00:32:07.200 --> 00:32:11.360] And that's because 4 to the power of 18 is a big enough number?
[00:32:11.360 --> 00:32:12.880] It's bigger than 3 billion.
[00:32:12.880 --> 00:32:13.280] Yeah.
[00:32:13.600 --> 00:32:14.080] Okay.
[00:32:14.640 --> 00:32:18.720] So, let's now talk about the talons.
[00:32:18.720 --> 00:32:24.080] First of all, what is that technique and why was that not necessarily going to serve your purpose?
[00:32:24.400 --> 00:32:31.040] So, the challenge with zinc finger is that you have engineered the fingers to recognize different combinations of three letters.
[00:32:31.040 --> 00:32:41.040] And you have to make sure that when you tether them together to make a zinc finger array, they can recognize what you intend to recognize in that multiple three fashion.
[00:32:41.040 --> 00:32:44.800] That turned out to be really cumbersome, and usually it doesn't work very well.
[00:32:44.800 --> 00:32:52.480] So, then this other system that you mentioned called transcription activator-like effector T-A-L-E nuclease was developed.
[00:32:52.480 --> 00:32:55.360] So, these systems came also from bacteria.
[00:32:55.360 --> 00:33:01.440] They came from a specific pathogenic bacteria called Xanthomonas or Rhizi.
[00:33:01.440 --> 00:33:05.520] So, it lives on rice plants and is a major rice pathogen.
[00:33:05.520 --> 00:33:14.800] The way these proteins work is that they're injected by the bacteria that's trying to colonize the plant into the plant cell.
[00:33:14.800 --> 00:33:22.720] And once it gets inside, it homes into the genome of the plant cell, finds a gene, and then starts to turn it on.
[00:33:22.720 --> 00:33:28.400] When it turns it on, it allows the plant to become more susceptible to the bacterial colonization.
[00:33:28.400 --> 00:33:33.960] And so, it allows the bacteria to be able to more successfully survive on this whole plant.
[00:33:29.840 --> 00:33:36.600] It's a major pathogenic system.
[00:33:36.920 --> 00:33:45.320] But what is really cool about talons or tails is that they recognize DNA in a very programmable fashion.
[00:33:45.320 --> 00:33:51.560] These proteins, they have repeat domains, just like zinc fingers, that form an array.
[00:33:51.560 --> 00:33:55.640] But individual domains recognize single DNA letters.
[00:33:55.640 --> 00:34:04.200] And so you can find these tail proteins in the bacteria that have 12 or 16 or even 20 different repeats.
[00:34:04.200 --> 00:34:11.800] And so they can recognize long stretches of 12 or 16 or 18 or 20 DNA letters in the plant genome.
[00:34:11.800 --> 00:34:13.400] Plant genome is also large.
[00:34:13.400 --> 00:34:16.840] They are sometimes two, three times the size of our genome.
[00:34:16.840 --> 00:34:21.640] So recognizing long sequences is important for achieving precision.
[00:34:21.640 --> 00:34:23.480] That was the tail system.
[00:34:23.480 --> 00:34:39.080] So what is really cool is that Ula Bonus, who is a researcher in Germany, and also Adam Bagnanove, who is a researcher at Iowa State at the time, they discovered that there's a specific code for how these tail proteins recognize DNA.
[00:34:39.080 --> 00:34:49.000] It turns out that within each one of these repeats, there are two amino acid letters that correspond with a specific DNA letter that it binds to.
[00:34:49.000 --> 00:35:02.600] And so if you take any tail protein and you just dial in different combinations of these two amino acids in each one of the domain, you can specify what DNA sequence this tail protein is able to recognize.
[00:35:02.600 --> 00:35:06.600] So it turned out to be much more easy to use than zinc fingers.
[00:35:06.920 --> 00:35:09.800] So were you satisfied with that?
[00:35:09.800 --> 00:35:11.080] I mean, obviously you weren't.
[00:35:11.080 --> 00:35:14.520] You were sitting there looking up Wikipedia for what CRISPR is when you heard about it.
[00:35:14.640 --> 00:35:21.920] So something must have said to you, hey, as good as the talons are and as much as they're an improvement over zinc fingers, they're still not good enough.
[00:35:21.920 --> 00:35:22.560] Why was that?
[00:35:22.560 --> 00:35:23.680] Why were they not good enough?
[00:35:24.000 --> 00:35:26.000] Zinc fingers were really hard to use.
[00:35:26.000 --> 00:35:37.360] When I tried to engineer it, it was very hard, very difficult, almost impossible for just a single researcher to get something that would recognize the DNA sequence that you're trying to get it to recognize.
[00:35:37.360 --> 00:35:38.800] So it was very hard to use.
[00:35:38.800 --> 00:35:50.960] Talons, on the other hand, were easier, but the repetitive nature of these proteins made it quite cumbersome to engineer new proteins to recognize the sequence you're trying to edit.
[00:35:50.960 --> 00:36:03.280] So for example, if you wanted to be able to modify a specific gene, it could take you maybe several weeks or even a couple months to be able to successfully make one of these tail proteins.
[00:36:03.280 --> 00:36:06.240] And when you do that, usually it works, but not always.
[00:36:06.240 --> 00:36:09.120] And sometimes it's not very effective.
[00:36:09.120 --> 00:36:17.120] Is that because it's so difficult to predict the folding structure of the protein, even if you know its sequence?
[00:36:17.120 --> 00:36:18.720] Why would it take that long?
[00:36:18.720 --> 00:36:24.400] It's long because from a technical perspective, these sequences are just very hard to work with.
[00:36:24.400 --> 00:36:28.640] Because they're very similar to each other, they're prone to recombination.
[00:36:28.640 --> 00:36:35.680] So in order to get a tail to work, you have to, in a very precise way, put every domain in the correct order.
[00:36:35.680 --> 00:36:41.200] Because if you're trying to recognize A, G, T, C, you have to put it together in A, G, T, C.
[00:36:41.200 --> 00:36:43.520] You can't have A, C, G, T.
[00:36:43.520 --> 00:36:45.120] It just wouldn't work.
[00:36:45.120 --> 00:36:52.880] So to put repetitive sequences together and line them up in exactly the order you want, that is really challenging to do.
[00:36:52.880 --> 00:36:55.120] It's possible, but it's very challenging.
[00:36:55.440 --> 00:36:56.400] So it's very interesting.
[00:36:56.400 --> 00:36:58.560] I mean, I think it's worth just sort of taking a step back.
[00:36:58.560 --> 00:37:14.120] Most people can sort of remember what they were thinking 10 years ago, 12, 15 years ago, and the human genome was sequenced nearly 25 years ago, and the promise of gene therapy was hailed as right around the corner.
[00:37:14.120 --> 00:37:19.880] And yet here we are a decade, more than a decade after the sequencing of the human genome.
[00:37:19.880 --> 00:37:29.320] And it doesn't appear that gene therapy through gene editing was any closer than it really was from a practical standpoint a decade earlier.
[00:37:29.320 --> 00:37:31.080] I think that's a bit of a disconnect for people.
[00:37:31.080 --> 00:37:42.600] I think most people who necessarily aren't in a lab would be surprised to understand that simply knowing what the sequence of genes are, I mean, A, we've talked about this a lot on the podcast.
[00:37:42.600 --> 00:37:45.800] We still have no idea what most of these genes do anyway.
[00:37:45.800 --> 00:37:53.000] We have no idea why there are coding segments and the majority of the DNA is non-coding segments.
[00:37:53.000 --> 00:37:57.960] And yet some of these non-coding segments are where mutations exist that result in disease.
[00:37:57.960 --> 00:38:00.200] I mean, all this stuff's still a bit of a mystery.
[00:38:00.200 --> 00:38:27.720] But here you're sort of getting at the central or maybe the most important or jugular question, which is if a person has a disease like cystic fibrosis or sickle cell anemia, where we really know in unambiguous terms where on the DNA, where in the gene this lies, where in the DNA, we know what the substitution is, we know which C was turned into a G or a T.
[00:38:27.720 --> 00:38:30.360] And all we need to be able to do is go in there and fix it.
[00:38:30.360 --> 00:38:33.160] That was something we couldn't do 10 years ago.
[00:38:33.160 --> 00:38:35.480] I think for many people, that's quite surprising.
[00:38:35.480 --> 00:38:40.680] And I know that that's not the problem you were trying to solve, but it's obviously the world you've created.
[00:38:40.680 --> 00:38:43.240] So let's talk about those steps down that road.
[00:38:43.240 --> 00:38:49.040] So you figure out what CRISPR is, you bring yourself up to the state of our discussion today.
[00:38:49.040 --> 00:38:50.240] What's the next thing?
[00:38:50.240 --> 00:38:51.600] The next thing in terms of CRISPR?
[00:38:51.760 --> 00:38:53.600] Yeah, the next thing that you're starting to now pursue.
[00:38:53.600 --> 00:38:58.320] I mean, how are you now going about solving your problem for your good?
[00:38:58.480 --> 00:39:04.560] What point do you realize you're working on a problem that has a far bigger application than just solving your problem?
[00:39:04.560 --> 00:39:13.040] I think pretty quickly, since I started working gene editing, because the Human Genome Project was completed in the early 2000s.
[00:39:13.040 --> 00:39:25.760] And with the human genome having been sequenced, and then also with DNA sequencing technology becoming cheaper and faster, scientists were able to start to sequence many, many more genomes.
[00:39:25.760 --> 00:39:37.040] And so they can start to make comparisons between healthy individuals and also people who are affected by specific diseases to see what's different between their genomes.
[00:39:37.040 --> 00:39:43.120] And by doing that comparison, they can identify the differences that may be causal for disease.
[00:39:43.120 --> 00:39:54.720] And so to date, based on genetic analysis, researchers have probably identified more than 5,000 genetic mutations that have a direct causative role in disease.
[00:39:54.720 --> 00:39:57.120] And so these are called genetic diseases.
[00:39:57.120 --> 00:40:01.840] They are usually affecting a small population of individuals.
[00:40:01.840 --> 00:40:08.480] They're not as common as things like cancer or diabetes or what people call complex or complicated diseases.
[00:40:08.480 --> 00:40:14.000] But nevertheless, these are the ones where we know the exact genetic cause.
[00:40:14.000 --> 00:40:21.120] And so the tantalizing idea is then if you know the mutation in the genome, why not just go and fix it?
[00:40:21.120 --> 00:40:23.280] And so that's where gene editing comes in.
[00:40:23.280 --> 00:40:27.440] And people have since the very beginning trying to realize this idea.
[00:40:27.440 --> 00:40:30.360] They were trying to work on it using mega-nucleases.
[00:40:29.440 --> 00:40:32.600] They were trying to solve this using zinc finger nucleases.
[00:40:33.560 --> 00:40:38.200] They were certainly trying to use talents to also treat diseases this way.
[00:40:38.200 --> 00:40:41.560] But the challenge is that they weren't very efficient.
[00:40:41.560 --> 00:40:48.680] And it was also difficult to apply them to be able to treat a disease with sufficient amount of efficacy.
[00:40:48.680 --> 00:40:57.000] When CRISPR came along, especially with CAS9, it was much easier to be able to design strategies to edit DNA.
[00:40:57.000 --> 00:41:03.800] And that made it much more feasible for many, many groups to really start to work on this idea.
[00:41:03.800 --> 00:41:07.240] Were you finished with your postdoc and now starting your own lab?
[00:41:07.240 --> 00:41:09.800] Yeah, I had just started my lab at MIT.
[00:41:09.800 --> 00:41:12.600] Okay, so you've got how many PhDs in your lab?
[00:41:12.600 --> 00:41:16.280] I probably had maybe 10 PhD students.
[00:41:16.280 --> 00:41:18.280] Okay, and a couple of postdocs to boot.
[00:41:18.280 --> 00:41:18.920] Yeah.
[00:41:19.240 --> 00:41:25.720] And these people have all come to you presumably because they're interested in optogenetics.
[00:41:25.720 --> 00:41:27.720] Yeah, they were interested in different things.
[00:41:27.720 --> 00:41:33.480] So at what point do you come back into the lab and say, I'm going to hit pause on the optogenetics problem.
[00:41:33.480 --> 00:41:36.600] I'm going to kind of go down this CRISPR path for a while.
[00:41:36.600 --> 00:41:41.400] When I first started my lab, I was already focused on the gene editing problem.
[00:41:41.400 --> 00:41:54.920] So when students came to me, even though they came to me wanting to work on optogenetics, I had to convince them that there's this other problem that is also interesting, and maybe we can try to work together and make a difference there.
[00:41:54.920 --> 00:42:02.040] And so I started to try to tell them about CRISPR, tell them about gene editing and all the potential applications.
[00:42:02.360 --> 00:42:04.040] So, keep going down the story now.
[00:42:04.040 --> 00:42:06.520] So, it's the early 2010s.
[00:42:06.520 --> 00:42:10.920] What are the next steps that you take to develop this technology?
[00:42:10.920 --> 00:42:14.800] So, when I first started my lab, I was working on talons.
[00:42:14.440 --> 00:42:21.840] And then, very quickly, I had learned about CRISPR, and I started to also get CRISPR projects going in the lab.
[00:42:22.160 --> 00:42:25.360] And we worked on both systems for a while at the same time.
[00:42:25.360 --> 00:42:34.160] We pretty quickly realized that Talons were difficult to use because of the cumbersome nature of how to make them.
[00:42:34.160 --> 00:42:39.760] And then, because of that, the promise of CRISPR was much more apparent.
[00:42:39.760 --> 00:42:42.560] So, maybe I'll give another analogy.
[00:42:42.560 --> 00:42:54.480] So, we now have a mobile phone, and on a phone, there are many different apps: apps that help you book trips, apps that help you send messages to your friends and family, apps that allow you to take photos.
[00:42:54.480 --> 00:42:56.640] You have a phone, and a phone can do everything.
[00:42:56.640 --> 00:42:58.320] You just load an app onto it.
[00:42:58.320 --> 00:43:05.040] With Talons or Zinc Fingers, the analogy would be you have to build a different device for each one of these functions.
[00:43:05.040 --> 00:43:07.600] Meaning, you would need a different phone for each app.
[00:43:07.600 --> 00:43:08.720] You need different hardware.
[00:43:08.720 --> 00:43:14.000] Yeah, so for every gene you're trying to target, you have to build a brand new protein to be able to target that gene.
[00:43:14.000 --> 00:43:18.320] And that is a very cumbersome and not very effective process.
[00:43:18.320 --> 00:43:23.200] With CRISPR, the promise is that CRISPR is like the smartphone.
[00:43:23.200 --> 00:43:27.120] You can load software onto it to recognize different genes.
[00:43:27.120 --> 00:43:29.840] And the software is the CRISPR RNA.
[00:43:29.840 --> 00:43:41.040] These RNAs are very easy to chemically synthesize, and you can define the gene by reading off of the sequence of the gene, which is already completed through the Human Genome Project.
[00:43:41.040 --> 00:43:48.480] So, all that was just a step function improved over the zinc finger and Talon technology.
[00:43:48.480 --> 00:44:04.040] So, we realized that if we can make CRISPR work, not only in a bacteria, but put it into a human cell and get it to recognize genes in the human cell, then we can have a much more powerful and much more democratized gene editing system.
[00:44:04.520 --> 00:44:12.200] So, what was the breakthrough or set of breakthroughs that led to the utility of this?
[00:44:12.200 --> 00:44:16.680] And did it start with let's just silence a gene first?
[00:44:16.680 --> 00:44:22.760] I mean, let's figure out how to go in and with precision bombing silence one gene.
[00:44:22.760 --> 00:44:29.640] Was that the first problem before the let's actually take a strand of novel DNA and put it in?
[00:44:29.640 --> 00:44:32.200] CRISPR is a natural nucleus.
[00:44:32.200 --> 00:44:37.000] So, in bacteria, it uses the guide RNA to recognize the virus DNA.
[00:44:37.000 --> 00:44:42.920] And then, once it recognizes it, it will cleave the virus DNA, so make a double-stranded DNA break.
[00:44:42.920 --> 00:44:46.360] And so, that's what we're trying to make happen in the human cell.
[00:44:46.360 --> 00:44:55.000] We try to program Cas9 with a guide RNA to go and recognize a specific gene in the human genome and then be able to cut it.
[00:44:55.000 --> 00:44:55.960] Which is valuable.
[00:44:55.960 --> 00:45:00.120] I mean, there are certain cases where overexpression of a gene is pathologic.
[00:45:00.120 --> 00:45:03.800] And if we silence a gene, we fix a disease.
[00:45:03.800 --> 00:45:04.840] Exactly.
[00:45:04.840 --> 00:45:11.640] So, there were a lot of studies done on how breaks in the DNA would get repaired.
[00:45:11.640 --> 00:45:20.920] So, Maria Jason, Jim Haber, they had studied maybe a couple decades before that how DNA repairs would get processed.
[00:45:20.920 --> 00:45:28.600] And so, what they found is that when you make a cut in the DNA, so when you make a break, it will activate repair processes in our cell.
[00:45:28.600 --> 00:45:31.960] So, in fact, our DNAs get DNA breaks all the time.
[00:45:31.960 --> 00:45:36.440] And we have a robust process to be able to fix them to prevent mutations.
[00:45:36.440 --> 00:45:46.960] So, what Maria, Jason, and Jim Haber found is that if you make a cut in the DNA, that cut will activate one of two different repair processes.
[00:45:46.960 --> 00:45:57.440] The first repair process will glue the DNA together, usually correctly, but in a very, very small number of instances, it will introduce a mistake.
[00:45:57.440 --> 00:46:01.040] And that mistake will inactivate the gene.
[00:46:01.040 --> 00:46:04.880] So, it will no longer make the protein product that it's supposed to make.
[00:46:04.880 --> 00:46:08.640] And this is very useful if you wanted to inactivate something in the cell.
[00:46:08.640 --> 00:46:17.040] Sometimes there are mutations that are deleterious, and if you can inactivate that deleterious mutation, then you can make the cell healthy again.
[00:46:17.040 --> 00:46:19.520] And so, that was a very powerful method.
[00:46:19.520 --> 00:46:25.280] The second repair process is called homology-directed repair, HDR.
[00:46:25.600 --> 00:46:32.800] And this relies on a template DNA that carries the sequence that you're trying to repair with.
[00:46:32.800 --> 00:46:43.760] And so, if you make a cut and you also provide a template DNA, then the repair process will copy whatever that's on the template into the DNA break site.
[00:46:43.760 --> 00:46:49.600] And this is a more powerful way to be able to change the DNA sequence in a design fashion.
[00:46:49.920 --> 00:47:00.320] Is there a risk when you cut the DNA and it repairs that it repairs in a manner that remains pathologic, even if it's distinct from the path that was already there?
[00:47:00.320 --> 00:47:04.080] It is possible, but the probability is much, much lower.
[00:47:04.400 --> 00:47:11.920] What about the holy grail, which is to literally edit a new gene to put something in that didn't exist?
[00:47:11.920 --> 00:47:14.400] What was required to take that leap?
[00:47:14.720 --> 00:47:18.000] There are different ways that you may want to change the DNA sequence.
[00:47:18.000 --> 00:47:22.880] You may want to inactivate something, you may want to delete something, and you may want to insert something.
[00:47:22.880 --> 00:47:29.360] So, to do each one of these, you need to have a machinery that will allow you to do that.
[00:47:29.400 --> 00:47:37.480] So, the holy grail would be to be able to insert a gene into anywhere you want precisely and also very efficiently.
[00:47:37.480 --> 00:47:41.400] And to date, that ability is still not quite there yet.
[00:47:41.400 --> 00:47:50.760] We're still working on, and many other groups are working on developing technologies to make that happen with high enough efficiency.
[00:47:50.760 --> 00:47:57.960] We can do it now with very low level, maybe less than a percent or maybe just single-digit percent.
[00:47:57.960 --> 00:48:02.040] But for a big gene that we're trying to put in, we don't have a good way to do that yet.
[00:48:02.360 --> 00:48:10.440] Okay, so currently, is it possible to SNP DNA anywhere you want with the current technology?
[00:48:10.440 --> 00:48:16.520] Yeah, with CRISPR with Cas9, we can pretty much target throughout the genome and make cuts.
[00:48:16.840 --> 00:48:21.800] So, what diseases are currently amenable to that type of gene therapy?
[00:48:21.800 --> 00:48:24.360] And we'll put aside the regulatory stuff, which we can talk about.
[00:48:24.360 --> 00:48:34.360] But if we pretended that humans were laboratory animals where we could do an experiment and actually do this, what are some of the diseases that we could directly affect in that manner?
[00:48:34.680 --> 00:48:40.360] So, there are a lot of genetic diseases where there is a mutation that is pathogenic.
[00:48:40.360 --> 00:48:41.640] Overexpression.
[00:48:41.640 --> 00:48:45.240] In this case, overexpression if you want to deactivate it, but some would be underexpressed.
[00:48:45.400 --> 00:48:55.560] Yeah, so these are genes that are usually important for the body, but there's a mutation in it, and that makes the resulting protein, the mutant protein, deleterious for the patient.
[00:48:55.560 --> 00:48:59.400] So, if you can inactivate these genes, then you can treat disease.
[00:48:59.400 --> 00:49:08.680] So, for example, there are diseases in the liver where there are certain proteins that cause amyloidosis, and that can lead to serious problems.
[00:49:08.680 --> 00:49:16.480] So, by using CRISPR, you can go in and cleave these genes, inactivate them, so that they no longer produce these toxic gene products.
[00:49:16.720 --> 00:49:24.400] Huntington is another example where there are mutations that are occurring in the gene that makes the gene deleterious.
[00:49:24.400 --> 00:49:30.320] And so, if you can go and try to inactivate these deleterious mutations, it may be possible to treat the disease.
[00:49:30.640 --> 00:49:46.000] Is it generally the case that autosomal-dominant diseases are an overexpression problem or an expression of something harmful problem, whereas recessive diseases are the opposite, where you tend to not be producing enough or something of that name?
[00:49:46.000 --> 00:49:46.960] Is that overly simplistic?
[00:49:47.520 --> 00:49:48.960] Yeah, that's a good explanation.
[00:49:49.280 --> 00:50:00.000] So, many people are probably familiar with Huntington's because it is one of the most devastating neurodegenerative diseases imaginable, perhaps second only to Lou Gehrig's disease or ALS.
[00:50:00.000 --> 00:50:07.120] But unlike ALS, where we don't really know the etiology, we clearly know the etiology of Huntington's disease, which is a gene.
[00:50:07.120 --> 00:50:08.160] It's a genetic disease.
[00:50:08.160 --> 00:50:10.560] It's an autosomal dominant gene.
[00:50:10.880 --> 00:50:14.400] And sadly, it doesn't present until later in life.
[00:50:14.400 --> 00:50:27.440] So, many times individuals will pass this gene on prior to the symptoms being manifest, and therefore they go on to suffer the fate of this disease having already passed it on.
[00:50:27.440 --> 00:50:32.480] Tell us a little bit about Huntington's disease and why it may or may not be amenable to this type of treatment.
[00:50:32.800 --> 00:50:37.040] So, Huntington's disease is caused by mutations in a gene called Huntington.
[00:50:37.040 --> 00:50:39.360] This is a gene that's expressing the brain.
[00:50:39.360 --> 00:50:46.720] And in mutated form, this gene accrues an expansion of repeat sequences within the gene.
[00:50:46.720 --> 00:50:51.520] And the longer the repeat is, the more deleterious it is for the patient.
[00:50:51.520 --> 00:51:04.440] And so, the idea would be to try to shorten these repeats, or maybe if you can reduce the amount of repeated sequence of the RNA that's expressed, you could also get a cell to be healthier.
[00:51:04.680 --> 00:51:09.800] The challenges are the repeats happen within the coding region of the gene.
[00:51:09.800 --> 00:51:13.000] So the region that is important for making the protein sequence.
[00:51:13.000 --> 00:51:19.960] So in order to make the resulting edit successful, you have to do it very precisely.
[00:51:19.960 --> 00:51:24.600] You have to do it exactly three letters at a time from the repeat sequence.
[00:51:24.600 --> 00:51:26.520] Otherwise, you would shift the frame.
[00:51:26.520 --> 00:51:29.400] And the gene, remind me, has how many base pairs?
[00:51:29.400 --> 00:51:31.000] The gene has thousands of base pairs.
[00:51:31.000 --> 00:51:32.120] Okay, so it's a huge gene.
[00:51:32.120 --> 00:51:32.600] Yeah.
[00:51:32.600 --> 00:51:38.280] And these repeats can also be several hundred or maybe a thousand repeats long.
[00:51:38.280 --> 00:51:41.080] And so you want to be able to delete them very precisely.
[00:51:41.080 --> 00:51:42.760] So that is one challenge.
[00:51:42.760 --> 00:51:49.000] The other challenge is to be able to deliver the gene editing machineries into the brain and get to enough cells.
[00:51:49.000 --> 00:51:57.320] And there are some virus-based technologies that are coming along, but still we don't have probably the most suitable method yet.
[00:51:57.640 --> 00:51:59.880] So let's talk about delivery a little bit.
[00:51:59.880 --> 00:52:01.800] How do you do this?
[00:52:01.800 --> 00:52:03.160] We now have this idea.
[00:52:03.160 --> 00:52:09.080] Hopefully people can wrap their head around this somewhat challenging idea of what CRISPR is.
[00:52:09.080 --> 00:52:11.160] And maybe we can again just sort of summarize it, right?
[00:52:11.160 --> 00:52:15.960] You have a CRISPR vehicle would be a Cas9 protein.
[00:52:15.960 --> 00:52:33.400] And you also have a guide RNA that is made up of both the piece that you actually want to put in wrapped around another sort of tracer piece that holds it firmly in the Cas9 protein.
[00:52:33.400 --> 00:52:37.400] That Cas9 protein, by the way, does it require its own HeLa case to open that?
[00:52:37.560 --> 00:52:39.000] No, it can do it itself.
[00:52:39.240 --> 00:52:39.880] Beautiful.
[00:52:39.880 --> 00:52:47.040] So it's a one-man shop that runs up the host DNA, opening it and waiting to find its match.
[00:52:47.280 --> 00:52:54.480] And it waits and waits and waits, and then it finds its match, holds the strands of DNA at the Cas9, and then clip.
[00:52:54.480 --> 00:52:55.040] Correct.
[00:52:55.360 --> 00:52:58.240] And obviously, if you then put in something, it can open it.
[00:52:58.400 --> 00:53:02.800] So, how do you actually deliver the Cas9 protein to an individual?
[00:53:02.800 --> 00:53:04.560] That is really the big challenge.
[00:53:04.560 --> 00:53:16.160] So, right now, there are clinical applications of CRISPR for treating different diseases, diseasing the blood, like sickle cell disease or diseasing the liver, diseasing the eye, and many other places.
[00:53:16.160 --> 00:53:22.080] And so, depending on where you're trying to deliver CRISPR into, there are different technologies that people use.
[00:53:22.080 --> 00:53:23.920] Maybe the simplest might be in the blood.
[00:53:23.920 --> 00:53:24.400] Sure.
[00:53:24.400 --> 00:53:30.800] Okay, so tell people what sickle cell anemia is and why it's amenable to this type of therapy.
[00:53:30.800 --> 00:53:31.280] Right.
[00:53:31.280 --> 00:53:36.800] So, sickle cell anemia is caused by a mutation that causes the red blood cell to sickle.
[00:53:36.800 --> 00:53:38.640] So, they form a sickle form.
[00:53:38.640 --> 00:53:41.120] And so, they're not able to properly function.
[00:53:41.120 --> 00:53:48.400] And sometimes they can aggregate, and then this can cause occlusion in the blood vessel and can cause serious problems.
[00:53:48.400 --> 00:53:56.000] And so, these red blood cells are made by progenitor or stem cells in the body that produce these red blood cells.
[00:53:56.000 --> 00:53:57.600] And it's a simple mutation.
[00:53:57.600 --> 00:54:01.760] It's a single-point mutation that changes one amino acid.
[00:54:01.760 --> 00:54:05.920] And that's one amino acid based on one base pair change.
[00:54:05.920 --> 00:54:07.040] I can't remember what it is.
[00:54:07.040 --> 00:54:09.120] It's an alanine to a glycine or something.
[00:54:09.120 --> 00:54:14.000] It's quite trivial, is what leads to all of this downstream badness you talk about.
[00:54:14.000 --> 00:54:17.600] But now, current treatment for these patients is blood transfusions, right?
[00:54:17.600 --> 00:54:22.240] I mean, it's an awful disease, and these patients experience unbearable pain.
[00:54:22.240 --> 00:54:26.400] As an aside, some might ask: why does this disease exist?
[00:54:26.400 --> 00:54:28.640] Why didn't Darwin get rid of this?
[00:54:28.640 --> 00:54:31.320] Well, it turns out there's an advantage to having the trait.
[00:54:31.320 --> 00:54:32.280] For malaria.
[00:54:29.840 --> 00:54:32.520] Right.
[00:54:32.760 --> 00:54:40.840] So it turns out that if you have one copy of the sickle gene and the other one is normal, you have normal-looking red blood cells.
[00:54:40.840 --> 00:54:45.080] You don't get the disease, but you actually get protection, as you said, from malaria.
[00:54:45.080 --> 00:54:50.520] So that would keep this propagating, particularly in a malaria-rich area like Africa.
[00:54:50.520 --> 00:54:56.360] This is why it's much more prevalent in a black population than a white population, because it offered some benefit.
[00:54:56.360 --> 00:54:59.480] But if you have two copies of the gene, you get the sickling.
[00:54:59.480 --> 00:55:02.360] Those are not the people that are passing on their genes historically.
[00:55:02.360 --> 00:55:08.120] They would have perished before reproduction and also just perished in a great deal of pain.
[00:55:08.120 --> 00:55:09.880] But nevertheless, here we are today.
[00:55:09.880 --> 00:55:16.520] So you have to be able to go into the bone marrow to make this change because there's no point in doing this if you have to do this every week.
[00:55:16.520 --> 00:55:18.280] You want to do it one and done, right?
[00:55:18.280 --> 00:55:19.000] That's right.
[00:55:19.000 --> 00:55:25.080] One of the promises of gene editing is that it can provide a single treatment that is a cure for the disease.
[00:55:25.080 --> 00:55:37.160] And so in the case of sickle cell, what happens is that the doctor will mobilize the stem cells, the bone marrow cells, from the patient, get them to come out and be able to harvest these bone marrow cells.
[00:55:37.480 --> 00:55:40.600] And they don't necessarily do this with a bone marrow aspirate.
[00:55:40.600 --> 00:55:46.360] They do it by giving them medications that cause them to secrete more progenitor cells into the plasma.
[00:55:46.360 --> 00:55:46.920] That's right.
[00:55:46.920 --> 00:55:50.600] This is a current practice in the medical field.
[00:55:50.600 --> 00:55:54.600] And so they will get the patient, harvest their bone marrow cells.
[00:55:54.600 --> 00:56:03.960] And these cells are going to be modified in the laboratory where researchers will take the messenger RNA for Cas9.
[00:56:03.960 --> 00:56:07.000] So this will allow the cell to produce the Cas9 protein.
[00:56:07.000 --> 00:56:09.000] And they will also take guide RNA.
[00:56:09.000 --> 00:56:18.080] Maybe just tell folks really quickly, sorry to interrupt, Feng, but maybe just explain really quickly for people the relationship between DNA, RNA, messenger RNA, protein.
[00:56:18.080 --> 00:56:23.280] It's the central dogma, but that way when you say what you're about to say, they'll know why it works.
[00:56:23.600 --> 00:56:26.640] So there are different ways to get a protein to the cell.
[00:56:26.640 --> 00:56:31.200] And the way that proteins are made in the cell is that they're encoded in DNA.
[00:56:31.200 --> 00:56:36.080] And the DNA has to be transcribed into messenger RNA.
[00:56:36.080 --> 00:56:41.200] And that RNA is then translated by the ribosome into the protein.
[00:56:41.200 --> 00:56:54.480] And so if you can put into a cell either the DNA, the gene for Cas9 or the messenger RNA for Cas9 or the protein for Cas9, the cell will eventually have Cas9.
[00:56:54.480 --> 00:56:58.960] Because if you put in the DNA, the cell will start to make mRNA based on it.
[00:56:58.960 --> 00:57:01.520] And that mRNA will get translated into the protein.
[00:57:01.520 --> 00:57:02.880] But how do you put it in the DNA?
[00:57:02.880 --> 00:57:05.200] Isn't that the whole problem that we're trying to solve?
[00:57:05.200 --> 00:57:05.360] Right.
[00:57:05.360 --> 00:57:08.000] So there are different ways to put these things in.
[00:57:08.000 --> 00:57:09.040] So for DNA.
[00:57:09.360 --> 00:57:10.560] Just use a virus.
[00:57:10.560 --> 00:57:19.040] You can use a virus, or you can, if you're working with cells in a petri dish, you can directly electroporate the DNA into the cell.
[00:57:19.040 --> 00:57:22.160] And this is done by zapping the cell with the electrical current.
[00:57:22.160 --> 00:57:24.080] It will rupture the membrane.
[00:57:24.080 --> 00:57:26.720] When the membrane ruptures, things can leak in.
[00:57:26.720 --> 00:57:29.040] So you have DNA that's outside the cell.
[00:57:29.040 --> 00:57:35.360] When the cell membrane ruptures, the DNA that's outside the cell will flow into the inside of the cell and get into the cell.
[00:57:35.360 --> 00:57:38.560] And this is actually how people treat sickle cell disease.
[00:57:38.560 --> 00:57:40.320] Except they're not putting DNA into the cell.
[00:57:40.320 --> 00:57:42.160] They're putting mRNA.
[00:57:42.160 --> 00:57:54.240] So they incubate these bone marrow stem cells that have the sickle cell mutation in a bath of mRNA for Cas9 and the guide RNA separately.
[00:57:54.240 --> 00:58:08.280] And it's amazing that once those cells acquire both the mRNA for Cas9 and the mRNA that is corresponding to the guide, it will translate into a Cas9 protein.
[00:58:08.280 --> 00:58:08.680] Yes.
[00:58:08.680 --> 00:58:11.880] It doesn't translate guide RNA into a protein.
[00:58:11.880 --> 00:58:14.360] It just stays there and they find each other.
[00:58:14.360 --> 00:58:15.320] That's correct.
[00:58:15.320 --> 00:58:21.080] It is so remarkable new that anything works in this universe, but that's up there is one of those things that kind of just amazes me.
[00:58:21.240 --> 00:58:32.040] Well, I mean, this is really the result of the biotechnology revolution, the molecular biology discoveries that have really under sort of paved the biotechnology revolution.
[00:58:32.040 --> 00:58:35.400] And now, what is the efficiency of that process?
[00:58:35.400 --> 00:58:46.360] So once you go ahead and put those two bits of very different RNA into the proximity of these bone marrow cells.
[00:58:46.680 --> 00:58:51.880] This can be quite efficient in the laboratory or in these petri dish settings.
[00:58:51.880 --> 00:58:54.360] This can be approaching 100%.
[00:58:54.680 --> 00:58:55.320] Okay.
[00:58:55.320 --> 00:59:03.800] So now within a very short period of time, you have cut out and also reinserted.
[00:59:03.960 --> 00:59:04.920] It's a single base pair.
[00:59:04.920 --> 00:59:05.560] So what are they doing?
[00:59:05.560 --> 00:59:07.400] What's the exact thing you're asking Cas9 to do?
[00:59:07.560 --> 00:59:13.480] Actually, for sickle cell, the treatment that has recently been approved in the last year is actually different.
[00:59:13.960 --> 00:59:37.320] The way the treatment works is that for sickle cell patients, it's been found that for some individuals who have the sickle cell mutation, if they also carry another mutation, or if they somehow is able to express the fetal version of hemoglobin, then their sickle cell symptoms are much, much less.
[00:59:37.320 --> 00:59:51.680] And so, for treating sickle cell patients with gene editing, the therapy actually goes and modifies a different gene, modulates its expression to then allow the fetal hemoglobin gene to turn on.
[00:59:51.680 --> 00:59:58.960] And why is that an easier solution than simply changing the one amino acid that's broken in the first place?
[00:59:58.960 --> 01:00:06.480] Because the way it works is that it simply makes a cut and it doesn't require that's right.
[01:00:06.480 --> 01:00:06.960] I see.
[01:00:06.960 --> 01:00:20.160] So, we're still at the point where in vitro, we're still better off with a cleavage, just a straight cut of the DNA, than even a single base pair switch to fix one amino acid.
[01:00:20.160 --> 01:00:22.240] That latter is a harder problem.
[01:00:22.240 --> 01:00:27.040] The latter is a harder problem, but there's also very good progress on that front.
[01:00:27.040 --> 01:00:30.240] So, for example, David Liu developed a methodology.
[01:00:30.640 --> 01:00:35.920] Yeah, also, yeah, he's a colleague of mine, and he developed a technology called base editing.
[01:00:35.920 --> 01:00:43.840] And base editing allows you to use Cas9, you get rid of the DNA cleaving activity of Cas9.
[01:00:43.840 --> 01:00:46.720] So, it simply goes and binds the DNA.
[01:00:46.720 --> 01:00:57.440] So, you use it as kind of a guidance system to direct a different enzyme called a deaminase to be able to go and chemically modify a single base.
[01:00:57.600 --> 01:01:11.280] It's amazing to me Feng that that is easier than what we just wish we could do, which is change that C to a G, take out the C, make it a G, and that will give me the amino acid I want.
[01:01:11.280 --> 01:01:15.840] Like the fact that we're chemically having to modify it with a deaminase.
[01:01:15.840 --> 01:01:18.800] What do you think is necessary to take this next?
[01:01:19.040 --> 01:01:25.280] Because we've already had, as you described it, a big step function from where we were 10 years ago.
[01:01:25.280 --> 01:01:31.080] What's going to be required for the next step function for the science fiction to start?
[01:01:31.400 --> 01:01:37.000] Yeah, so I think this really goes back to the division of genetic medicine.
[01:01:37.000 --> 01:01:40.120] Genetic medicine is very powerful.
[01:01:40.120 --> 01:01:44.760] CRISPR is part of it, but it's really a two-component system.
[01:01:44.760 --> 01:01:48.840] There is the medicine itself, and then there's also the delivery technology.
[01:01:48.840 --> 01:01:56.440] So you need to have the right vehicle for delivery and the right payload to be able to treat the disease in the right cell.
[01:01:56.440 --> 01:01:59.480] And we're limited more on the delivery than delivery.
[01:01:59.480 --> 01:02:01.800] So the payload technology has come a long way.
[01:02:01.800 --> 01:02:11.720] We now have mRNA, we have Cas9, we have base editing, we have prime editing, we have a lot of different 

Prompt 2: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 3: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 4: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Prompt 5: Context Setup

You are an expert data extractor tasked with analyzing a podcast transcript. 
I will provide you with part 2 of 3 from a podcast transcript. 
I will then ask you to extract different types of information from this content in subsequent messages. Please confirm you have received and understood the transcript content.

Transcript section:
dology.
[01:00:30.640 --> 01:00:35.920] Yeah, also, yeah, he's a colleague of mine, and he developed a technology called base editing.
[01:00:35.920 --> 01:00:43.840] And base editing allows you to use Cas9, you get rid of the DNA cleaving activity of Cas9.
[01:00:43.840 --> 01:00:46.720] So, it simply goes and binds the DNA.
[01:00:46.720 --> 01:00:57.440] So, you use it as kind of a guidance system to direct a different enzyme called a deaminase to be able to go and chemically modify a single base.
[01:00:57.600 --> 01:01:11.280] It's amazing to me Feng that that is easier than what we just wish we could do, which is change that C to a G, take out the C, make it a G, and that will give me the amino acid I want.
[01:01:11.280 --> 01:01:15.840] Like the fact that we're chemically having to modify it with a deaminase.
[01:01:15.840 --> 01:01:18.800] What do you think is necessary to take this next?
[01:01:19.040 --> 01:01:25.280] Because we've already had, as you described it, a big step function from where we were 10 years ago.
[01:01:25.280 --> 01:01:31.080] What's going to be required for the next step function for the science fiction to start?
[01:01:31.400 --> 01:01:37.000] Yeah, so I think this really goes back to the division of genetic medicine.
[01:01:37.000 --> 01:01:40.120] Genetic medicine is very powerful.
[01:01:40.120 --> 01:01:44.760] CRISPR is part of it, but it's really a two-component system.
[01:01:44.760 --> 01:01:48.840] There is the medicine itself, and then there's also the delivery technology.
[01:01:48.840 --> 01:01:56.440] So you need to have the right vehicle for delivery and the right payload to be able to treat the disease in the right cell.
[01:01:56.440 --> 01:01:59.480] And we're limited more on the delivery than delivery.
[01:01:59.480 --> 01:02:01.800] So the payload technology has come a long way.
[01:02:01.800 --> 01:02:11.720] We now have mRNA, we have Cas9, we have base editing, we have prime editing, we have a lot of different types of editing technology and even epigenetic editing.
[01:02:11.720 --> 01:02:19.880] But the bottleneck is how do we put these really powerful payloads into the right cells in the right tissue in the body?
[01:02:19.880 --> 01:02:20.760] Say those again.
[01:02:20.760 --> 01:02:23.320] What were our tools, Cas9, base pair editing?
[01:02:23.480 --> 01:02:24.760] Yeah, we have Cas9.
[01:02:24.760 --> 01:02:25.880] We have base editing.
[01:02:25.880 --> 01:02:27.240] We have prime editing.
[01:02:27.240 --> 01:02:29.480] We have different recombinases.
[01:02:29.480 --> 01:02:34.520] We have epigenetic modifiers that also are based on Cas9.
[01:02:34.520 --> 01:02:35.560] We have mRNA.
[01:02:35.560 --> 01:02:36.920] We have siRNA.
[01:02:36.920 --> 01:02:41.000] We have a lot of different things that can modulate and modify cells.
[01:02:41.000 --> 01:02:46.200] I want to actually come back and talk about the epigenetic modification using Cas9.
[01:02:46.200 --> 01:02:48.520] It's not a payload problem, it's a delivery problem.
[01:02:48.520 --> 01:02:48.920] Right.
[01:02:48.920 --> 01:02:49.560] Yeah.
[01:02:49.560 --> 01:02:51.800] And it's also a biological problem.
[01:02:51.800 --> 01:02:54.200] Right, because many diseases will not be amenable to this.
[01:02:54.200 --> 01:02:59.160] So I'm sure everybody thinks, well, Peter, we've heard you say that cancer is a genetic disease.
[01:02:59.160 --> 01:03:03.320] Does that mean that once we solve the delivery problem, we solve cancer?
[01:03:03.320 --> 01:03:05.240] Why is that not necessarily true?
[01:03:05.240 --> 01:03:10.120] That is because cancer is caused by many different risk mutations in the cell.
[01:03:10.120 --> 01:03:20.560] And so it's difficult to treat cancer by correcting the mutation because you really have to be able to correct at a very, very high efficiency.
[01:03:20.880 --> 01:03:31.600] If you have a few cells, a few cancer cells that have not been corrected, those cells will continue to divide and replicate and form tumors and even metastasize.
[01:03:31.600 --> 01:03:33.040] That is really challenging.
[01:03:33.040 --> 01:03:38.000] But people are using gene editing in the cancer therapeutic context.
[01:03:38.000 --> 01:03:48.240] What they're doing is that they are using gene editing to engineer immune cells so that the immune cells are more potent at recognizing and killing cancer cells.
[01:03:48.240 --> 01:03:50.880] And this is part of what's called immunotherapy.
[01:03:50.880 --> 01:03:51.360] Yeah.
[01:03:51.360 --> 01:03:53.360] Everybody's talking about AI.
[01:03:53.360 --> 01:03:59.920] Does AI enable this any better on either side, on the payload side or on the delivery side?
[01:03:59.920 --> 01:04:02.960] AI is very powerful for protein engineering.
[01:04:02.960 --> 01:04:10.560] In the past few years, there's been amazing breakthrough in the use of AI for predicting protein structures.
[01:04:10.560 --> 01:04:20.000] Each protein is made of a unique sequence of amino acids, and the unique sequence allows a protein to fold into a specific shape.
[01:04:20.000 --> 01:04:30.560] And one of the holy grail problems for a long time has been how do you take just the letters of a protein and predict what the shape of the protein looks like?
[01:04:30.560 --> 01:04:37.600] And this is something that many, many scientists have worked on for a long time, but hasn't been able to come up with a good solution.
[01:04:37.600 --> 01:04:48.400] But it was really in 2020-21, the use of AI by this group called DeepMind that was able to come up with a solution called AlphaFoll2.
[01:04:48.400 --> 01:04:57.520] And AlphaFold2 is an AI-based system that has learned from all of the structures of proteins that scientists have experimentally determined.
[01:04:57.520 --> 01:05:01.880] And when they solved those structures, there's a huge database of them.
[01:05:01.880 --> 01:05:10.440] And they were able to use AI to look at all of them and learn from that large database to then come up with a prediction system called AlphaFollow 2.
[01:05:10.440 --> 01:05:12.040] It's amazing to me.
[01:05:12.040 --> 01:05:14.200] Maybe just explain to people again.
[01:05:14.200 --> 01:05:15.480] Pick your favorite protein.
[01:05:15.480 --> 01:05:18.760] How many amino acids are in it in the primary sequence?
[01:05:18.760 --> 01:05:22.920] We can pick the green fluorescent protein from jellyfish, maybe 300 amino acids.
[01:05:23.080 --> 01:05:23.480] 300.
[01:05:23.480 --> 01:05:24.840] It's a relatively small protein.
[01:05:24.840 --> 01:05:25.320] Yeah.
[01:05:25.320 --> 01:05:25.960] Okay.
[01:05:25.960 --> 01:05:30.600] So it has a primary structure, which is like, what are the actual amino acids?
[01:05:30.600 --> 01:05:31.000] Correct.
[01:05:31.000 --> 01:05:35.880] It has a secondary structure, which is like, well, when does it actually form a helix?
[01:05:35.880 --> 01:05:37.720] When does it form a sheet?
[01:05:37.720 --> 01:05:42.440] It has a tertiary structure, which is kind of like how it starts to bend.
[01:05:42.760 --> 01:05:48.920] And then it has this quaternary structure, which is how the whole thing fits together in complicated three-dimensional folds.
[01:05:48.920 --> 01:05:49.800] Exactly.
[01:05:49.800 --> 01:05:58.840] What you said a moment ago is if a scientist wants to make a protein and they know what it needs to look like, they kind of know what the quaternary structure is.
[01:05:58.840 --> 01:05:59.800] I got to get this protein.
[01:05:59.800 --> 01:06:02.920] I got to design a molecule that fits in that receptor.
[01:06:02.920 --> 01:06:08.040] It's almost a trial and error problem to go from the primary sequence to that.
[01:06:08.040 --> 01:06:08.520] Right.
[01:06:08.520 --> 01:06:20.520] You're saying that AI is really good at doing this thing where it knows the relationship between primary sequence and final quaternary structure.
[01:06:20.520 --> 01:06:26.600] Is that just literally linear regression at a level we've never understood it because humans can't do it?
[01:06:26.600 --> 01:06:31.320] But is it basically just solving the world's most complicated linear regression problem?
[01:06:31.320 --> 01:06:34.520] I think at a very fundamental level, that is what is happening.
[01:06:34.520 --> 01:06:39.320] But with the human brain, we can't process so much data very effectively.
[01:06:39.320 --> 01:06:45.840] But with AI, you can have these massive neural networks that can really process this.
[01:06:45.840 --> 01:06:48.320] Let's go back to animal models for a moment.
[01:06:44.840 --> 01:06:50.880] You touched briefly on the idea of a transgenic mouse.
[01:06:51.040 --> 01:06:56.560] And again, people who have listened to this podcast for years are no stranger to the transgenic mouse.
[01:06:56.560 --> 01:07:00.880] So many amazing breakthroughs in science have come through these.
[01:07:00.880 --> 01:07:06.000] And frankly, just through genetic understanding of mice, making changes to a mouse gene.
[01:07:06.000 --> 01:07:08.880] You know, we recently had Dina Dubal on the podcast.
[01:07:08.880 --> 01:07:13.040] We talked about Clotho, to me, one of the most interesting proteins out there.
[01:07:13.040 --> 01:07:16.800] And of course, we learned the story of how Clotho came about.
[01:07:16.800 --> 01:07:21.440] Silencing a gene, found this thing, overexpressed the gene, found this thing.
[01:07:21.440 --> 01:07:24.640] Okay, how does CRISPR enable that today?
[01:07:24.640 --> 01:07:33.920] Has it changed the ability of people working on totally other problems to get there quicker using laboratory animals?
[01:07:33.920 --> 01:07:34.640] Right.
[01:07:34.640 --> 01:07:39.440] So the transgenic mouse technology really revolutionized biology.
[01:07:39.440 --> 01:07:46.640] And when the transgenic mouse technology was developed, the way it worked is that you would start with stem cells for mice.
[01:07:46.640 --> 01:07:52.800] You would modify these stem cells and then you put the stem cells back into an embryo.
[01:07:52.800 --> 01:07:59.760] And then you transplant the embryo into a mouse so that the embryo can develop into fetus and then be born as a new mouse.
[01:07:59.760 --> 01:08:01.680] That is a very long process.
[01:08:01.680 --> 01:08:09.760] And then once the mouse is born, usually that mouse does not have 100% of that genetic modification.
[01:08:09.760 --> 01:08:12.000] So the mouse is called a mosaic.
[01:08:12.000 --> 01:08:24.960] And then you have to take that mouse and breed the mouse again with another mouse, and hoping that the mosaic part is the one that gets expressed and you basically try to concentrate the mosaic portion of it.
[01:08:24.960 --> 01:08:25.760] Exactly.
[01:08:25.760 --> 01:08:35.800] So, when you use this methodology, it can take probably a year or maybe even longer to be able to generate a specific transgenic mouse.
[01:08:29.600 --> 01:08:37.880] So, it used to take a long time to do this.
[01:08:37.880 --> 01:08:49.720] But now, with CRISPR technology, what people can do is they can directly inject the gene editing Cas9 guide RNA into an embryo, into a single-cell embryo, and then modify it there.
[01:08:50.040 --> 01:08:54.040] So, again, this is so brilliant because we can't do this in humans, of course.
[01:08:54.040 --> 01:08:57.560] I mean, we could, but that's a whole ethical discussion we should talk about.
[01:08:57.560 --> 01:08:58.920] But we can directly do that.
[01:08:58.920 --> 01:09:01.160] So, we can make a transgenic mouse in one go.
[01:09:01.160 --> 01:09:01.640] Exactly.
[01:09:01.640 --> 01:09:04.280] So, the mouse gestation period is 21 days.
[01:09:04.280 --> 01:09:06.760] So, after 21 days, you have a transgenic mouse.
[01:09:06.760 --> 01:09:09.320] What has this done to the field of biomedical research?
[01:09:09.400 --> 01:09:12.360] It has accelerated biomedical research dramatically.
[01:09:12.360 --> 01:09:18.280] So, imagine yourself as a graduate student, and usually a PhD will take five years.
[01:09:18.280 --> 01:09:23.960] And a lot of that time is the rote work of transfecting mice and waiting for the.
[01:09:24.040 --> 01:09:24.280] Yeah.
[01:09:24.280 --> 01:09:24.440] Yeah.
[01:09:24.440 --> 01:09:30.120] So, if you have to wait two years to get a mouse so that you can begin your experiment, that is a long time.
[01:09:30.120 --> 01:09:35.560] So, now with CRISPR or gene editing, you can get a mouse in two, three months.
[01:09:35.880 --> 01:09:38.120] So, let's play devil's advocate for a moment.
[01:09:38.120 --> 01:09:42.520] When you did your PhD, you had to slog through the old-fashioned way.
[01:09:42.520 --> 01:09:44.520] Were there hidden benefits of that?
[01:09:44.520 --> 01:09:49.480] In other words, did it give you more time to read, more time to be curious, more time to fail?
[01:09:49.480 --> 01:10:04.120] Again, it's a tangential discussion, but do you worry that with this remarkable precision tool that budding scientists are missing out on an experience that you and your entire generation and everyone before you had?
[01:10:04.120 --> 01:10:10.120] Or do you think that that's a relatively small price to pay for the pace of development?
[01:10:10.120 --> 01:10:11.880] I'm not too concerned about it.
[01:10:11.880 --> 01:10:20.560] I think if we can accelerate the accumulation of knowledge, the acquisition of data, I think that will really help science move a lot faster.
[01:10:20.560 --> 01:10:35.840] I think in the future, especially with higher throughput technologies, CRISPR, DNA sequencing, and many other things, together we're going to be accumulating new data for biology at an exponential pace.
[01:10:35.840 --> 01:10:47.840] And we're not only going to be relying on our own ability to analyze the data, but we're going to have AI to help us, these large systems that can draw much, much larger regression analysis.
[01:10:47.840 --> 01:10:55.040] And with that, I think biology discovery and disease treatment development will really accelerate.
[01:10:55.040 --> 01:10:57.200] I think that's a really exciting future.
[01:10:57.200 --> 01:11:06.880] Now, we've talked a lot about Cas9, but you've also specifically done quite a lot with another protein, another CRISPR-associated protein called Cas13.
[01:11:06.880 --> 01:11:12.160] What's the difference, and how does this potentially impact future work?
[01:11:12.160 --> 01:11:18.240] CRISPR is a bacterial immune system, and there are many, many different types of CRISPR.
[01:11:18.240 --> 01:11:25.040] And so in nature, bacteria are invaded by DNA viruses, by RNA viruses, all sorts of different viruses.
[01:11:25.040 --> 01:11:33.120] Cas9 protects bacteria against DNA viruses, but then there also needs to be a CRISPR system that protects against RNA viruses.
[01:11:33.120 --> 01:11:37.760] So Cas13 is the RNA analog of Cas9.
[01:11:37.760 --> 01:11:43.760] It uses a guide RNA to recognize the RNA virus and then cleave the RNA genome.
[01:11:43.760 --> 01:11:57.840] And what is really interesting about Cas13 is that, unlike Cas9, it not only cleaves the recognized RNA, but once it recognizes a piece of RNA, it also turns on almost a suicidal function.
[01:11:57.840 --> 01:12:01.960] It goes and cleaves any other RNA that's in the bacterial cell.
[01:12:02.280 --> 01:12:19.400] And so, in the infection cycle, you can think of this as an altruistic system where when the Cas13 recognizes my cell has been infected by RNA virus, I'm going to shut myself down, kill the cell, and save the population.
[01:12:19.720 --> 01:12:21.880] So, hang on, why is that the case?
[01:12:21.880 --> 01:12:25.160] So, why does the cell not choose to do that with the DNA virus?
[01:12:25.160 --> 01:12:29.000] Are RNA viruses necessarily more lethal to bacteria?
[01:12:29.000 --> 01:12:31.960] RNA is usually more abundant.
[01:12:31.960 --> 01:12:35.640] Once the RNA gets produced, there are many copies.
[01:12:35.640 --> 01:12:39.640] And so, it's difficult to shut down every single copy of RNA.
[01:12:39.640 --> 01:12:42.120] Whereas with DNA, there's usually just one copy of DNA.
[01:12:42.120 --> 01:12:43.160] So, if you can shut it down.
[01:12:43.480 --> 01:12:51.480] So, when a DNA virus infects a bacteria, let's just talk about a very specific bacteriophage latches on the outside of an E.
[01:12:51.480 --> 01:12:54.920] coli, it's going to shoot in a piece of DNA.
[01:12:54.920 --> 01:12:59.160] We've already talked at length about how that works and the role Cas9 plays in that.
[01:12:59.160 --> 01:13:07.560] When a different bacteriophage comes along, you're saying it inserts a lot of RNA, or just the RNA replicates much quicker.
[01:13:07.720 --> 01:13:08.520] RNA replicates quicker.
[01:13:08.840 --> 01:13:11.240] Okay, so it inserts a small amount of RNA.
[01:13:11.240 --> 01:13:17.400] But remind me why a small amount of RNA will be amplified much quicker than a small amount of DNA.
[01:13:17.400 --> 01:13:19.480] Is it because it's one step less?
[01:13:19.480 --> 01:13:24.040] It's already made the machinery to go in front of the translating ribosome.
[01:13:24.040 --> 01:13:24.440] Exactly.
[01:13:24.440 --> 01:13:29.080] Because DNA has to go to RNA to make proteins that go back to make DNA.
[01:13:29.080 --> 01:13:33.160] So you're saying Cas13 also has a suicide feature.
[01:13:33.160 --> 01:13:38.520] And the suicide feature is actually quite useful for developing diagnostic technologies.
[01:13:38.520 --> 01:13:46.880] And so, especially during COVID, we use CAS13 to develop a way to detect coronavirus RNA.
[01:13:43.240 --> 01:13:51.520] And it provided a way to have a simple and rapid detection method.
[01:13:51.840 --> 01:13:52.800] So say more about that.
[01:13:52.800 --> 01:13:54.480] Was that really the first application?
[01:13:54.640 --> 01:13:57.360] That was, I think, one of the first applications.
[01:13:57.360 --> 01:13:58.000] Yeah.
[01:13:58.000 --> 01:13:59.520] How would that have been done before?
[01:13:59.520 --> 01:14:03.280] And what was the speed and accuracy of it being done in that way?
[01:14:03.280 --> 01:14:07.200] So the most widespread method for diagnostics is using PCR.
[01:14:07.200 --> 01:14:09.760] So it checks for the nucleic acid sequence.
[01:14:09.760 --> 01:14:12.080] But PCR is a laboratory-based test.
[01:14:12.080 --> 01:14:17.600] It requires a machine called a PCR machine, which is complicated to run.
[01:14:17.600 --> 01:14:21.600] And you have to be in a laboratory environment to go through the test.
[01:14:21.600 --> 01:14:32.320] What CAS13 provided is something that's more similar to an antigen test, where you can run it at a point of care or even potentially at home.
[01:14:32.320 --> 01:14:36.560] And it will simply require you to take a swab to have the sample.
[01:14:36.560 --> 01:14:44.240] You mix the sample into a buffer, and then Cas13 would react in that buffer to be able to detect the virus sequence.
[01:14:44.240 --> 01:14:49.920] Then you load it onto a paper strip, it will run, and then you see whether or not a band shows up.
[01:14:50.240 --> 01:15:00.880] When we think now about gene editing going forward, and again, we've already established that the payload is not the problem, it's the targeting and the delivery.
[01:15:00.880 --> 01:15:05.360] What are the relative advantages and disadvantages of Cas9 versus Cas13?
[01:15:05.360 --> 01:15:12.160] And are there other CAS or CRISPR-associated proteins out there that might even be better than both of them?
[01:15:12.480 --> 01:15:17.280] So CAS9 and CAS13 both have therapeutic applications.
[01:15:17.280 --> 01:15:20.960] But one of the challenges is that they are large proteins.
[01:15:20.960 --> 01:15:27.600] Cas9 is 1,300 amino acids long, and Cas13s are usually around 1,000 amino acids long.
[01:15:27.600 --> 01:15:29.040] So they're fairly large proteins.
[01:15:29.280 --> 01:15:30.600] How long was Cas9?
[01:15:30.600 --> 01:15:32.440] 1300 amino acids.
[01:15:32.440 --> 01:15:40.760] And so, in order to get Cas9 into a cell, you have to be able to fit Cas9 and the guide RNA into your delivery system.
[01:15:40.760 --> 01:15:48.440] And if you're using viral vectors to deliver, they are usually very compact and you can barely fit Cas9 in.
[01:15:48.440 --> 01:15:52.120] So then the nice thing is, what if you have something that's smaller?
[01:15:52.120 --> 01:16:01.480] And so we and also many other groups have worked on trying to discover new proteins that are more compact and more easily packageable.
[01:16:01.480 --> 01:16:08.920] And there are some out there, but sometimes usually they're not as specific or not as active as Cas9.
[01:16:08.920 --> 01:16:11.960] And so there are trade-offs to using some of those systems.
[01:16:11.960 --> 01:16:20.920] And we're working on engineering them, and there's a lot of good progress turning those systems into specific and comparably active systems as Cas9.
[01:16:20.920 --> 01:16:32.680] So what would be, if you could wave a magic wand, how big a Cas protein would you tolerate such that your Cas protein plus your guide RNA would easily fit into your delivery vehicle?
[01:16:32.680 --> 01:16:35.400] Would you want to be half that size and be 500 amino acids?
[01:16:35.400 --> 01:16:36.920] Do you need to be smaller than that?
[01:16:36.920 --> 01:16:40.280] If you could shrink it down to 1,000 base pairs, so 300 amino acids.
[01:16:40.440 --> 01:16:41.560] 300 amino acids.
[01:16:41.560 --> 01:16:44.520] That would be ideal, but I think that's challenging to do.
[01:16:44.680 --> 01:16:46.760] Let's maybe make sure I understand why.
[01:16:46.760 --> 01:16:59.720] You're saying, look, at the end of the day, I need a protein that has the structural integrity to hold the guide RNA in place, make its way down into the nucleus, open up the DNA.
[01:16:59.720 --> 01:17:03.240] So it's basically two enzymes: it's a helicase and a nuclease.
[01:17:03.240 --> 01:17:07.640] And it has to be able to march along the DNA, recognize while holding.
[01:17:07.640 --> 01:17:15.000] Again, it's a mechanical problem if you really stop to think about it, just on the smallest level, and then hold, cut, boom, insert eventually.
[01:17:15.200 --> 01:17:18.080] And you're saying, at some point, we just abut the limits of physics.
[01:17:18.080 --> 01:17:21.920] I need a certain amount of amino acids to make that structure.
[01:17:21.920 --> 01:17:26.640] But it sounds to me like you're saying you don't believe that that exists in nature.
[01:17:26.640 --> 01:17:31.760] You're not out there looking for a 300-amino acid Cas protein anymore.
[01:17:31.760 --> 01:17:35.680] You're now going to say, let's use AI to help us build one.
[01:17:35.680 --> 01:17:41.040] There are natural forms of proteins that are small and are like Cas9.
[01:17:41.040 --> 01:17:50.320] In one of our projects, trying to look for a small Cas9, we thought, let's look at the evolutionary origin of where Cas9 came from.
[01:17:50.320 --> 01:18:02.560] By tracing the evolutionary history of Cas9, we found that there is a very large family protein called ISCB that is the ancestral form of Cas9.
[01:18:02.560 --> 01:18:05.760] ISCB is a very small protein.
[01:18:05.760 --> 01:18:08.400] It's only about 450 amino acids.
[01:18:08.400 --> 01:18:11.280] So it's about a third of the size of Cas9.
[01:18:11.280 --> 01:18:13.200] And it does exactly what Cas9 does.
[01:18:13.200 --> 01:18:15.120] It's got a helicase activity.
[01:18:15.120 --> 01:18:17.200] It's got nuclease domains.
[01:18:17.200 --> 01:18:21.760] And it also works with the RNA guide to recognize and cleave DNA.
[01:18:21.760 --> 01:18:28.800] But what is different between ISCB and Cas9 is that the guide RNA for ISCB is much, much larger.
[01:18:28.800 --> 01:18:31.280] So you rob Peter and you got to pay Paul.
[01:18:31.280 --> 01:18:32.320] Exactly.
[01:18:32.320 --> 01:18:35.840] And RNA is not as stable, not as robust.
[01:18:35.840 --> 01:18:38.080] It's more prone to degradation.
[01:18:38.080 --> 01:18:40.640] Yeah, you don't want to go any bigger on guide RNA.
[01:18:40.640 --> 01:18:42.320] You need the best of both worlds.
[01:18:42.320 --> 01:18:49.240] You need a Cas-associated protein that is small and you want to be able to keep the guide RNA.
[01:18:49.240 --> 01:18:49.800] Small.
[01:18:49.800 --> 01:18:50.400] Small.
[01:18:50.400 --> 01:18:50.720] Right.
[01:18:50.720 --> 01:18:52.160] That's the holy grail.
[01:18:52.160 --> 01:18:52.640] Okay.
[01:18:52.640 --> 01:18:57.600] And is it your prediction that that will have to be developed synthetically?
[01:18:57.600 --> 01:19:09.560] I think through engineering, it's not clear that such a compact system has been developed by nature, but we can start from nature's Cas9 or ISCB as a scaffold and then begin to engineer.
[01:19:09.960 --> 01:19:11.800] So what kind of race is on for that?
[01:19:11.800 --> 01:19:15.480] Because I can't even fathom the commercial value of that.
[01:19:15.480 --> 01:19:30.120] If there was a new protein called the Casalpha or CasOmega, whatever, the synthetic version that was small enough to now easily deliver payload, this is a trillion-dollar product.
[01:19:30.360 --> 01:19:31.720] A lot of people are working on it.
[01:19:31.720 --> 01:19:33.960] I think it's certainly going to be very, very useful.
[01:19:33.960 --> 01:19:39.800] Is the lion's share of this work being done in universities or is it being done inside of biotech companies?
[01:19:39.800 --> 01:19:41.400] It's in both places now.
[01:19:41.400 --> 01:19:46.840] Is this one of the biggest races going on in CRISPR biology today?
[01:19:46.840 --> 01:19:48.680] I'm not sure this is the biggest race.
[01:19:48.680 --> 01:19:51.480] There are a lot of other capabilities that we want to realize.
[01:19:51.480 --> 01:19:51.880] Okay.
[01:19:51.880 --> 01:19:57.480] For example, how do you insert large genes into the genome precisely and efficiently?
[01:19:57.480 --> 01:19:59.880] I think that is just as important of a problem.
[01:19:59.880 --> 01:20:04.680] Cas9, even though it's large, we can deliver it to some cells already.
[01:20:04.680 --> 01:20:05.480] So there's already.
[01:20:05.720 --> 01:20:07.240] In vivo or only in vitro?
[01:20:07.320 --> 01:20:08.200] In vivo.
[01:20:08.200 --> 01:20:11.480] What cells can be delivered in vivo using Cas9 currently?
[01:20:11.480 --> 01:20:12.440] So liver.
[01:20:12.440 --> 01:20:12.840] Okay.
[01:20:12.840 --> 01:20:19.640] Yeah, liver is a place where we can use lipid nanoparticles to deliver Cas9 and guide RNA into.
[01:20:19.640 --> 01:20:24.680] The COVID vaccine is made using mRNA and lipid nanoparticle.
[01:20:24.680 --> 01:20:32.440] And so it's a very similar approach where you formulate these lipids with the Cas9 mRNA and guide RNA.
[01:20:32.440 --> 01:20:36.600] And what is it about the liver that makes it amenable to a lipid nanoparticle?
[01:20:36.600 --> 01:20:39.320] There's a lot of lipid recycling happening in the liver.
[01:20:39.320 --> 01:20:45.000] And so when you have a lipid nanoparticle, they get bound by these recycling proteins and get taken up.
[01:20:45.280 --> 01:20:45.920] Interesting.
[01:20:45.920 --> 01:20:48.080] Does it matter the manner in which it's given?
[01:20:48.080 --> 01:20:50.000] Intravenous, intramuscular?
[01:20:50.000 --> 01:20:56.240] Do all lipid nanoparticles end up there provided they're not ingested orally and presumably digested?
[01:20:56.240 --> 01:20:57.600] A lot of it goes to liver.
[01:20:57.600 --> 01:21:01.920] Yeah, because liver also is one of the areas in our body that filter out all the toxins.
[01:21:01.920 --> 01:21:02.320] Yeah.
[01:21:02.320 --> 01:21:03.520] So everything gets trapped there.
[01:21:03.520 --> 01:21:19.200] So in other words, if you were thinking about one of the rare inborn diseases of metabolism, these are the really rare diseases where children are born without a particular enzyme that allows them to either metabolize a certain protein or glucose even for that matter.
[01:21:19.200 --> 01:21:28.880] Do we think that we're on the cusp of being able to use, I don't want to suggest that Cas9 is primitive, but in the context of our discussion, we've realized it has some limitations.
[01:21:28.880 --> 01:21:36.160] But do we think that the CRISPR-Cas9 system is already good enough and sufficient to cure some of those diseases?
[01:21:36.160 --> 01:21:38.000] For some of them, potentially.
[01:21:38.000 --> 01:21:44.640] It will depend on the mutation, whether or not we can use Cas9 or base editing or prime editing to be able to fix the innovation.
[01:21:44.800 --> 01:21:46.480] You have to figure out the payload problem.
[01:21:46.480 --> 01:21:46.800] Right.
[01:21:47.280 --> 01:21:55.840] But if it's in the liver and it can be addressed by targeting hepatocytes in the liver, then the delivery problem is already addressable.
[01:21:55.840 --> 01:21:56.480] Okay.
[01:21:56.480 --> 01:21:58.560] What about genetic diseases of the eye?
[01:21:58.560 --> 01:22:03.600] That's also a place that's easy to reach and you don't require systemic administration.
[01:22:03.600 --> 01:22:09.200] What are the genetic conditions of the eye that might be amenable to this treatment as it stands today?
[01:22:09.200 --> 01:22:14.240] Yeah, there are different eye diseases that are affected by single gene mutations.
[01:22:14.240 --> 01:22:21.840] For example, LCA10 is one of the eye diseases that there's already been a CRISPR strategy being developed for.
[01:22:21.840 --> 01:22:41.880] The way that these diseases in the eye are treated is by designing Cas9 and a guide RNA to be able to knock out the gene that is causing degenerative conditions in the eye, and then using a viral vector to deliver the Cas9 gene and also the guide RNA intraocularly into the patient.
[01:22:41.880 --> 01:22:48.840] And so once the virus gets into the cells in the eye, they will make Cas9, they'll make the guide RNA, and then that will carry out the modification.
[01:22:49.160 --> 01:22:51.560] So in that sense, it's a bit of the old meets the new.
[01:22:51.560 --> 01:23:03.720] You're taking the oldest trick in the book that was the original, original gene therapy vehicle, the virus, and you're combining it with a far smarter payload, which is Cas9 and Guide RNA.
[01:23:03.720 --> 01:23:06.280] Where do these stand in clinical trials right now?
[01:23:06.280 --> 01:23:10.520] Both the liver and the eye, which would obviously be the leading edge of this.
[01:23:10.520 --> 01:23:15.640] So the eye is the first place where in the U.S., a gene therapy was developed and approved.
[01:23:15.640 --> 01:23:17.720] And so this is a drug called Loxterna.
[01:23:17.720 --> 01:23:23.480] Loxerna puts a gene into the eye to be able to treat a disease called LCA2.
[01:23:23.480 --> 01:23:33.960] And so basically the virus provides a gene that is missing in the cells in the eye, and that is able to allow the patients to regain some light sensitivity.
[01:23:33.960 --> 01:23:35.800] And so that was the first gene therapy.
[01:23:35.800 --> 01:23:38.520] And these are patients that are completely blind without it?
[01:23:38.520 --> 01:23:39.160] That's right.
[01:23:39.160 --> 01:23:42.680] And then how much light sensitivity do they get once this gene is inserted?
[01:23:42.680 --> 01:23:46.840] They get some so that they can move around in a room with large obstacles.
[01:23:46.840 --> 01:23:47.400] Wow.
[01:23:47.400 --> 01:23:48.040] Yeah.
[01:23:48.040 --> 01:23:51.240] And this is all done just through a single injection in the eye.
[01:23:51.240 --> 01:23:52.360] A single injection.
[01:23:52.360 --> 01:23:53.000] Wow.
[01:23:53.000 --> 01:23:55.640] What are other ocular targets?
[01:23:55.640 --> 01:24:01.320] So LCA10 is another one that was being developed by Edithask medicine.
[01:24:01.320 --> 01:24:08.360] And the way this disease developed is that there is a mutation that causes degeneration in the eye.
[01:24:08.360 --> 01:24:21.120] And the idea is to use Cas9, use the same viral vector system to deliver it into the eye and have Cas9 inactivate this mutant gene so that it can slow down or stop degeneration.
[01:24:21.440 --> 01:24:27.840] So LCA2, you have to put an active gene in that was missing.
[01:24:27.840 --> 01:24:28.240] Right.
[01:24:28.560 --> 01:24:32.640] LCA, was it 9 or 12, 10, you have to deactivate a gene?
[01:24:33.120 --> 01:24:33.760] That's right.
[01:24:33.760 --> 01:24:34.160] Okay.
[01:24:34.160 --> 01:24:35.520] Where is that in clinical trials?
[01:24:35.520 --> 01:24:36.560] What phase is that in?
[01:24:36.560 --> 01:24:38.400] So it went through phase one, two.
[01:24:38.400 --> 01:24:38.800] Okay.
[01:24:38.800 --> 01:24:40.640] What was the efficacy in phase two?
[01:24:40.640 --> 01:24:41.920] There was some efficacy.
[01:24:41.920 --> 01:24:44.400] What's the phenotype of this patient without gene therapy?
[01:24:44.400 --> 01:24:45.120] Is it total blindness?
[01:24:45.600 --> 01:24:48.320] Also blindness or deteriorating vision.
[01:24:48.320 --> 01:24:49.920] So blind by what age?
[01:24:49.920 --> 01:24:51.280] Probably 30s.
[01:24:51.520 --> 01:24:52.400] Okay, wow.
[01:24:52.400 --> 01:24:54.640] And then what did the phase two find?
[01:24:54.640 --> 01:24:56.960] How much eyesight were they able to restore?
[01:24:56.960 --> 01:25:02.560] They found that there's some improvement in vision, but I think it wasn't as robust as they had hoped for.
[01:25:02.560 --> 01:25:03.120] Okay.
[01:25:03.120 --> 01:25:04.400] Why do you think that is?
[01:25:04.400 --> 01:25:08.400] Why do you think that these therapies are not fully restoring vision?
[01:25:08.400 --> 01:25:18.560] In the case of the LCA2, is it because by the time you treat these people, the neuroplasticity part of it has lost its window of development?
[01:25:18.560 --> 01:25:27.360] In other words, is the problem that if you treat these people late in life, the part of the brain that receives the visual signal isn't developed?
[01:25:27.360 --> 01:25:30.800] Or is it literally that we're just not fixing the eye?
[01:25:30.800 --> 01:25:32.720] The retina doesn't regenerate.
[01:25:32.720 --> 01:25:37.440] So if it's already degenerating, you have to deal with what is left in the retina.
[01:25:37.440 --> 01:25:43.520] And so what you can restore is really capped by whatever that's already left there.
[01:25:43.520 --> 01:25:46.960] And plus there's also inefficiencies in the delivery systems.
[01:25:47.040 --> 01:25:50.080] You're not restoring 100% of the cells.
[01:25:50.080 --> 01:26:01.400] And if you're layering on gene editing on top of it, which also has some less than 100% efficiency, and when you multiply that all together, that's why you're not getting surfaced restoration.
[01:26:01.720 --> 01:26:07.560] So, what would have to be true to fully restore vision in those patients?
[01:26:07.560 --> 01:26:10.360] What set of things would have to be true therapeutically?
[01:26:10.680 --> 01:26:13.560] I think it's only very hard to fully restore vision.
[01:26:13.560 --> 01:26:18.280] If you really want to fully restore vision, you probably have to regenerate the retina.
[01:26:18.280 --> 01:26:21.320] So, you have to replenish cells that are missing.
[01:26:21.320 --> 01:26:23.400] Could that be done epigenetically?
[01:26:23.400 --> 01:26:31.000] What do we know about the epigenome of the retina in the adult versus the infant?
[01:26:31.000 --> 01:26:47.240] Do we know that if we could simultaneously, or even in two treatments, correct the genetic defect, but then return the epigenome to the milieu of embryogenesis or early life?
[01:26:47.240 --> 01:26:49.160] Could that fix the problem?
[01:26:49.480 --> 01:26:56.040] That could potentially work, although this is not my expertise, so I don't know all the processes involved there.
[01:26:56.040 --> 01:27:09.720] If you can recapitulate development in the eye and allow cells to redevelop the retina as it was developing during development, then potentially you can regenerate the eye.
[01:27:09.720 --> 01:27:15.960] On the side of the liver, what has been the success rate of the Cas9 delivery system there?
[01:27:15.960 --> 01:27:19.480] The lipid nanoparticle delivering the Cas9 payload, to be more exact.
[01:27:19.720 --> 01:27:21.400] In the liver, it's quite robust.
[01:27:21.400 --> 01:27:24.200] You can probably get 80-90% in the liver.
[01:27:24.200 --> 01:27:28.920] And that's sufficient to correct certainly any underexpression, right?
[01:27:28.920 --> 01:27:29.400] Right.
[01:27:29.720 --> 01:27:39.160] In the liver, there's also an interesting development where scientists are trying to target a gene called PCSK9 to be able to treat cardiovascular disease.
[01:27:39.160 --> 01:27:41.720] So, they're going to use a lipid nanoparticle.
[01:27:41.720 --> 01:27:49.520] You're going to put in a new PCSK9 gene that's inactive, or you're just going to deliver a vehicle that paralyzes PCSK9?
[01:27:49.520 --> 01:27:56.400] Yeah, the strategy is to paralyze PCSK9 so that you can inactivate PCSK9 and then reduce cholesterol.
[01:27:56.400 --> 01:27:59.360] Any idea how much a treatment like that would cost?
[01:27:59.360 --> 01:28:00.400] I don't know.
[01:28:00.400 --> 01:28:04.800] Initially, maybe tens of thousand dollars.
[01:28:05.120 --> 01:28:16.560] Well, I mean, to be honest, that would be really cheap because the drug that inhibits PCSK9 inhibitor, and there are three of them out there, when they came out, they were $15,000 a year drugs.
[01:28:16.560 --> 01:28:19.360] Now they're $6,000 a year drugs.
[01:28:19.360 --> 01:28:22.320] So call it $10,000 a year as the current drug cost.
[01:28:22.320 --> 01:28:24.560] So you're saying that.
[01:28:24.560 --> 01:28:25.520] Oh, it might be that much more.
[01:28:25.520 --> 01:28:30.560] Okay, again, still relatively inexpensive compared to the whole thing to a lifetime of therapy.
[01:28:30.560 --> 01:28:38.080] Is there any particular reason it costs that much if you actually just look at the drug and don't try to bake in the cost of the last 10 years of developing it?
[01:28:38.080 --> 01:28:53.680] In other words, if a company today came along and said, I'm interested in developing a drug that one and done treats PCSK9 and lowers LDL cholesterol indefinitely, how much would it actually cost to make that drug under GMP conditions at scale?
[01:28:53.680 --> 01:28:58.240] I don't know the exact cost of good, but it's probably much lower than that.
[01:28:58.240 --> 01:29:02.400] But in order to get to that, we need to get the processes developed.
[01:29:02.400 --> 01:29:06.640] And so I think over time, we're definitely going to be able to get down the cost.
[01:29:06.640 --> 01:29:11.760] Large-scale mRNA production, large-scale formulation of nanoparticles.
[01:29:11.760 --> 01:29:15.200] I think those are the processes that people are making really good progress on.
[01:29:15.200 --> 01:29:17.680] So what is driving the cost right now?
[01:29:17.680 --> 01:29:20.320] What is making it expensive today?
[01:29:20.320 --> 01:29:32.360] I think there are a lot of factors, including development cost and also the fact that these drugs, it's the first time developing these modalities of drugs.
[01:29:29.520 --> 01:29:35.800] And so the manufacturing processes need to get developed.
[01:29:36.280 --> 01:29:38.680] And what specifically is it?
[01:29:38.680 --> 01:29:43.000] Presumably we're just making synthetic Cas9 and Cas13 all day long, right?
[01:29:43.000 --> 01:29:45.560] In the laboratory at laboratory scale.
[01:29:45.560 --> 01:29:47.560] I see, but commercially not.
[01:29:47.560 --> 01:29:48.120] Right.
[01:29:48.120 --> 01:29:51.240] So you make your own Cas9 and Cas13 in your lab.
[01:29:51.240 --> 01:29:51.800] We do.
[01:29:51.800 --> 01:29:52.040] Okay.
[01:29:52.040 --> 01:29:52.760] Yeah, we do.
[01:29:52.760 --> 01:29:58.360] You can also buy it from commercial companies, but scale is usually for laboratory use.
[01:29:58.360 --> 01:30:01.080] If you think about it, a mouse is 10 grams.
[01:30:01.080 --> 01:30:04.360] A human can be 40 kilograms.
[01:30:04.360 --> 01:30:07.560] So that's, you know, a thousand times larger in body weight.
[01:30:07.560 --> 01:30:10.440] So you need a thousand times more material.
[01:30:10.440 --> 01:30:16.920] And I think it's developing the process to scale that to that level to be able to treat human beings.
[01:30:16.920 --> 01:30:20.040] That is where the expensive process is.
[01:30:20.040 --> 01:30:24.040] Is there any foundational roadblock to doing this?
[01:30:24.040 --> 01:30:34.600] Or is there a Moore's Law aspect to this where it's just going to get significantly cheaper over time in the same way that transistors just get significantly cheaper over time?
[01:30:34.600 --> 01:30:40.040] I'm not an expert on this, but I think there will definitely be efficiencies from scale.
[01:30:40.040 --> 01:30:45.080] Enzymatic processes for making these RNAs is getting much better.
[01:30:45.080 --> 01:30:46.760] Purity is improving.
[01:30:46.760 --> 01:30:52.360] So all those things, I think they will compound and that will result in significant cost reduction.
[01:30:52.360 --> 01:30:58.840] How much do you follow slash pay attention to the regulation of gene editing?
[01:30:58.840 --> 01:31:23.920] I know that, gosh, sometime in the last six, seven years, there was a very controversial case in China with a scientist who had, it seems somewhat nefariously from my reading of the story, edited the embryos of kids to render them, on the surface, this sounds like a great idea, but rendered them immune to HIV.
[01:31:23.920 --> 01:31:26.160] And I believe one of their parents was HIV positive.
[01:31:26.160 --> 01:31:28.640] This was an IVF case.
[01:31:28.640 --> 01:31:46.000] But it turned out that there was an enormous amount of backlash from the scientific and medical community because I guess, one, he did it without the full consent of the medical community, and also the belief that the risk of transmission of HIV was quite low to the child in the first place.
[01:31:46.000 --> 01:31:55.920] Can you say more about that case in particular, and then also what the current state of genetic manipulation is from a sort of ethical legal standpoint?
[01:31:55.920 --> 01:31:57.920] So this was back in 2018.
[01:31:57.920 --> 01:32:00.880] There was a couple who wanted to have children.
[01:32:00.880 --> 01:32:08.960] The father was HIV positive, and so they wanted to make sure that their children are not infected by HIV.
[01:32:08.960 --> 01:32:16.960] And so scientists made a mutation in the embryos that is a gene called CCR5.
[01:32:16.960 --> 01:32:20.800] A CCR5 mutation is a naturally occurring mutation in the human population.
[01:32:20.960 --> 01:32:28.640] Single digit percent of individuals carry this mutation, and these individuals are naturally immune to HIV infection.
[01:32:28.640 --> 01:32:30.720] This is the magic Johnson mutation.
[01:32:30.720 --> 01:32:31.040] Right.
[01:32:31.040 --> 01:32:36.400] They don't have this specific protein on the surface to allow HIV to bind and enter the cell.
[01:32:36.400 --> 01:32:42.560] And so the scientists edited the embryo, human embryo, to remove this CCR5 gene.
[01:32:42.560 --> 01:32:46.240] But it turned out that that edit wasn't complete.
[01:32:46.240 --> 01:32:48.640] Earlier, we're talking about mosaicism.
[01:32:48.640 --> 01:32:56.480] So, the result of his edit was actually two girls who were mosaics for the CCR5 mutation.
[01:32:56.480 --> 01:33:02.760] So, the editing happened, and the embryos were implanted in the mother and carried to term.
[01:32:59.360 --> 01:33:04.760] And so, two baby girls were born.
[01:33:05.080 --> 01:33:14.520] And when the genetics of these baby girls were analyzed, it turned out that they were highly mosaic, suggesting that the editing wasn't very efficient.
[01:33:14.520 --> 01:33:17.960] And the edit was done at how many cells in theory?
[01:33:17.960 --> 01:33:18.760] Was it one?
[01:33:18.760 --> 01:33:22.840] I don't know exactly how many cells, but very early on, post-fertilization.
[01:33:22.840 --> 01:33:34.760] But to have guaranteed no mosaicism, would you need to do this edit at a single cell and then proliferate only that cell and discard the rest of the blastocytes?
[01:33:34.760 --> 01:33:36.360] In theory, that's the way to do it.
[01:33:36.360 --> 01:33:38.760] And do we know what this person did?
[01:33:38.760 --> 01:33:43.480] I don't know exactly how much of the technical details were released.
[01:33:43.480 --> 01:33:48.840] So, these girls are born mosaic, but what's their phenotype with respect to CCR5?
[01:33:48.840 --> 01:33:52.520] So, some of their immune cells have CCR5 and some don't.
[01:33:52.520 --> 01:33:57.800] So, that means that they can be infected by HIV for those cells that are CCR5 positive.
[01:33:57.800 --> 01:34:05.400] But it probably means that they'll never die of AIDS because they will always maintain a population of T cells that are not susceptible.
[01:34:05.400 --> 01:34:15.320] They could be HIV-infected, but they'll never have a T cell level that falls so low that their CD4 count gets below whatever the threshold is for AIDS.
[01:34:15.320 --> 01:34:15.640] Right.
[01:34:15.960 --> 01:34:20.200] Okay, but the world really responded harshly to this, correct?
[01:34:20.200 --> 01:34:20.680] Correct.
[01:34:20.680 --> 01:34:21.640] That's right.
[01:34:21.640 --> 01:34:23.800] What was the fallout for this scientist?
[01:34:23.800 --> 01:34:29.160] The reason that there was a huge backlash is because there really wasn't any there was no medical need to do this.
[01:34:29.160 --> 01:34:29.480] Correct.
[01:34:29.720 --> 01:34:30.200] That's right.
[01:34:30.200 --> 01:34:31.560] It's not justified.
[01:34:31.560 --> 01:34:39.080] So, the scientific and also medical community, everyone voiced their concern about this ethical issue that just occurred.
[01:34:39.080 --> 01:34:43.400] And I think the scientist was put under house arrest for a while.
[01:34:43.400 --> 01:34:50.480] And this incident also really motivated much more ethical discussions around gene editing.
[01:34:50.640 --> 01:34:54.480] There was ethical discussion before, but it really focused the issue.
[01:34:54.480 --> 01:35:07.760] There were multiple working groups that were established, multinational working groups between the different national academies, U.S., China, UK, as well as the WHO had several working groups.
[01:35:07.760 --> 01:35:16.160] And I think in the U.S., there is legal regulation that prevents a germline modification, but it's certainly not the case internationally.
[01:35:16.480 --> 01:35:20.080] What do you think is being worked on outside of the U.S.
[01:35:20.240 --> 01:35:24.480] in places where there's no regulation with respect to germline mutation?
[01:35:24.480 --> 01:35:35.440] So, for example, are people trying to basically say, look, we're going to get rid of APOE4 isoform and make it APOE3 or 2 isoform?
[01:35:35.440 --> 01:35:37.840] I mean, what is the extent or ambition of people?
[01:35:37.840 --> 01:35:43.120] Are we going to delete LPA genes so that there's no LP little A phenotype?
[01:35:43.120 --> 01:35:49.360] I mean, you could make a case that a lot of these things would unidirectionally improve human health.
[01:35:49.360 --> 01:35:50.800] What are people working on?
[01:35:50.800 --> 01:35:53.200] And then let's talk a little bit about the ethics.
[01:35:53.200 --> 01:35:59.120] I don't know what is happening internationally because people are not publicizing their work.
[01:35:59.120 --> 01:36:09.520] But I think what is known is that there is international consensus that we don't want to do these types of embryo germline editing right now.
[01:36:09.840 --> 01:36:12.320] Even for things where it really matters.
[01:36:12.320 --> 01:36:15.200] So the APOE4 and the LPA don't.
[01:36:15.200 --> 01:36:16.480] You don't have to do that.
[01:36:16.480 --> 01:36:19.040] But what about Huntington's?
[01:36:19.040 --> 01:36:26.160] What about inborn errors of metabolism where children are going to probably die during infancy?
[01:36:26.160 --> 01:36:30.680] Cystic fibrosis, things for which we have no viable treatments.
[01:36:30.680 --> 01:36:37.400] Is the international consensus still that this type of gene editing will not be pursued?
[01:36:37.720 --> 01:36:41.800] I think there's a lot of discussion going on, so it's certainly not a settled issue.
[01:36:41.800 --> 01:36:54.840] But what I think scientists have all come to terms with is that the technology still needs much more validation and development before it's ready for application in this germline editing setting.
[01:36:54.840 --> 01:37:07.640] The efficiency, the specificity, both need to be optimized further in order for there to be any chance of a germline therapy having the intended effect.
[01:37:07.960 --> 01:37:21.240] And how much of that do you think is the main reason for the hesitation here versus the slippery slope argument, which says, yes, of course those applications are absolutely worth pursuing.
[01:37:21.240 --> 01:37:28.200] But if we do that, how far are we from doing APOE4, which may be a little bit gray?
[01:37:28.200 --> 01:37:41.960] And then what about identifying genes that are, frankly, not remotely related to lifespan or medical necessity, but instead relate to kind of the stuff that people talk about in science fiction?
[01:37:41.960 --> 01:37:45.160] Hey, this is a gene that might make my kid a little bit taller.
[01:37:45.160 --> 01:37:52.680] This is a gene that relates to intelligence or some other physical characteristic that is completely irrelevant to their health.
[01:37:52.680 --> 01:38:00.760] Is it more about this is dangerous, we don't know how to do it, or even if we figure this out, we don't want to open up a Pandora's box?
[01:38:01.080 --> 01:38:04.040] I think this is very much the debate that's happening right now.
[01:38:04.040 --> 01:38:11.800] There are patient groups who are advocates for using this, using gene editing in a germline setting to treat disease.
[01:38:11.800 --> 01:38:17.600] Assuming the technology is safe and efficacious, then we should do it because it will alleviate suffering.
[01:38:17.600 --> 01:38:19.440] So there is that argument.
[01:38:19.440 --> 01:38:32.480] There are also people arguing the slippery slope argument, which is if we allow for X and Y, then eventually we're going to be getting into uncharted territories with designer babies and so forth.
[01:38:32.480 --> 01:38:35.120] So that is very much the debate that's happening.
[01:38:35.120 --> 01:38:42.000] I think while that debate is happening, I think it's important to recognize that science is also progressing other fronts.
[01:38:42.000 --> 01:38:48.400] There are likely advances in science that will achieve the same outcome.
[01:38:48.400 --> 01:38:49.600] Without gene editing.
[01:38:49.600 --> 01:38:53.920] And so all those things, I think, are in competition with each other in the ethical debate.
[01:38:53.920 --> 01:38:56.560] And that's why this is such a complicated issue.
[01:38:56.880 --> 01:39:06.560] You're one of three or four people on the planet who not only know more about this technology, but have been personally responsible for it.
[01:39:06.560 --> 01:39:12.000] As such, I can't imagine you get to sit quietly during these debates.
[01:39:12.000 --> 01:39:18.080] Even though you're not an ethicist or a philosopher, I'm sure people want to understand what you think.
[01:39:18.080 --> 01:39:21.200] So how do you think about this?
[01:39:21.200 --> 01:39:26.720] Not from a technical standpoint, which we'll continue to talk about in a minute, but from an ethical standpoint.
[01:39:26.720 --> 01:39:41.040] Where do you think the line should be drawn in what we as a scientific or medical community permit with respect to gene editing in the germline, which is obviously the area where we're, I guess we should have made that point a little more clear at the outset.
[01:39:41.040 --> 01:39:45.040] We're talking about making an edit that will persist in perpetuity.
[01:39:45.040 --> 01:39:47.360] Yeah, I've thought about this issue a lot.
[01:39:47.360 --> 01:40:05.560] The things that are very easy to agree on is that if there is an obvious and important medical benefit, so especially for inborn aerosol metabolism or other known genetic mutations, if the technology is there, it's something that is definitely doable.
[01:40:05.560 --> 01:40:08.920] And I think it's okay to improve the lives of those patients.
[01:40:09.240 --> 01:40:10.760] Is that a mainstream view?
[01:40:10.760 --> 01:40:17.400] Or do you think that there is still a lot in the establishment who have not come around to that yet because of this slippery slope argument?
[01:40:17.720 --> 01:40:20.600] There are certainly people who have not come around to that.
[01:40:20.600 --> 01:40:29.800] And I think when making decisions like that, when making a medical decision like that, it's also important to consider what other alternative methods there may be.
[01:40:29.800 --> 01:40:38.920] For example, pre-implantation genetic testing is another method where you might be able to screen out embryos that have those mutations.
[01:40:38.920 --> 01:40:41.240] Which for IVF, we certainly can.
[01:40:41.240 --> 01:40:45.080] Obviously, for IVF, all of those diseases can be checked.
[01:40:45.080 --> 01:40:45.480] Right.
[01:40:45.800 --> 01:40:55.800] So then it gets to maybe a more complicated question, which is, is there a role for genetic engineering within in utero, for example?
[01:40:55.800 --> 01:41:02.120] Or is that just so technically challenging that it's much easier to focus on just the IVF side of things?
[01:41:02.440 --> 01:41:11.000] So certainly on the disease treatment, monogenic genetic disorder, IVF, I think that that is probably the most likely application.
[01:41:11.000 --> 01:41:14.680] Further down the road, there are some obstacles to solve.
[01:41:14.680 --> 01:41:17.000] One, we don't really understand biology enough.
[01:41:17.000 --> 01:41:23.000] So if you wanted to make our kid 30 IQ points smarter, we don't really know how to do that.
[01:41:23.000 --> 01:41:25.320] And so the biology needs to catch up.
[01:41:25.320 --> 01:41:37.400] But I do think that as society continues to develop, if the science is there and the technology is also there, I think people will opt to do that.
[01:41:37.720 --> 01:41:46.400] Sorry, meaning let's just assume, and by the way, I think this is personally, Feng, my view is I think these problems are way harder than people think.
[01:41:46.720 --> 01:42:07.120] I think these polygenic, nuanced traits like intelligence, athletic performance, resilience, happiness, all of those things I think are infinitely more complicated than we believe and probably just as environmental as they are genetic.
[01:42:07.120 --> 01:42:16.880] So even if you give somebody the right genetic template, if they're not in the right environmental surrounding, they won't necessarily even develop the way you hope.
[01:42:16.880 --> 01:42:17.680] Absolutely.
[01:42:17.680 --> 01:42:19.920] You might give somebody 30 more IQ points.
[01:42:19.920 --> 01:42:25.440] It doesn't translate to a person being demonstrably more intelligent if they aren't in the right environment.
[01:42:25.440 --> 01:42:29.760] That's my prediction: that stuff is way, way, way harder.
[01:42:29.760 --> 01:42:33.040] And the biology is complicated.
[01:42:33.040 --> 01:42:36.400] But it's more complicated than any lay person is thinking about.
[01:42:36.400 --> 01:42:40.640] And also, another thing is biology has a lot of compensation.
[01:42:40.640 --> 01:42:50.080] You might make some mutations that make the person smarter, but that could also increase cancer risk or impair athletic capability, make a person live shorter.
[01:42:50.080 --> 01:42:56.400] Those complicated trade-offs are things that I think people maybe don't fully appreciate.
[01:42:56.400 --> 01:43:01.200] Yeah, for me, it's there's a reason I'm not an ethicist.
[01:43:01.200 --> 01:43:06.800] Things seem much more obvious to me sometimes, which means I'm probably not a nuanced enough thinker on these regards.
[01:43:06.800 --> 01:43:12.480] But when it comes to a disease that's going to kill you with no other treatment, it seems like a no-brainer.
[01:43:12.480 --> 01:43:18.400] Genetic editing and genetic engineering should absolutely be considered a part of the equation.
[01:43:18.400 --> 01:43:37.880] At the other end of the spectrum, when we're talking about things that are truly superfluous, like intelligence, height, athletic performance, eye color, any sort of trait, those seem clearly like a horrible reason, not for the ethical reasons, like, well, what does that say about disparity?
[01:43:37.880 --> 01:43:38.600] Forget all that.
[01:43:38.600 --> 01:43:40.280] It's the reasons you just said.
[01:43:40.280 --> 01:43:43.160] We have no idea how complicated the system is.
[01:43:43.160 --> 01:43:47.560] If you're going to take a huge risk, there has to be a huge payoff.
[01:43:47.560 --> 01:43:50.360] The asymmetry of that strikes me as too much.
[01:43:50.360 --> 01:43:51.720] And then there's the gray area.
[01:43:51.720 --> 01:43:54.600] And I guess on the gray area, I do feel a bit nuanced, right?
[01:43:54.600 --> 01:43:57.720] Which is, should we just get rid of LPA genes?
[01:43:57.720 --> 01:44:08.120] Well, maybe, but then to your point, we have anti-sense oligonucleotide inhibitors that are just around the corner that effectively will get rid of LP little A.
[01:44:08.120 --> 01:44:10.040] They shut the gene off.
[01:44:10.040 --> 01:44:20.040] So why would we risk trying to eradicate this from the population when we can just give you a drug that does the same thing with fewer side effects and less permanence?
[01:44:20.040 --> 01:44:20.920] Exactly.
[01:44:20.920 --> 01:44:24.760] Or you can do it not in the germline, but just in the somatic cells.
[01:44:24.760 --> 01:44:26.760] That could also be beneficial.
[01:44:26.760 --> 01:44:31.560] And then there's yet the other area, which is: look at something like autism.
[01:44:31.560 --> 01:44:45.960] Autism is a genetic disease, despite what many people will have you believe, but the heritability of autism is so high that we know it's largely a genetic disease, but very polygenic, very subject to other things.
[01:44:45.960 --> 01:44:48.280] And do we really want to get rid of it?
[01:44:48.280 --> 01:44:49.000] I don't know.
[01:44:49.000 --> 01:44:49.560] Maybe.
[01:44:49.560 --> 01:44:59.320] I mean, certainly when you see a child that's devastated by autism, who's nonverbal, who's harming themselves, it would be a very logical thing to say.
[01:44:59.320 --> 01:45:08.440] But when you start to move further from that end of the spectrum towards the end of the spectrum where people are quite functional with autism, you might say, well, I don't know.
[01:45:08.440 --> 01:45:11.160] It actually comes with some benefits as well.
[01:45:11.160 --> 01:45:16.320] And do we want to create a homogeneous society where everybody is the same?
[01:45:16.720 --> 01:45:18.000] I don't think we want to.
[01:45:18.000 --> 01:45:20.160] This to me is the crux of sophistication.
[01:45:20.160 --> 01:45:20.560] Yeah.
[01:45:20.560 --> 01:45:21.040] Yeah.
[01:45:21.040 --> 01:45:23.200] Right, whether it be mood.
[01:45:23.200 --> 01:45:27.440] Because that diversity is important for the human race as a whole.
[01:45:27.440 --> 01:45:33.680] That diversity is what will allow the human race to be resilient in the long run.
[01:45:33.680 --> 01:45:36.560] So I want to talk a little bit about your story going back.
[01:45:36.560 --> 01:45:38.000] So you grew up in China.
[01:45:38.000 --> 01:45:40.800] You came to the middle of the country, right?
[01:45:40.800 --> 01:45:41.680] Where did you grow up?
[01:45:41.680 --> 01:45:42.720] I grew up in Iowa.
[01:45:42.720 --> 01:45:46.000] So I moved to Des Moines, Iowa when I was 11 years old.
[01:45:46.000 --> 01:45:47.440] What brought your parents here?
[01:45:47.440 --> 01:45:48.800] My mother came to the U.S.
[01:45:48.800 --> 01:45:49.280] first.
[01:45:49.280 --> 01:45:52.640] She was an exchange scholar in computer science.
[01:45:52.640 --> 01:46:02.560] She had a chance to visit one of the schools in Iowa, and she saw that the educational system in Iowa was much more hands-on as opposed to Zhou memorization.
[01:46:02.560 --> 01:46:08.080] And because of that, she thought I would maybe benefit more from that type of instruction.
[01:46:08.080 --> 01:46:10.080] So she decided to stay in the U.S.
[01:46:10.080 --> 01:46:11.280] and immigrate here.
[01:46:11.600 --> 01:46:17.360] When did you start to become aware of your intellectual abilities, for lack of a better term?
[01:46:17.360 --> 01:46:19.520] I mean, was school always so easy for you?
[01:46:19.520 --> 01:46:23.600] Were you sort of breezing through high school, acing every test put in front of you?
[01:46:23.600 --> 01:46:24.960] Or what was it like?
[01:46:24.960 --> 01:46:27.440] I don't think I'm all that smart.
[01:46:27.440 --> 01:46:29.760] I've always been interested in science.
[01:46:29.760 --> 01:46:31.840] I've always been very curious.
[01:46:31.840 --> 01:46:38.080] So I think I've just always kind of followed my curiosity to do something that is enjoyable and fun.
[01:46:38.080 --> 01:46:41.920] What is really interesting is I didn't like biology at all.
[01:46:41.920 --> 01:46:44.000] You preferred physics and chemistry and mathematics?
[01:46:44.080 --> 01:46:44.720] Yeah, and math.
[01:46:44.720 --> 01:46:46.080] It was more logical.
[01:46:46.080 --> 01:46:50.960] Things that I can understand and build a mental model for, and then be able to predict things.
[01:46:50.960 --> 01:46:54.160] Biology was very much zoomed memorization.
[01:46:54.160 --> 01:46:59.360] So, when I first moved to Iowa in seventh grade, there was life science in middle school.
[01:46:59.360 --> 01:47:05.240] I remember it was really about memorizing trees and dissecting frogs and identifying anatomical parts.
[01:47:05.560 --> 01:47:08.360] It wasn't so much based on logic.
[01:47:08.360 --> 01:47:13.560] It was during seventh grade that I attended a Saturday enrichment class.
[01:47:13.560 --> 01:47:15.800] It was on molecular biology.
[01:47:15.800 --> 01:47:19.960] I didn't know what biology had to do with molecular things, so I went to it.
[01:47:19.960 --> 01:47:26.520] And in that class, that's when I started to learn about the advances in modern biology.
[01:47:26.520 --> 01:47:28.680] The teacher was very passionate.
[01:47:28.680 --> 01:47:33.080] He taught us about DNA, RNA, protein, essential dogma.
[01:47:33.080 --> 01:47:45.800] But he also had us do fun experiments like extracting DNA from strawberries and putting an antibiotic resistance gene into a bacteria so that it can survive on empicillin.
[01:47:45.800 --> 01:47:53.240] He also, at the same time, showed us this, I call it a documentary, but it's actually a Hollywood movie called Jurassic Park.
[01:47:53.240 --> 01:48:12.360] Because if you put yourself in my shoes at the time where you're learning all these molecular biology fundamentals, gene splicing, all that, and then watching Jurassic Park to see all those theories being so tangibly there to make a dinosaur.
[01:48:12.360 --> 01:48:17.960] You know, the movie really felt like a documentary, but it was also just so inspiring and exciting.
[01:48:17.960 --> 01:48:20.440] Because that's about when Jurassic Park came out.
[01:48:20.440 --> 01:48:21.720] It would have been the mid-90s.
[01:48:21.720 --> 01:48:22.200] Yes.
[01:48:22.200 --> 01:48:23.480] Which is when you were in middle school.
[01:48:23.480 --> 01:48:24.760] Yeah, it was 94.
[01:48:24.760 --> 01:48:30.280] So, anyway, so I saw that and I became really interested in learning more about molecular biology.
[01:48:30.280 --> 01:48:37.880] We also learned about gene therapy and the promise of using molecular biology to build rationally designed medicine.
[01:48:37.880 --> 01:48:40.600] So that really captivated my imagination.
[01:48:40.600 --> 01:48:42.360] The teacher was amazing.
[01:48:42.360 --> 01:48:44.040] His name is Ed Pelkington.
[01:48:44.040 --> 01:48:51.200] Was he only the teacher of the enrichment class, or did he also have a regular class that he taught in high school or middle school?
[01:48:51.200 --> 01:48:57.440] He was a consultant for school, and he was working with kids who are interested in science and technology.
[01:48:57.440 --> 01:49:04.640] But he remembered that both myself and also a few other students were really captivated by molecular biology.
[01:49:04.640 --> 01:49:14.080] And so by the time I started 10th grade, he came to us and he said, there's this really cool opportunity that you guys may want to take advantage of.
[01:49:14.080 --> 01:49:22.080] The Iowa Methodist Medical Center had just opened a gene therapy lab, and they have a volunteer program at a hospital.
[01:49:22.080 --> 01:49:27.600] And maybe you guys can apply and specify that you wanted to volunteer in the gene therapy lab.
[01:49:27.600 --> 01:49:29.120] And maybe you get to go there.
[01:49:29.120 --> 01:49:34.480] So we applied, me and three other kids, and we got admitted to the gene therapy lab.
[01:49:34.480 --> 01:49:36.960] They taught us how to do experiments.
[01:49:36.960 --> 01:49:46.960] As you remember, the very first experiment I did, where the scientist had me put the gene for a jellyfish protein called green fluorescent protein.
[01:49:46.960 --> 01:49:50.000] This is a protein that makes jellyfish glow at night.
[01:49:50.000 --> 01:49:52.480] I put this gene into a human cancer cell.
[01:49:52.480 --> 01:49:54.320] Just using an adenovirus?
[01:49:54.640 --> 01:49:56.000] Using just lipid.
[01:49:56.000 --> 01:49:59.760] We wrapped it up with a bit of fat, and then it got absorbed by the cell.
[01:49:59.760 --> 01:50:03.440] And so I did that the afternoon, the day before.
[01:50:03.760 --> 01:50:09.600] And then the following day, I went to the lab, and the scientist took me into this dark microscope room.
[01:50:09.600 --> 01:50:12.640] There was a beam of blue light coming out of the microscope.
[01:50:12.640 --> 01:50:17.280] We put the petri dish with the cells on it, and he told me to look into the eyepiece.
[01:50:17.280 --> 01:50:21.760] And I saw a field of green cells that were fluorescent.
[01:50:21.760 --> 01:50:22.720] That had taken up this gene.
[01:50:22.800 --> 01:50:24.160] It looked alien to me.
[01:50:24.160 --> 01:50:25.280] And you were in 10th grade.
[01:50:25.280 --> 01:50:26.480] I was in 10th grade.
[01:50:26.480 --> 01:50:33.080] And that moment just made me feel so inspired about what we were doing.
[01:50:33.560 --> 01:50:40.360] And this idea that we can use our knowledge to then start to engineer rationally new medicine.
[01:50:40.360 --> 01:50:42.520] From that moment on, I thought science is really cool.
[01:50:42.520 --> 01:50:45.160] I want to do more experimental science.
[01:50:45.160 --> 01:50:54.920] Do you ever just reflect on the direction your life took in response to something as seemingly arbitrary as where your parents chose to move?
[01:50:54.920 --> 01:51:10.920] Your good fortune to be in that enrichment class, and then your good fortune to have a very special teacher who not only did this incredible work with you, but then stayed as a mentor, got you to apply to this program as a sophomore in high school.
[01:51:10.920 --> 01:51:33.000] I mean, like, I'm sure you've heard the arguments for the remarkable set of circumstances that allowed Bill Gates and Paul Allen to be in the right place at the right time from the right high school that had a computer lab that took these people who were naturally quite brilliant, but more importantly, put them in an environment where they could sort of leapfrog ahead of the time.
[01:51:33.000 --> 01:51:35.240] I mean, do you see a parallel there?
[01:51:35.240 --> 01:51:37.400] I think I have been very fortunate.
[01:51:37.400 --> 01:51:44.600] And I think there are a couple of things that I, over time, have developed more and more gratefulness and appreciation for.
[01:51:44.600 --> 01:51:48.680] Number one, I think, is the importance of teachers.
[01:51:48.680 --> 01:52:03.640] I've been very fortunate that throughout my life, I've been just met with many teachers who care about their students and teachers who really want to find as many opportunities as possible to help their students develop.
[01:52:03.640 --> 01:52:06.440] They really cared about nurturing the next generation.
[01:52:06.440 --> 01:52:11.880] And I think having that and having experienced that was really a great fortune of mine.
[01:52:11.880 --> 01:52:14.360] The second, I think, is really about America.
[01:52:14.400 --> 01:52:17.360] I think having been able to immigrate to the U.S.
[01:52:17.360 --> 01:52:30.960] and to go through the American education system where there's really a mirror-based system where you can work hard and be able to learn and develop and have access to opportunities.
[01:52:30.960 --> 01:52:34.240] That is really special about America.
[01:52:34.240 --> 01:52:44.240] So I think education and having a system where it nurtures people who want to develop and work hard, I think has been my great fortune.
[01:52:44.240 --> 01:52:48.400] Do you think that had you grown up in China, it would have been different?
[01:52:48.400 --> 01:52:51.920] Not because obviously people in China don't go on to do amazing things.
[01:52:51.920 --> 01:52:53.760] It also produces great scientists.
[01:52:53.760 --> 01:53:07.280] But just again, speaking to the randomness of this beautiful situation with this amazing teacher and this program and all of the things that followed, do you think you could have still stumbled onto the direction you would have?
[01:53:07.280 --> 01:53:08.640] Maybe even in a different field?
[01:53:08.640 --> 01:53:14.080] I guess what I'm getting at is you're very modest, but obviously you're very special in what you've done.
[01:53:14.080 --> 01:53:23.040] I wonder if you think you would have done great things regardless, or do you think that, no, sometimes you have to be in the right place at the right time?
[01:53:23.040 --> 01:53:25.440] I think faith is important.
[01:53:25.440 --> 01:53:28.000] There was a lot of opportunity in China, too.
[01:53:28.000 --> 01:53:35.920] China from the 1980s all the way to now has developed exponentially, but I don't know where I would have ended up.
[01:53:35.920 --> 01:53:37.680] There were a lot more people in China.
[01:53:37.680 --> 01:53:39.040] Things were competitive.
[01:53:39.040 --> 01:53:40.960] And the education system is different.
[01:53:40.960 --> 01:53:42.800] I like to tinker with things.
[01:53:42.800 --> 01:53:45.760] And in China, it's much more memorization-based.
[01:53:45.760 --> 01:53:51.360] I don't know, but I'm very grateful for having had all of the teachers and opportunities here.
[01:53:51.360 --> 01:53:53.360] What did you study at Harvard for your undergrad?
[01:53:53.360 --> 01:53:55.520] I majored in chemistry and physics.
[01:53:55.520 --> 01:54:05.000] And you did that, obviously, knowing you were interested in genetics, but did you feel that you wanted a greater grounding in the physical sciences because of your ultimate ambitions?
[01:54:05.000 --> 01:54:08.840] Or why did you choose that as opposed to biology or molecular biology?
[01:54:08.840 --> 01:54:09.720] Exactly.
[01:54:09.720 --> 01:54:36.040] I knew I wanted to study biology and engineer biological systems, but I felt that biology was developing so rapidly and new information was accruing on a weekly basis with new papers and new studies that it's probably more beneficial if I study something that's not going to change very much from the time that I enrolled in college to the time I graduate.
[01:54:36.040 --> 01:54:39.960] And so chemistry and physics were much more established fields.
[01:54:39.960 --> 01:54:48.120] And it provided a scientific foundation that I think has been very tremendously helpful as I continue to work in biology.
[01:54:48.120 --> 01:54:58.200] And at the same time, I did work outside of classes in a biological lab where I was practicing biological experiments and reading the latest literature.
[01:54:58.200 --> 01:55:00.120] And I thought that was a nice combination.
[01:55:00.120 --> 01:55:05.960] And when you selected Stanford for your PhD, did you do so because of Carl?
[01:55:05.960 --> 01:55:07.320] Or was there another reason?
[01:55:07.320 --> 01:55:12.840] And then you ultimately, after a year or so of classwork, found your way into Carl's lab?
[01:55:12.840 --> 01:55:15.000] Carl hasn't started at Stanford yet.
[01:55:15.000 --> 01:55:26.280] When I was growing up, especially after moving to Iowa, I read about people like Steve Jobs and Bill Gates and internet revolution that was happening.
[01:55:26.280 --> 01:55:30.360] And so I had a special feeling about Silicon Valley.
[01:55:30.360 --> 01:55:32.120] I was very intrigued by it.
[01:55:32.120 --> 01:55:39.240] And so when I applied to graduate school, I thought Stanford, being in Silicon Valley, would be a really good place to be.
[01:55:39.400 --> 01:55:41.240] I made the decision that way.
[01:55:41.240 --> 01:55:55.680] How did you then make the decision to go back to the East Coast for your postdoc, back to Harvard MIT area, as opposed to stay in the Silicon Valley and pursue your passions there in closer approximation to industry?
[01:55:55.680 --> 01:56:02.640] Although, of course, everybody realizes Boston would clearly be not too far behind the Silicon Valley for biotech industry.
[01:56:02.640 --> 01:56:06.400] I think at the time it was probably the most interesting opportunity.
[01:56:06.400 --> 01:56:10.400] I should mention that Carl Dyseros is a phenomenal mentor.
[01:56:10.400 --> 01:56:25.440] I learned a tremendous amount from him, not only about doing science, but also how to just be a good contributing member of the scientific community with sharing of information and reagents and the transfer of knowledge to as many people as possible.
[01:56:25.440 --> 01:56:30.000] And he was also a great mentor where he just gave me an opportunity to try things.
[01:56:30.000 --> 01:56:36.720] So during graduate school, for many months, I was working on a biofuel project in Carl's lab.
[01:56:36.720 --> 01:56:37.760] What year was this?
[01:56:37.760 --> 01:56:40.240] This was 2007, 2008.
[01:56:40.240 --> 01:56:42.880] So it was an energy crisis, oil crisis.
[01:56:42.880 --> 01:56:44.640] Biofuel was an important thing.
[01:56:44.880 --> 01:56:48.320] That's what Alex Horavius and I were working at at the exact same time.
[01:56:48.320 --> 01:56:48.800] Right.
[01:56:48.800 --> 01:56:49.680] 2008.
[01:56:49.680 --> 01:56:52.240] Around that time, I thought this was an interesting problem.
[01:56:52.240 --> 01:56:54.560] So I explored that in Carl's lab.
[01:56:54.560 --> 01:57:01.120] We thought maybe we could even go and raise venture capital and if we had something working out well.
[01:57:01.120 --> 01:57:05.440] But it turned out that around 2008, the financial crisis also hit.
[01:57:05.440 --> 01:57:10.880] And so a lot of those opportunities at that moment seem to have just vanished.
[01:57:10.880 --> 01:57:15.760] But at that same time, I was contacted by someone from the Harvard Society Fellows.
[01:57:15.760 --> 01:57:18.880] And they said, We liked your work as a graduate student.
[01:57:18.880 --> 01:57:24.000] Would you be interested in coming to the Society of Fellows and just explore science here?
[01:57:24.160 --> 01:57:25.680] Thought that was interesting.
[01:57:25.680 --> 01:57:31.480] They said, We'll give you a stipend, and you're not expected to do anything specific.
[01:57:31.480 --> 01:57:35.240] You can just come and hang out and think about interesting things.
[01:57:29.920 --> 01:57:36.920] So I applied and went there.
[01:57:37.240 --> 01:57:41.320] That's where I started to explore the gene editing technology.
[01:57:41.640 --> 01:57:44.440] So you're barely 40 years old.
[01:57:44.440 --> 01:57:47.720] You're one of the most prominent scientists in your field.
[01:57:47.720 --> 01:57:50.760] Are you optimistic about the state of science?
[01:57:50.760 --> 01:57:55.960] And I'll point out two things that are negative that you don't need to comment on because I don't want to put you in hot water.
[01:57:55.960 --> 01:58:00.040] But there's been a clear attack on meritocracy in the United States.
[01:58:00.040 --> 01:58:04.680] So a lot of the opportunities that served you well might not have served you well today.
[01:58:04.680 --> 01:58:08.680] They might not have been available to you today for various reasons, including your race.
[01:58:08.680 --> 01:58:24.920] And similarly, I think despite all of the remarkable scientific advances during COVID, there was also a loss of public faith in science during COVID, where some of the lines between science and advocacy got blurred greatly, in addition to a whole bunch of other things.
[01:58:24.920 --> 01:58:35.640] So in other words, I guess what I'm saying is there are a lot of things today that don't look as promising as they did maybe 10 years ago vis-a-vis the field and just sort of the state of the art.
[01:58:35.640 --> 01:58:40.120] Despite those things, do you remain as optimistic as ever or do you have concerns?
[01:58:40.120 --> 01:58:41.320] I am an optimist.
[01:58:41.320 --> 01:58:48.040] I think that's the only way to be, because if you're not optimistic, then there's only a downside when you are like that.
[01:58:48.040 --> 01:58:49.800] But I am optimistic about science.
[01:58:50.120 --> 01:58:54.840] I'm so excited about all of the rapid advances that are happening.
[01:58:54.840 --> 01:59:01.640] Knowledge about biology, about the human body, physiology, is accumulating at a very rapid pace.
[01:59:01.640 --> 01:59:06.280] Every week, there are interesting and exciting discoveries that are being reported.
[01:59:06.280 --> 01:59:20.880] And our technologies for studying biological systems are also accelerating with sequencing technology development to protein mass spectrometry to gene editing to new microscopy.
[01:59:21.200 --> 01:59:28.080] That is allowing us to collect new and more rich data cheaper and faster.
[01:59:28.080 --> 01:59:29.360] And then there's AI.
[01:59:29.360 --> 01:59:45.840] With the advance of AI and larger computing platforms, we can analyze and learn and build models around those data that are much more powerful and much more predictive and generative than ever before.
[01:59:46.160 --> 02:00:03.680] And that, combined with future advances in robotics, where we can have automation of experimentation and maybe even building a closed-loop system where AI and with human intervention can design and iterate our experiments rapidly.
[02:00:03.680 --> 02:00:09.600] I think it's going to accelerate science and medicine and human health beyond our imagination.
[02:00:09.600 --> 02:00:11.840] So I think that's really exciting.
[02:00:11.840 --> 02:00:17.280] At the same time, I think we need to continue to motivate people's interest in science.
[02:00:17.280 --> 02

Prompt 6: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 7: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 8: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Prompt 9: Context Setup

You are an expert data extractor tasked with analyzing a podcast transcript. 
I will provide you with part 3 of 3 from a podcast transcript. 
I will then ask you to extract different types of information from this content in subsequent messages. Please confirm you have received and understood the transcript content.

Transcript section:
0 years ago vis-a-vis the field and just sort of the state of the art.
[01:58:35.640 --> 01:58:40.120] Despite those things, do you remain as optimistic as ever or do you have concerns?
[01:58:40.120 --> 01:58:41.320] I am an optimist.
[01:58:41.320 --> 01:58:48.040] I think that's the only way to be, because if you're not optimistic, then there's only a downside when you are like that.
[01:58:48.040 --> 01:58:49.800] But I am optimistic about science.
[01:58:50.120 --> 01:58:54.840] I'm so excited about all of the rapid advances that are happening.
[01:58:54.840 --> 01:59:01.640] Knowledge about biology, about the human body, physiology, is accumulating at a very rapid pace.
[01:59:01.640 --> 01:59:06.280] Every week, there are interesting and exciting discoveries that are being reported.
[01:59:06.280 --> 01:59:20.880] And our technologies for studying biological systems are also accelerating with sequencing technology development to protein mass spectrometry to gene editing to new microscopy.
[01:59:21.200 --> 01:59:28.080] That is allowing us to collect new and more rich data cheaper and faster.
[01:59:28.080 --> 01:59:29.360] And then there's AI.
[01:59:29.360 --> 01:59:45.840] With the advance of AI and larger computing platforms, we can analyze and learn and build models around those data that are much more powerful and much more predictive and generative than ever before.
[01:59:46.160 --> 02:00:03.680] And that, combined with future advances in robotics, where we can have automation of experimentation and maybe even building a closed-loop system where AI and with human intervention can design and iterate our experiments rapidly.
[02:00:03.680 --> 02:00:09.600] I think it's going to accelerate science and medicine and human health beyond our imagination.
[02:00:09.600 --> 02:00:11.840] So I think that's really exciting.
[02:00:11.840 --> 02:00:17.280] At the same time, I think we need to continue to motivate people's interest in science.
[02:00:17.280 --> 02:00:21.040] We need to make sure that the best talent are going into science.
[02:00:21.040 --> 02:00:24.400] Do you worry that we've lost the focus on STEM?
[02:00:24.400 --> 02:00:26.880] I mean, obviously, people talk about the heyday of science, right?
[02:00:26.880 --> 02:00:36.560] During the 1960s, when kids growing up saw the space program, saw the Cold War, the best and the brightest wanted to do science.
[02:00:36.560 --> 02:00:38.160] They wanted to do engineering.
[02:00:38.160 --> 02:00:39.760] Do you feel that's slipping away?
[02:00:39.760 --> 02:00:42.400] And if so, what do you think is necessary to bring it back?
[02:00:42.400 --> 02:00:45.280] How do we get the absolute best people into STEM?
[02:00:45.600 --> 02:00:47.680] I think we can do more for sure.
[02:00:47.680 --> 02:00:51.600] I think we need to get kids more excited about science.
[02:00:51.600 --> 02:01:07.880] We need an education system to support kids who are curious and who have special interests in the science and technical things and to give them opportunities like the ones that I had to really explore those curiosities and interests.
[02:01:07.880 --> 02:01:12.440] When they're young, they are optimistic and they have energy.
[02:01:12.440 --> 02:01:22.200] And if we can nurture that and help them maintain that curiosity and energy, I think that's how we get the best people to continue in science.
[02:01:22.200 --> 02:01:24.120] Yeah, I couldn't agree with you more.
[02:01:24.120 --> 02:01:26.840] And this is not an investment that pays off in a year.
[02:01:26.840 --> 02:01:32.360] You've got to be doing this today, and then you'll get your payoff in a decade and two decades.
[02:01:32.360 --> 02:01:41.240] And sometimes those investments are really hard for society to make because it's hard to say, I got to pay money now for something and I don't get paid back for 10 or 20 years.
[02:01:41.240 --> 02:01:51.240] Those are hard things to accept, but I hope that people listening to this podcast appreciate, certainly in your case, what an enormous value it was for that investment.
[02:01:51.240 --> 02:01:53.400] In many ways, it's a great story of lots of things.
[02:01:53.400 --> 02:01:54.760] It's a story about immigration.
[02:01:54.760 --> 02:01:56.760] It's a story about science education.
[02:01:56.760 --> 02:01:59.080] It's a story about curiosity.
[02:01:59.080 --> 02:02:01.000] It's a story about the American dream.
[02:02:01.000 --> 02:02:04.120] So anyway, I'm really glad we finally got a chance to sit down.
[02:02:04.120 --> 02:02:05.880] It was absolutely worth the wait.
[02:02:05.880 --> 02:02:11.000] I'm excited to follow your undoubted continued success over the coming decades.
[02:02:11.000 --> 02:02:13.480] Thank you so much for having me here today.
[02:02:13.800 --> 02:02:16.760] Thank you for listening to this week's episode of The Drive.
[02:02:16.760 --> 02:02:21.560] It's extremely important to me to provide all of this content without relying on paid ads.
[02:02:21.560 --> 02:02:24.920] To do this, our work is made entirely possible by our members.
[02:02:24.920 --> 02:02:31.880] And in return, we offer exclusive member-only content and benefits above and beyond what is available for free.
[02:02:31.880 --> 02:02:39.160] So, if you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of the subscription.
[02:02:39.160 --> 02:02:42.040] Premium membership includes several benefits.
[02:02:42.040 --> 02:02:50.240] First, comprehensive podcast show notes that detail every topic, paper, person, and thing that we discuss in each episode.
[02:02:50.240 --> 02:02:54.160] And the word on the street is: nobody's show notes rival ours.
[02:02:54.160 --> 02:02:58.240] Second, monthly Ask Me Anything or AMA episodes.
[02:02:58.240 --> 02:03:10.000] These episodes are comprised of detailed responses to subscriber questions, typically focused on a single topic and are designed to offer a great deal of clarity and detail on topics of special interest to our members.
[02:03:10.000 --> 02:03:13.760] You'll also get access to the show notes for these episodes, of course.
[02:03:13.760 --> 02:03:19.920] Third, delivery of our premium newsletter, which is put together by our dedicated team of research analysts.
[02:03:19.920 --> 02:03:28.080] This newsletter covers a wide range of topics related to longevity and provides much more detail than our free weekly newsletter.
[02:03:28.080 --> 02:03:40.000] Fourth, access to our private podcast feed that provides you with access to every episode, including AMA's, Sans the Spiel you're listening to now, and in your regular podcast feed.
[02:03:40.000 --> 02:03:50.240] Fifth, The Qualies, an additional member-only podcast we put together that serves as a highlight reel featuring the best excerpts from previous episodes of the Drive.
[02:03:50.240 --> 02:03:55.600] This is a great way to catch up on previous episodes without having to go back and listen to each one of them.
[02:03:55.600 --> 02:03:58.800] And finally, other benefits that are added along the way.
[02:03:58.800 --> 02:04:07.120] If you want to learn more and access these member-only benefits, you can head over to peteratimd.com forward slash subscribe.
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[02:04:12.640 --> 02:04:18.000] You can also leave us review on Apple Podcasts or whatever podcast player you use.
[02:04:18.000 --> 02:04:31.080] This podcast is for general informational purposes only and does not constitute the practice of medicine, nursing, or other professional healthcare services, including the giving of medical advice, no doctor-patient relationship is formed.
[02:04:29.680 --> 02:04:36.520] The use of this information and the materials linked to this podcast is at the user's own risk.
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[02:04:56.840 --> 02:05:09.640] For all of my disclosures and the companies I invest in or advise, please visit petertamd.com forward slash about where I keep an up-to-date and active list of all disclosures.

Prompt 10: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 11: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 12: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Full Transcript

[00:00:10.960 --> 00:00:13.760] Hey everyone, welcome to the Drive Podcast.
[00:00:13.760 --> 00:00:15.600] I'm your host, Peter Attia.
[00:00:15.600 --> 00:00:24.160] This podcast, my website, and my weekly newsletter all focus on the goal of translating the science of longevity into something accessible for everyone.
[00:00:24.160 --> 00:00:30.960] Our goal is to provide the best content in health and wellness, and we've established a great team of analysts to make this happen.
[00:00:30.960 --> 00:00:36.000] It is extremely important to me to provide all of this content without relying on paid ads.
[00:00:36.000 --> 00:00:46.480] To do this, our work is made entirely possible by our members, and in return, we offer exclusive member-only content and benefits above and beyond what is available for free.
[00:00:46.480 --> 00:00:54.480] If you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of the subscription.
[00:00:54.480 --> 00:01:03.200] If you want to learn more about the benefits of our premium membership, head over to peteratiamd.com forward slash subscribe.
[00:01:04.480 --> 00:01:07.840] My guest this week is Feng Sheng.
[00:01:07.840 --> 00:01:18.640] Feng is a professor of neuroscience at MIT as well as an investigator at the Howard Hughes Medical Institute and a core member of the Broad Institute of MIT and Harvard.
[00:01:18.640 --> 00:01:34.400] He earned his bachelor's degree in chemistry and physics from Harvard University, after which he went on to earn his PhD in chemical and biological engineering at Stanford University, where he worked with one of our previous podcast guests, Carl Deseroth, in developing the technique of optogenetics.
[00:01:34.400 --> 00:01:47.520] From there, he returned to Harvard as a research fellow before starting his own research lab and professorship at MIT in 2011, where he subsequently contributed mightily to the development of the CRISPR-Cas9 system for gene editing.
[00:01:47.520 --> 00:01:59.440] Feng has earned numerous honors and accolades for his work, including being selected for membership in the National Academy of Sciences, the National Academy of Medicine, and the American Academy of Arts and Sciences.
[00:01:59.440 --> 00:02:03.320] He has also a fellow of the National Academy of Inventors.
[00:02:03.320 --> 00:02:10.280] In this episode, we explore the origins of CRISPR and discuss Feng's early work in optogenetics at Stanford.
[00:02:10.280 --> 00:02:17.880] We discuss the foundations of gene editing, discussing the challenges and breakthroughs in the field and how CRISPR revolutionized the process.
[00:02:17.880 --> 00:02:30.760] We talk about the practical implications of CRISPR, such as its potential to treat genetic diseases, the importance of delivery methods, and the current success and limitations in targeting cells like those in the liver and eye.
[00:02:30.760 --> 00:02:36.680] We discuss the ethical considerations of gene editing, touching on the debate surrounding germline modification.
[00:02:36.680 --> 00:02:45.880] Finally, we reflect on Feng's personal journey, the significance of mentorship and education, and where he sees potential for the future of science and genetic medicine.
[00:02:45.880 --> 00:02:51.000] So without further delay, please enjoy my conversation with Feng Sheng.
[00:02:55.480 --> 00:02:59.400] Hey, Feng, thank you so much for detouring your trip and coming through Austin.
[00:02:59.400 --> 00:03:03.560] I've been really looking forward to sitting down with you for, frankly, about a year.
[00:03:03.560 --> 00:03:16.760] So this is a topic that I don't think there's anybody who's heard this podcast who hasn't heard the term CRISPR, but I think very few people can actually explain it and explain what a powerful tool it is.
[00:03:16.760 --> 00:03:26.360] But I do think that before we get there, it would be really helpful to kind of understand a little bit more about your journey on one hand, and then the journey of gene editing as a parallel.
[00:03:26.360 --> 00:03:27.960] Let's start with yours.
[00:03:27.960 --> 00:03:32.600] You and I overlapped a little bit because, I mean, not temporarily, but you were at Stanford.
[00:03:32.600 --> 00:03:35.160] Were you a postdoc in Carl Deseroth's lab?
[00:03:35.160 --> 00:03:37.880] Well, first of all, thank you for having me be on this podcast.
[00:03:37.880 --> 00:03:46.080] I've listened to your podcast on and off, especially when I'm running around or exercising, and it's always I learn a lot from listening to the podcast.
[00:03:46.320 --> 00:03:46.720] Awesome.
[00:03:46.720 --> 00:03:48.240] Thank you for having me here.
[00:03:48.240 --> 00:03:49.760] So, back to Stanford.
[00:03:44.600 --> 00:03:51.280] I was there as a graduate student.
[00:03:51.600 --> 00:03:55.600] I was in the lab of a researcher named Carl Dyseroth.
[00:03:55.600 --> 00:03:57.040] I was there for five years.
[00:03:57.040 --> 00:03:57.520] That's right.
[00:03:57.520 --> 00:03:59.520] You did your PhD there with Carl.
[00:03:59.520 --> 00:04:02.080] Now, as you know, Carl and I were classmates.
[00:04:02.080 --> 00:04:04.080] Carl's been on this podcast.
[00:04:04.080 --> 00:04:14.160] And so, maybe folks who either didn't listen to that podcast or who did but have forgotten, give us kind of a quick summary of the type of work that you and Carl did.
[00:04:14.160 --> 00:04:19.360] When I was working with Carl Dyseroth, we developed a technology called optogenetics.
[00:04:19.360 --> 00:04:30.240] And it's a way of studying brain cells in the brain, how they are connected together, and how they mediate memory, mediate different types of physiological function.
[00:04:30.240 --> 00:04:42.080] The way it works is that we took a gene from a green algae, and this is a gene that senses light and converts it into electrical current in a cell.
[00:04:42.080 --> 00:04:52.640] So, we can put this gene from the green algae right into the brain cells in a mouse, and we can shine blue light or yellow light and control the brain activity in these mice.
[00:04:52.640 --> 00:05:08.880] So, for example, if you wanted to study sleep, you can put this gene into different groups of cells in the brain and stimulate them, and you can find out which ones of these are controlling wakefulness or which ones are causing the mouse to become more sleepy.
[00:05:08.880 --> 00:05:31.400] So, if you do this systematically, one by one, from one type of cell to another type of cell, you can gradually start to put together a picture of how the brain is wired together, and then also what are the different components that govern all sorts of behaviors from sleep and wakefulness to thirst and hunger to memory and even to motivation and happiness.
[00:05:29.840 --> 00:05:34.040] So, it was really fun to be at Stanford and working with Carl.
[00:05:34.280 --> 00:05:38.120] To me, the thing that always stood out about the technique was just the resolution.
[00:05:38.120 --> 00:05:46.840] I don't know what a great analogy would be, but it was resolution at the level of the word rather than the page, if you were thinking about a book, for example.
[00:05:46.840 --> 00:05:47.480] Right.
[00:05:47.480 --> 00:05:53.080] What is incredible about these algal proteins is that they are very, very fast.
[00:05:53.080 --> 00:06:00.280] So, you can show the way the brain cells are able to signal to each other at the action potential level.
[00:06:00.280 --> 00:06:09.240] So, action potentials are these individual signals that they're basically like the phonemes of the speech that one neuron speaks with another neuron.
[00:06:09.240 --> 00:06:12.600] And you can control it at every single phoneme level.
[00:06:12.600 --> 00:06:14.360] And that is pretty cool.
[00:06:14.360 --> 00:06:22.440] And since we're going to be talking a lot about gene editing, what was the technique that you guys used to insert those algal genes into the brains?
[00:06:22.440 --> 00:06:27.240] The way that you would put a gene into the brain is usually by using a virus.
[00:06:27.240 --> 00:06:40.360] So, this is a virus that exists in nature, but we have engineered it by removing everything that is pathogenic about the virus and then replacing those pathogenic genes with the gene that we're trying to put into the brain.
[00:06:40.360 --> 00:06:43.480] So, in this case, it's the gene from the green algae.
[00:06:43.480 --> 00:06:55.480] And by injecting the virus into a brain area that you want to study, the virus will infect all the cells in that region and then make those cells begin to produce this algal protein.
[00:06:55.480 --> 00:07:00.200] So, once the neuron starts to carry this algal protein, it becomes light-sensitive.
[00:07:00.200 --> 00:07:03.960] So, you can turn blue light on it and be able to stimulate it.
[00:07:03.960 --> 00:07:05.320] Yeah, amazing.
[00:07:05.320 --> 00:07:07.320] What year did you finish your PhD?
[00:07:07.320 --> 00:07:08.760] In 2009.
[00:07:08.760 --> 00:07:12.520] Okay, and then you went to MIT or you went to the Broad?
[00:07:12.680 --> 00:07:13.640] Then I went to Harvard.
[00:07:13.640 --> 00:07:13.960] Okay.
[00:07:13.960 --> 00:07:18.640] I was there for about a year, and then I went to MIT and Broad right after that.
[00:07:14.920 --> 00:07:21.200] Okay, and then your attention there sort of turned.
[00:07:21.360 --> 00:07:25.680] You obviously worked some on optogenetics, but what else did you pivot into?
[00:07:26.000 --> 00:07:44.080] As I was working on optogenetics, and especially toward the end of graduate school, I began to realize that one of the biggest bottlenecks facing optogenetics is our ability to insert the algo gene into specific places in the genome.
[00:07:44.080 --> 00:07:53.200] And the reason for that is because in order for us to study different types of brain cells, we need to have very precise targeting of different types of brain cells.
[00:07:53.200 --> 00:07:54.800] Brain cells are not just one type.
[00:07:54.800 --> 00:07:56.160] Neurons is not a single type.
[00:07:56.160 --> 00:07:59.360] There are probably hundreds of different types of brain cells.
[00:07:59.360 --> 00:08:03.520] The way that they're defined is based on their molecular property.
[00:08:03.520 --> 00:08:10.320] So each brain cell, even though they all share the same genome, they have different sets of genes that are turned on.
[00:08:10.320 --> 00:08:17.680] That's why brain cells that control pain sensation versus brain cells that are involved in Parkinson's disease are different.
[00:08:17.680 --> 00:08:26.160] So the way that you would target one or another type of brain cell is by figuring out what are the molecular signatures of that cell.
[00:08:26.160 --> 00:08:38.160] So if you know that gene A is turned on in that brain cell and not in another type of brain cell, then you can insert this algo gene into the region that's controlling gene A.
[00:08:38.160 --> 00:08:42.400] That way it will only get turned on in the first type of neuron.
[00:08:42.400 --> 00:08:49.280] And the way to insert this gene into that precise place in the genome required gene editing.
[00:08:49.280 --> 00:08:51.840] And it was really hard to do at the time.
[00:08:51.840 --> 00:09:00.000] And so I thought maybe if I wanted to get optogenetics to become even more powerful and useful, we need to make gene editing more easy to use.
[00:09:00.840 --> 00:09:09.640] So by the time I went to Harvard, I began to focus more on trying to figure out how do you more easily be able to modify the genome.
[00:09:09.640 --> 00:09:19.080] Yeah, so this is to me where the story gets so interesting because you stumble across a problem that you're trying to solve because of your passion.
[00:09:19.080 --> 00:09:21.320] You've already alluded to why this is so difficult.
[00:09:21.320 --> 00:09:25.640] And I guess maybe just go back and explain something so that the listener understands.
[00:09:25.640 --> 00:09:39.880] Why was it easy to do what you did in Carl's lab, relatively speaking, where you're putting an entire gene into presumably an adenovirus and letting the adenovirus infect the neurons and stick a whole gene in?
[00:09:39.880 --> 00:09:44.360] Why is that a different problem than the one you just described that you started to solve at Harvard?
[00:09:44.360 --> 00:09:46.360] Make sure everybody understands that distinction.
[00:09:46.360 --> 00:09:53.640] The work that I was doing when I was a graduate student with Carl Dysroth is that we were simply trying to insert a gene into brain cells.
[00:09:53.640 --> 00:09:56.680] Meaning you don't get to care where it goes specifically.
[00:09:57.000 --> 00:10:02.360] We can get it into the rough area in the brain, but there are many different types of cells there.
[00:10:02.360 --> 00:10:07.400] And so we weren't as precise in our ability to target those cells.
[00:10:07.400 --> 00:10:19.160] And we also developed some tricks to be able to get it into a specific type of cell, but that was only limited to mice because we can genetically modify mice and it will take a long time.
[00:10:19.160 --> 00:10:27.720] It will take a year or two years to be able to make those mice available to engineer them, but it wasn't generally applicable.
[00:10:27.720 --> 00:10:41.240] And so especially if you think about how to turn optogenetics into a therapeutic to use in the human, we certainly couldn't go in and use those transgenic technologies to make it work in the human brain.
[00:10:41.240 --> 00:10:43.480] So, this was a major problem.
[00:10:43.800 --> 00:10:44.440] Okay.
[00:10:44.440 --> 00:10:54.240] So, let's keep that story on the side for a moment and let's tell in parallel another story, a story that started long before you were even in college, right?
[00:10:54.240 --> 00:10:55.520] When you were a young kid.
[00:10:55.520 --> 00:10:56.080] Right.
[00:10:56.080 --> 00:11:08.400] And it stems from an observation that grew out of a discovery in sequences that existed in bacterial DNA, a certain type of repeating structure that had all these interesting characteristics.
[00:11:08.400 --> 00:11:11.280] So, let's go back to the 80s and tell a little bit of that story.
[00:11:11.280 --> 00:11:19.040] And obviously, I wouldn't be going through this if it wasn't going to quickly converge with your life, change the direction of your life, but let's go back to the 80s.
[00:11:19.040 --> 00:11:33.120] I was born in the 80s, but what was really interesting that you're pointing out is that back in the 80s, there was a group of Japanese researchers who were just looking at DNA sequences of bacteria, and they were looking at E.
[00:11:33.120 --> 00:11:33.840] coli.
[00:11:33.840 --> 00:11:42.080] And what they found is that within some of the genomes, the DNA sequences of these bacteria, there are these regions that are very repetitive.
[00:11:42.080 --> 00:11:44.880] So it will just repeat over and over and over again.
[00:11:44.880 --> 00:11:50.560] Normally, genome sequences are not repetitive because they encode genes and different genes.
[00:11:50.560 --> 00:11:59.440] But here, they found that there are these repeat sequences that are all grouped together, so they're clustered, and they are not tandem repeats.
[00:11:59.440 --> 00:12:06.560] So, it's not repeat one next to each other, but they're interspaced by a short fixed-length gap.
[00:12:06.560 --> 00:12:13.600] And so it's basically A, B, A, C, A, D, A, E, A, G.
[00:12:13.920 --> 00:12:20.720] And so it just continues to repeat itself, but in this regularly spaced pattern.
[00:12:20.720 --> 00:12:24.560] And when they first found it, they had no idea what this sequence was all about.
[00:12:24.880 --> 00:12:33.560] And there's something else about those repeating segments that was quite interesting as well, which is in each direction, whichever way you read them, they were the same.
[00:12:33.560 --> 00:12:34.040] Right.
[00:12:34.040 --> 00:12:35.880] So they're called palindromes.
[00:12:35.880 --> 00:12:37.320] DNA is double-stranded.
[00:12:37.320 --> 00:12:39.560] So there's a top strand and there's a bottom strand.
[00:12:39.560 --> 00:12:42.360] And so this is why they look like a double helix.
[00:12:42.360 --> 00:12:43.960] And so they twist and turn.
[00:12:43.960 --> 00:12:51.400] What is interesting is that when you read these repeat sequences from the top and you read in the reverse way on the bottom, they're almost the same.
[00:12:51.400 --> 00:12:53.080] So they're a palindrome.
[00:12:53.080 --> 00:12:58.920] So you had these palindromic clustered repeats that were interspaced.
[00:12:58.920 --> 00:13:02.600] And if you say that in the right way, you get a few letters.
[00:13:02.600 --> 00:13:07.160] C R I S P R CRISPR.
[00:13:07.160 --> 00:13:12.920] So the name CRISPR, that term was actually coined nearly 40 years ago, right?
[00:13:12.920 --> 00:13:15.240] So CRISPR is really a brilliant acronym.
[00:13:15.240 --> 00:13:21.160] And so C-R-I-S-P-R stands for exactly how these repeats look.
[00:13:21.160 --> 00:13:27.640] Clustered, regularly, interspaced, short, palindromic, repeat.
[00:13:27.640 --> 00:13:30.920] So CRISPR is really brilliant and it's very catchy.
[00:13:30.920 --> 00:13:35.960] But this name wasn't the name that was given to these repeats back in the 80s.
[00:13:35.960 --> 00:13:38.280] In fact, it had many different names.
[00:13:38.280 --> 00:13:40.440] Did that name not come along until the 90s?
[00:13:40.440 --> 00:13:42.200] It didn't come until the early 2000s.
[00:13:42.200 --> 00:13:42.600] Oh, really?
[00:13:42.600 --> 00:13:42.920] Okay.
[00:13:42.920 --> 00:13:43.480] Yeah.
[00:13:43.480 --> 00:13:46.040] Before Francesco Mojito.
[00:13:46.200 --> 00:13:51.080] Mojica came up with that name, but I mean, early 20s, 2000s, yeah.
[00:13:51.080 --> 00:14:01.880] Now, if my reading of the history is accurate, understandably, scientists in the 80s and 90s focused on the repeating segments.
[00:14:01.880 --> 00:14:06.600] Obviously, any good scientist, I think, would look at that and realize this is a very interesting observation.
[00:14:06.600 --> 00:14:08.040] How do we figure out what it is?
[00:14:08.040 --> 00:14:16.400] But the focus was on the repeating segments, what you described as the A in the A, B, A, C, A, D.
[00:14:16.720 --> 00:14:28.640] And I believe it was Francesco who was the first to observe that actually what's interesting is not the repeating segments, it's the seemingly uninteresting segments in between them that are different.
[00:14:28.960 --> 00:14:30.000] Exactly.
[00:14:30.000 --> 00:14:35.520] So, these CRISPR repeats, they have this conserved A sequence.
[00:14:35.520 --> 00:14:38.160] And this A, of course, repeats many, many times.
[00:14:38.160 --> 00:14:40.000] So, it's the most obvious thing.
[00:14:40.000 --> 00:14:44.320] And usually, when we are looking at things, we look for things that have the strongest signal.
[00:14:44.320 --> 00:14:50.080] So, when you have 20, 30 repeats of the same sequence, that's the strongest signal there is.
[00:14:50.080 --> 00:14:53.040] But it turns out that is not an interesting part.
[00:14:53.040 --> 00:15:00.080] The interesting part is actually the non-repeating sequences that's interspaced between pairs of these repeats.
[00:15:00.080 --> 00:15:03.520] So, for Francisco Mojica, he's a Spanish researcher.
[00:15:03.520 --> 00:15:08.720] He's been looking at bacteria and looking at sort of weird sequences for a long time.
[00:15:08.720 --> 00:15:20.240] And what he did back in the early 2000s is that he took these nine-repeat sequences and he just searched against viruses in the bacterial world.
[00:15:20.240 --> 00:15:24.480] A few of them he found matched virus sequences.
[00:15:24.480 --> 00:15:34.000] And so, that was really a breakthrough because it started to highlight that maybe these non-repeating sequences are foreign to the bacteria.
[00:15:34.000 --> 00:15:35.920] They came from somewhere else.
[00:15:35.920 --> 00:15:40.480] And somehow, bacteria acquire them into these repeat patterns.
[00:15:40.800 --> 00:15:56.320] And that really started to launch the CRISPR revolution because that observation and that inference allowed people to start to realize that maybe this has something to do with how bacteria and the viruses are interacting with each other.
[00:15:56.320 --> 00:16:06.600] Yeah, what's interesting when you again look at that story is that when he tried to publish that finding, it was rejected by virtually every significant journal out there.
[00:16:06.600 --> 00:16:10.040] They viewed this as either incorrect, uninteresting, whatever.
[00:16:10.040 --> 00:16:17.480] It was ultimately published, although I don't recall the name of the journal that first published that finding, but it was many rungs below nature science, etc.
[00:16:17.800 --> 00:16:20.120] The irony of science sometimes.
[00:16:20.120 --> 00:16:29.000] Maybe folks, we can remind them just how the human immune system works, because just like bacteria, we are also encountering viruses all the time.
[00:16:29.000 --> 00:16:33.240] Viruses do nothing good for us, just as they do nothing good for bacteria.
[00:16:33.240 --> 00:16:35.880] We have a pretty negative relationship with viruses.
[00:16:35.880 --> 00:16:40.840] All they want to do is use us as hosts to replicate their genomic material, and in the process, they tend to make us sick.
[00:16:40.840 --> 00:16:47.800] So obviously, when a virus infects a bacteria, it's just using its genetic machinery to replicate, and it's going to kill the bacteria.
[00:16:47.800 --> 00:16:50.600] So, as you said, the bacteria needs a tool to fight back.
[00:16:50.600 --> 00:16:57.320] Now, we do it through the creation of antibodies, but how did this story continue to unfold?
[00:16:57.320 --> 00:16:59.400] What were the bacteria doing?
[00:16:59.400 --> 00:17:08.280] Or more to the point, what did Francesco realize was happening with this artifact left behind of viral DNA?
[00:17:08.280 --> 00:17:15.080] In the early days of CRISPR research, there were actually several different converging lines of work.
[00:17:15.080 --> 00:17:21.640] So, there's Francisco Mojica, who's looking at these repeat sequences within the bacterial genome.
[00:17:21.640 --> 00:17:27.240] But then there were also other researchers who began to zero in on a group of genes.
[00:17:27.240 --> 00:17:31.960] So, these are things that are telling the bacteria to make certain types of protein.
[00:17:31.960 --> 00:17:35.400] There are these certain genes that are right next to the repeats.
[00:17:35.400 --> 00:17:43.880] And it took a while for people to begin to associate the genes and the repeat to be together as one single system.
[00:17:43.880 --> 00:17:50.080] But the people who are studying the genes realized that these genes were carrying nucleases.
[00:17:50.080 --> 00:17:56.400] So nucleases are proteins that usually go and cut up either DNA or RNA.
[00:17:56.400 --> 00:18:01.360] And so they initially thought that maybe these genes were involved in DNA repair.
[00:18:01.360 --> 00:18:12.560] So for example, Eugene Kunan, who is a really brilliant bioinformatician, he's been studying these genes and he really started to zero in on what biologically these genes may be doing.
[00:18:12.560 --> 00:18:18.000] But the linkage with these repeats took a little while longer to get associated.
[00:18:18.000 --> 00:18:48.320] But it was really when the discovery that there are these viral sequences in the repeats and that there are these genes that are associated with the repeat that are involved in, say, DNA cleavage, that started to really put together a framework for thinking that maybe this is a system where these viral sequences are working together with the DNA cleaving proteins to go and recognize viral sequences and try to cleave viral sequences.
[00:18:48.640 --> 00:18:53.360] And we can put some names on these things so that people can start to see where the thing is going.
[00:18:53.360 --> 00:19:08.080] So you talked about these genes that were near but at a distance from both the palindromic repeats and the inner spaced segments that we now realize were copies of viral DNA.
[00:19:08.080 --> 00:19:16.000] And as you said, they coded for proteins or enzymes called nucleases, which cut DNA.
[00:19:16.000 --> 00:19:18.160] And similarly, we have helicases.
[00:19:18.160 --> 00:19:22.160] So you have certain enzymes that can unwind the DNA so that they can go in and cut.
[00:19:22.160 --> 00:19:25.840] And these were referred to as CRISPR-associated proteins, correct?
[00:19:25.840 --> 00:19:28.560] Which is abbreviated as CAS.
[00:19:28.880 --> 00:19:33.800] So was CAS1 and CAS2 the first that were identified in that regard?
[00:19:34.120 --> 00:19:35.160] That's exactly right.
[00:19:35.160 --> 00:19:40.200] So these genes that are right next to the CRISPR repeats, they're called CAS proteins.
[00:19:40.200 --> 00:19:45.960] Although they probably underwent many, many different renaming over the course of two decades.
[00:19:45.960 --> 00:19:57.640] But eventually, the community researchers who were studying CRISPR proteins and CRISPR RNA came together and started to really curate these different genes.
[00:19:57.640 --> 00:20:07.640] And so Cas1, Cas2, Cas3, CAS4, et cetera, these are things that are kind of numbered based on, in part, the order that they were discovered.
[00:20:07.640 --> 00:20:12.760] So the most popular protein or the most widely used protein now is called Cas9.
[00:20:12.760 --> 00:20:18.920] And this is one of the Cas proteins that are found among an array of many, many different CRISPR proteins.
[00:20:18.920 --> 00:20:22.600] So yeah, so these CAS proteins work with the CRISPR RNA.
[00:20:22.600 --> 00:20:30.440] And CRISPR-RNA refers to these repeats, which are encoded in DNA, but they are made by bacteria into RNA.
[00:20:30.440 --> 00:20:33.160] And those RNA are called CRISPR RNA.
[00:20:33.160 --> 00:20:34.840] They don't encode protein.
[00:20:34.840 --> 00:20:43.880] They simply are a short guide sequence that directs the CAS protein to find the target virus sequence.
[00:20:43.880 --> 00:20:51.480] So CAS protein, CRISPR-RNA, they together form a complex that goes and provides a defense function for the bacteria.
[00:20:51.800 --> 00:21:11.320] And whereas our defense against a virus is going to be making an antibody and/or activating another type of immune cell with an antigen receptor on it called a T cell, the defense of the bacteria is simply to cut the genetic material of the virus to kill the virus.
[00:21:11.320 --> 00:21:14.200] Obviously, a much more directed approach.
[00:21:14.200 --> 00:21:28.960] Let's walk through two scenarios: first infection, re-infection, and explain the difference between how the bacteria defends itself with special attention to the use of the CRISPR system and the Cas9 enzyme.
[00:21:28.960 --> 00:21:30.800] First infection now, out of the gate.
[00:21:30.800 --> 00:21:31.440] You're an E.
[00:21:31.440 --> 00:21:32.160] coli.
[00:21:32.160 --> 00:21:33.440] I'm a bacteriophage.
[00:21:33.440 --> 00:21:34.880] You've never seen me before.
[00:21:34.880 --> 00:21:35.680] I come along.
[00:21:35.680 --> 00:21:39.760] I've just injected my viral DNA into you.
[00:21:39.760 --> 00:21:42.480] So CRISPR is an adaptive immune system.
[00:21:42.480 --> 00:21:50.800] So it means this system can evolve with the bacteria to be able to accommodate many, many virus infections.
[00:21:50.800 --> 00:21:59.760] So when the virus, which is called a bacteriophage, first infects the bacteria, it will inject its genetic information into the bacteria.
[00:21:59.760 --> 00:22:03.280] The virus is usually very powerful and very potent.
[00:22:03.280 --> 00:22:07.120] It will probably wipe out most of the bacterial population.
[00:22:07.120 --> 00:22:23.840] But for a very small number of cells, maybe one out of a million, the CRISPR system will successfully recognize a piece of the DNA of that virus and begin to insert it into this repeat area in the CRISPR system.
[00:22:23.840 --> 00:22:25.600] And how long a piece is that typically?
[00:22:25.600 --> 00:22:27.280] How many nucleotides in that piece?
[00:22:27.280 --> 00:22:34.400] It's usually 30 letters long, and that is enough for the bacteria to uniquely recognize the virus.
[00:22:34.400 --> 00:22:37.760] So during the first infection, most bacteria die.
[00:22:37.920 --> 00:22:44.400] Very, very small number of them begin to acquire a snippet of the genetic information of this virus.
[00:22:44.400 --> 00:22:46.640] And it inserts it into the CRISPR system.
[00:22:46.640 --> 00:22:52.480] So those bacteria that survived have now acquired immunity against these viruses.
[00:22:52.800 --> 00:23:00.000] And in the process of surviving, what do they have to rapidly do to fend off that first viral infection?
[00:23:00.440 --> 00:23:04.200] So the bacteria has many different defense systems in addition to CRISPR.
[00:23:04.200 --> 00:23:08.040] So in fact, CRISPR is not the first line of defense.
[00:23:08.040 --> 00:23:14.280] There are other things that are also very powerful technologies now that are called restriction endonucleases.
[00:23:14.280 --> 00:23:17.800] These are proteins or enzymes that bacteria use.
[00:23:17.800 --> 00:23:23.480] They don't adapt, so they don't evolve, but they recognize fixed letter sequences.
[00:23:23.480 --> 00:23:28.360] And sometimes these will always get activated first and try to fend off the virus.
[00:23:28.360 --> 00:23:32.360] But if it doesn't, then there are a host of other defense systems.
[00:23:32.360 --> 00:23:34.680] One of these will eventually work.
[00:23:34.680 --> 00:23:41.080] But unfortunately for the bacterial population, these things don't come in quick enough, and that's why most of the cells die.
[00:23:41.080 --> 00:23:49.240] But the few where these things were able to keep up with the virus, that's what allows the CRISPR systems to begin to acquire the genetic information.
[00:23:49.240 --> 00:23:51.720] So now let's talk about that subsequent infection.
[00:23:51.720 --> 00:23:56.840] So we've obviously, through a very Darwinian mechanism, selected a subset of the E.
[00:23:56.840 --> 00:24:08.200] coli in this case that indeed are able to not just survive with their first and second line defense against the bacteriophage, but now they've also developed the memory, so to speak, the way we would use the term.
[00:24:08.200 --> 00:24:25.080] So now it's a month later, the same phage comes along, infects you, inserts its genetic material, but now you actually have interspersed between your CRISPR repeats, you actually have the 30 nucleotides that match a sequence within that virus.
[00:24:25.080 --> 00:24:29.000] So now, how do you spring into action to resist this infection?
[00:24:29.000 --> 00:24:35.080] So after first infection, in a way, the bacteria has been vaccinated against this virus.
[00:24:35.080 --> 00:24:41.160] So the second time when this virus comes around, it will inject its genetic information into the bacteria.
[00:24:41.160 --> 00:24:47.440] But now the bacteria in the CRISPR repeat area has a signature of this virus.
[00:24:47.760 --> 00:25:01.120] So the repeat area will get turned on and it will start to make CRISPR RNA that carry a 30-base pair long or 20-base pair long guide that's able to recognize the incoming virus.
[00:25:01.120 --> 00:25:05.040] So the Cas protein will bind to this CRISPR RNA.
[00:25:05.040 --> 00:25:10.560] They will go and try to search along all the DNA sequences in the bacteria.
[00:25:10.560 --> 00:25:17.920] When it finds a match in the virus's DNA, it will activate the nuclease and it will cut the DNA.
[00:25:17.920 --> 00:25:21.280] And approximately how many base pairs are in viral DNA?
[00:25:21.280 --> 00:25:22.400] Depends on the virus.
[00:25:22.400 --> 00:25:28.160] They can be small, maybe 10,000 letters, or they can be long, 100,000 or even longer.
[00:25:28.160 --> 00:25:32.400] Which, again, just for context, so people understand, we have about 3 billion.
[00:25:32.400 --> 00:25:36.640] So we're still talking about tiny, tiny, tiny amounts of DNA.
[00:25:36.640 --> 00:25:40.560] And by the way, does it differ if it's an RNA virus versus a DNA virus?
[00:25:40.560 --> 00:25:41.920] Is the process identical?
[00:25:41.920 --> 00:25:43.440] It's very similar.
[00:25:43.760 --> 00:25:58.880] So the re-infection, they tend to make pretty quick work because now, once the bacteria is able to make the Cas protein, which it can make really quickly, in between the CRISPR segment, it makes the RNA segment to guide it and match it.
[00:25:58.880 --> 00:26:06.400] And really, that's kind of a CRISPR RNA plus a truncated other version of an RNA that holds the RNA in the Cas protein.
[00:26:06.560 --> 00:26:08.640] We call that whole thing the guide RNA.
[00:26:08.640 --> 00:26:23.760] How quickly can that Cas9 enzyme holding the guide RNA, how quickly in actual time, does it go through the entire sequence of viral DNA until it finds its place to land and cleave?
[00:26:23.760 --> 00:26:25.360] That process is probably pretty fast.
[00:26:25.360 --> 00:26:36.840] I don't know exactly how quick it is, but also it's important to recognize that in a single bacteria, there are many, many copies of the Cas9 along with the guide RNA.
[00:26:37.160 --> 00:26:47.480] And so that means once the virus comes in, there are many, many copies of Cas9 that are simultaneously in parallel searching against the virus DNA to see whether or not there's a match.
[00:26:47.480 --> 00:26:58.200] And this is why the system is so powerful, because it's able to very quickly, in a parallel fashion, find the match and then inactivate the virus within minutes.
[00:26:58.200 --> 00:27:04.280] And there's something else that is pretty unique about where those cuts take place.
[00:27:04.280 --> 00:27:11.080] When we bring in the viral memory, it always begins with three particular nucleotides.
[00:27:11.080 --> 00:27:12.680] What's the significance of that?
[00:27:12.680 --> 00:27:13.400] Right.
[00:27:13.400 --> 00:27:21.400] So what you're talking about is what CRISPR scientists call PEM sequence, PAM, or proto-spacer JSON motif.
[00:27:21.560 --> 00:27:22.680] Just a jargon.
[00:27:22.680 --> 00:27:32.680] What is significant about that is that it is a sequence that is only found in the bacterial virus's genome, but not in the bacteria's genome.
[00:27:32.680 --> 00:27:43.800] When the bacteria acquires a piece of the virus's sequence and sticks it into its own genome, one question is: couldn't the CRISPR system go and recognize the bacteria's genome?
[00:27:43.800 --> 00:27:49.400] It's sort of like, couldn't you turn the weapon on yourself and cut your own DNA and kill yourself?
[00:27:49.400 --> 00:27:50.200] Exactly.
[00:27:50.200 --> 00:27:55.720] Yeah, so how does it avoid this self-targeting or autoimmunity against itself?
[00:27:55.720 --> 00:27:57.720] And this is where the PAM sequence comes in.
[00:27:57.720 --> 00:28:09.560] The PAM sequence is in the viral genome, is right next to the sequence that is acquired into the CRISPR system, but it itself is not acquired into the bacterial genome.
[00:28:09.560 --> 00:28:16.720] And so, what you see in the bacterial genomes, CRISPR repeat, is just the recognition sequence, but no PIM.
[00:28:14.200 --> 00:28:21.120] And so, Cas9 requires the PIM to activate recognition and cleavage.
[00:28:21.440 --> 00:28:26.160] And so, without a PEM, it doesn't cleave itself, but it's still able to target a virus.
[00:28:26.480 --> 00:28:40.880] So, Hang, does this bring us more or less up to speed with the state of the art when you turned your attention to how I can create a finer resolution for gene editing for my optogenetics problem?
[00:28:40.880 --> 00:28:43.280] Is this about where the state of the art was?
[00:28:43.280 --> 00:28:49.200] Yeah, so I started to work at MIT and the Broad Institute in 2011.
[00:28:49.200 --> 00:28:55.040] And so, when I first started, I went to a scientific presentation and they were talking about CRISPR.
[00:28:55.040 --> 00:28:58.000] And they mentioned that CRISPRs are nucleases.
[00:28:58.000 --> 00:29:04.400] Because I was thinking about nucleases and gene editing at the time, when I heard that word, it just got me interested.
[00:29:04.400 --> 00:29:08.640] And so, I went on to Wikipedia and looked at what CRISPR is.
[00:29:08.640 --> 00:29:17.920] And Francis Mohica and Simon Moiniu and Rodal Berengu, they had just published the very early studies on the Cas9 system.
[00:29:17.920 --> 00:29:20.080] Back then, it was still called Cas5.
[00:29:20.080 --> 00:29:22.800] It was only later on renamed to be Cas9.
[00:29:22.800 --> 00:29:27.680] But there were all these papers that if you read them, there aren't too many of them.
[00:29:27.680 --> 00:29:35.520] You can piece together the information and you can get a sense that this is an RNA-guided DNA targeting and cleaving enzyme.
[00:29:35.520 --> 00:29:44.800] And at the time, there were other gene editing systems that were being worked on by researchers in the field, something called zinc finger nuclease or talon.
[00:29:44.800 --> 00:29:50.720] And these are systems that use proteins to recognize DNA, but not using RNA to recognize DNA.
[00:29:51.040 --> 00:29:57.040] Let's talk a little bit about both of those, because they were the state of the art up until 15 years ago.
[00:29:57.040 --> 00:30:04.040] And I want to understand both how they work and why they may not have been sufficient for your application.
[00:30:04.280 --> 00:30:08.040] In other words, why were you looking for something beyond two techniques that already existed?
[00:30:08.360 --> 00:30:16.360] When I first became interested in gene editing in the late 2000s, there were people already developing gene editing technologies.
[00:30:16.360 --> 00:30:21.080] In fact, there were multiple iterations of technologies that came around.
[00:30:21.080 --> 00:30:24.360] There's something called mega nuclease, which was very, very early.
[00:30:24.360 --> 00:30:27.800] And then what got me excited was a New York Times article.
[00:30:27.800 --> 00:30:30.040] I think it was 2008 or 2009.
[00:30:30.040 --> 00:30:33.160] It talked about a system called zinc finger nuclease.
[00:30:33.160 --> 00:30:45.640] There's a company in California that's called Sangamo Biosciences, and they were already developing zinc finger nucleases for gene therapy to be able to go and edit DNA and treat disease.
[00:30:45.640 --> 00:30:56.440] But zinc finger was a really challenging system for scientists to adapt and use because it required very sophisticated protein engineering.
[00:30:56.440 --> 00:31:00.280] The way it works is that zinc fingers are protein domains.
[00:31:00.280 --> 00:31:03.000] They're just one glob of protein.
[00:31:03.000 --> 00:31:07.960] Each finger, each zinc finger, can recognize three letters of DNA.
[00:31:07.960 --> 00:31:09.560] And they occur in nature.
[00:31:09.560 --> 00:31:15.480] So you can find them in naturally occurring DNA binding proteins called transcription factors.
[00:31:15.480 --> 00:31:26.840] And they allow transcription factors to go and recognize different genes in the genome to modulate their activity, either turn them on or turn them off or change how much they are expressed in the cell.
[00:31:26.840 --> 00:31:31.240] But how do you get specificity when you are only recognizing three nucleotides?
[00:31:31.240 --> 00:31:36.840] The probability of those three nucleotides showing up seems pretty likely across the genome.
[00:31:36.840 --> 00:31:37.400] Exactly.
[00:31:37.400 --> 00:31:41.720] So, nature has solved this problem by forming zinc finger arrays.
[00:31:41.720 --> 00:31:44.560] So, they tether multiple fingers together.
[00:31:44.560 --> 00:31:47.920] Ah, and all of them have to hit their target of three nucleotides.
[00:31:44.280 --> 00:31:48.800] Correct, that's right.
[00:31:49.040 --> 00:31:53.200] So, if you have an array of three fingers, they recognize nine letters.
[00:31:53.280 --> 00:31:56.000] If you have an array of six fingers, then that's 18 letters.
[00:31:56.000 --> 00:32:02.560] So, in a complicated genome, our genome with 3 billion letters, 18 would give us uniqueness.
[00:32:02.560 --> 00:32:07.200] So, 18 can allow you to find or define just a single position.
[00:32:07.200 --> 00:32:11.360] And that's because 4 to the power of 18 is a big enough number?
[00:32:11.360 --> 00:32:12.880] It's bigger than 3 billion.
[00:32:12.880 --> 00:32:13.280] Yeah.
[00:32:13.600 --> 00:32:14.080] Okay.
[00:32:14.640 --> 00:32:18.720] So, let's now talk about the talons.
[00:32:18.720 --> 00:32:24.080] First of all, what is that technique and why was that not necessarily going to serve your purpose?
[00:32:24.400 --> 00:32:31.040] So, the challenge with zinc finger is that you have engineered the fingers to recognize different combinations of three letters.
[00:32:31.040 --> 00:32:41.040] And you have to make sure that when you tether them together to make a zinc finger array, they can recognize what you intend to recognize in that multiple three fashion.
[00:32:41.040 --> 00:32:44.800] That turned out to be really cumbersome, and usually it doesn't work very well.
[00:32:44.800 --> 00:32:52.480] So, then this other system that you mentioned called transcription activator-like effector T-A-L-E nuclease was developed.
[00:32:52.480 --> 00:32:55.360] So, these systems came also from bacteria.
[00:32:55.360 --> 00:33:01.440] They came from a specific pathogenic bacteria called Xanthomonas or Rhizi.
[00:33:01.440 --> 00:33:05.520] So, it lives on rice plants and is a major rice pathogen.
[00:33:05.520 --> 00:33:14.800] The way these proteins work is that they're injected by the bacteria that's trying to colonize the plant into the plant cell.
[00:33:14.800 --> 00:33:22.720] And once it gets inside, it homes into the genome of the plant cell, finds a gene, and then starts to turn it on.
[00:33:22.720 --> 00:33:28.400] When it turns it on, it allows the plant to become more susceptible to the bacterial colonization.
[00:33:28.400 --> 00:33:33.960] And so, it allows the bacteria to be able to more successfully survive on this whole plant.
[00:33:29.840 --> 00:33:36.600] It's a major pathogenic system.
[00:33:36.920 --> 00:33:45.320] But what is really cool about talons or tails is that they recognize DNA in a very programmable fashion.
[00:33:45.320 --> 00:33:51.560] These proteins, they have repeat domains, just like zinc fingers, that form an array.
[00:33:51.560 --> 00:33:55.640] But individual domains recognize single DNA letters.
[00:33:55.640 --> 00:34:04.200] And so you can find these tail proteins in the bacteria that have 12 or 16 or even 20 different repeats.
[00:34:04.200 --> 00:34:11.800] And so they can recognize long stretches of 12 or 16 or 18 or 20 DNA letters in the plant genome.
[00:34:11.800 --> 00:34:13.400] Plant genome is also large.
[00:34:13.400 --> 00:34:16.840] They are sometimes two, three times the size of our genome.
[00:34:16.840 --> 00:34:21.640] So recognizing long sequences is important for achieving precision.
[00:34:21.640 --> 00:34:23.480] That was the tail system.
[00:34:23.480 --> 00:34:39.080] So what is really cool is that Ula Bonus, who is a researcher in Germany, and also Adam Bagnanove, who is a researcher at Iowa State at the time, they discovered that there's a specific code for how these tail proteins recognize DNA.
[00:34:39.080 --> 00:34:49.000] It turns out that within each one of these repeats, there are two amino acid letters that correspond with a specific DNA letter that it binds to.
[00:34:49.000 --> 00:35:02.600] And so if you take any tail protein and you just dial in different combinations of these two amino acids in each one of the domain, you can specify what DNA sequence this tail protein is able to recognize.
[00:35:02.600 --> 00:35:06.600] So it turned out to be much more easy to use than zinc fingers.
[00:35:06.920 --> 00:35:09.800] So were you satisfied with that?
[00:35:09.800 --> 00:35:11.080] I mean, obviously you weren't.
[00:35:11.080 --> 00:35:14.520] You were sitting there looking up Wikipedia for what CRISPR is when you heard about it.
[00:35:14.640 --> 00:35:21.920] So something must have said to you, hey, as good as the talons are and as much as they're an improvement over zinc fingers, they're still not good enough.
[00:35:21.920 --> 00:35:22.560] Why was that?
[00:35:22.560 --> 00:35:23.680] Why were they not good enough?
[00:35:24.000 --> 00:35:26.000] Zinc fingers were really hard to use.
[00:35:26.000 --> 00:35:37.360] When I tried to engineer it, it was very hard, very difficult, almost impossible for just a single researcher to get something that would recognize the DNA sequence that you're trying to get it to recognize.
[00:35:37.360 --> 00:35:38.800] So it was very hard to use.
[00:35:38.800 --> 00:35:50.960] Talons, on the other hand, were easier, but the repetitive nature of these proteins made it quite cumbersome to engineer new proteins to recognize the sequence you're trying to edit.
[00:35:50.960 --> 00:36:03.280] So for example, if you wanted to be able to modify a specific gene, it could take you maybe several weeks or even a couple months to be able to successfully make one of these tail proteins.
[00:36:03.280 --> 00:36:06.240] And when you do that, usually it works, but not always.
[00:36:06.240 --> 00:36:09.120] And sometimes it's not very effective.
[00:36:09.120 --> 00:36:17.120] Is that because it's so difficult to predict the folding structure of the protein, even if you know its sequence?
[00:36:17.120 --> 00:36:18.720] Why would it take that long?
[00:36:18.720 --> 00:36:24.400] It's long because from a technical perspective, these sequences are just very hard to work with.
[00:36:24.400 --> 00:36:28.640] Because they're very similar to each other, they're prone to recombination.
[00:36:28.640 --> 00:36:35.680] So in order to get a tail to work, you have to, in a very precise way, put every domain in the correct order.
[00:36:35.680 --> 00:36:41.200] Because if you're trying to recognize A, G, T, C, you have to put it together in A, G, T, C.
[00:36:41.200 --> 00:36:43.520] You can't have A, C, G, T.
[00:36:43.520 --> 00:36:45.120] It just wouldn't work.
[00:36:45.120 --> 00:36:52.880] So to put repetitive sequences together and line them up in exactly the order you want, that is really challenging to do.
[00:36:52.880 --> 00:36:55.120] It's possible, but it's very challenging.
[00:36:55.440 --> 00:36:56.400] So it's very interesting.
[00:36:56.400 --> 00:36:58.560] I mean, I think it's worth just sort of taking a step back.
[00:36:58.560 --> 00:37:14.120] Most people can sort of remember what they were thinking 10 years ago, 12, 15 years ago, and the human genome was sequenced nearly 25 years ago, and the promise of gene therapy was hailed as right around the corner.
[00:37:14.120 --> 00:37:19.880] And yet here we are a decade, more than a decade after the sequencing of the human genome.
[00:37:19.880 --> 00:37:29.320] And it doesn't appear that gene therapy through gene editing was any closer than it really was from a practical standpoint a decade earlier.
[00:37:29.320 --> 00:37:31.080] I think that's a bit of a disconnect for people.
[00:37:31.080 --> 00:37:42.600] I think most people who necessarily aren't in a lab would be surprised to understand that simply knowing what the sequence of genes are, I mean, A, we've talked about this a lot on the podcast.
[00:37:42.600 --> 00:37:45.800] We still have no idea what most of these genes do anyway.
[00:37:45.800 --> 00:37:53.000] We have no idea why there are coding segments and the majority of the DNA is non-coding segments.
[00:37:53.000 --> 00:37:57.960] And yet some of these non-coding segments are where mutations exist that result in disease.
[00:37:57.960 --> 00:38:00.200] I mean, all this stuff's still a bit of a mystery.
[00:38:00.200 --> 00:38:27.720] But here you're sort of getting at the central or maybe the most important or jugular question, which is if a person has a disease like cystic fibrosis or sickle cell anemia, where we really know in unambiguous terms where on the DNA, where in the gene this lies, where in the DNA, we know what the substitution is, we know which C was turned into a G or a T.
[00:38:27.720 --> 00:38:30.360] And all we need to be able to do is go in there and fix it.
[00:38:30.360 --> 00:38:33.160] That was something we couldn't do 10 years ago.
[00:38:33.160 --> 00:38:35.480] I think for many people, that's quite surprising.
[00:38:35.480 --> 00:38:40.680] And I know that that's not the problem you were trying to solve, but it's obviously the world you've created.
[00:38:40.680 --> 00:38:43.240] So let's talk about those steps down that road.
[00:38:43.240 --> 00:38:49.040] So you figure out what CRISPR is, you bring yourself up to the state of our discussion today.
[00:38:49.040 --> 00:38:50.240] What's the next thing?
[00:38:50.240 --> 00:38:51.600] The next thing in terms of CRISPR?
[00:38:51.760 --> 00:38:53.600] Yeah, the next thing that you're starting to now pursue.
[00:38:53.600 --> 00:38:58.320] I mean, how are you now going about solving your problem for your good?
[00:38:58.480 --> 00:39:04.560] What point do you realize you're working on a problem that has a far bigger application than just solving your problem?
[00:39:04.560 --> 00:39:13.040] I think pretty quickly, since I started working gene editing, because the Human Genome Project was completed in the early 2000s.
[00:39:13.040 --> 00:39:25.760] And with the human genome having been sequenced, and then also with DNA sequencing technology becoming cheaper and faster, scientists were able to start to sequence many, many more genomes.
[00:39:25.760 --> 00:39:37.040] And so they can start to make comparisons between healthy individuals and also people who are affected by specific diseases to see what's different between their genomes.
[00:39:37.040 --> 00:39:43.120] And by doing that comparison, they can identify the differences that may be causal for disease.
[00:39:43.120 --> 00:39:54.720] And so to date, based on genetic analysis, researchers have probably identified more than 5,000 genetic mutations that have a direct causative role in disease.
[00:39:54.720 --> 00:39:57.120] And so these are called genetic diseases.
[00:39:57.120 --> 00:40:01.840] They are usually affecting a small population of individuals.
[00:40:01.840 --> 00:40:08.480] They're not as common as things like cancer or diabetes or what people call complex or complicated diseases.
[00:40:08.480 --> 00:40:14.000] But nevertheless, these are the ones where we know the exact genetic cause.
[00:40:14.000 --> 00:40:21.120] And so the tantalizing idea is then if you know the mutation in the genome, why not just go and fix it?
[00:40:21.120 --> 00:40:23.280] And so that's where gene editing comes in.
[00:40:23.280 --> 00:40:27.440] And people have since the very beginning trying to realize this idea.
[00:40:27.440 --> 00:40:30.360] They were trying to work on it using mega-nucleases.
[00:40:29.440 --> 00:40:32.600] They were trying to solve this using zinc finger nucleases.
[00:40:33.560 --> 00:40:38.200] They were certainly trying to use talents to also treat diseases this way.
[00:40:38.200 --> 00:40:41.560] But the challenge is that they weren't very efficient.
[00:40:41.560 --> 00:40:48.680] And it was also difficult to apply them to be able to treat a disease with sufficient amount of efficacy.
[00:40:48.680 --> 00:40:57.000] When CRISPR came along, especially with CAS9, it was much easier to be able to design strategies to edit DNA.
[00:40:57.000 --> 00:41:03.800] And that made it much more feasible for many, many groups to really start to work on this idea.
[00:41:03.800 --> 00:41:07.240] Were you finished with your postdoc and now starting your own lab?
[00:41:07.240 --> 00:41:09.800] Yeah, I had just started my lab at MIT.
[00:41:09.800 --> 00:41:12.600] Okay, so you've got how many PhDs in your lab?
[00:41:12.600 --> 00:41:16.280] I probably had maybe 10 PhD students.
[00:41:16.280 --> 00:41:18.280] Okay, and a couple of postdocs to boot.
[00:41:18.280 --> 00:41:18.920] Yeah.
[00:41:19.240 --> 00:41:25.720] And these people have all come to you presumably because they're interested in optogenetics.
[00:41:25.720 --> 00:41:27.720] Yeah, they were interested in different things.
[00:41:27.720 --> 00:41:33.480] So at what point do you come back into the lab and say, I'm going to hit pause on the optogenetics problem.
[00:41:33.480 --> 00:41:36.600] I'm going to kind of go down this CRISPR path for a while.
[00:41:36.600 --> 00:41:41.400] When I first started my lab, I was already focused on the gene editing problem.
[00:41:41.400 --> 00:41:54.920] So when students came to me, even though they came to me wanting to work on optogenetics, I had to convince them that there's this other problem that is also interesting, and maybe we can try to work together and make a difference there.
[00:41:54.920 --> 00:42:02.040] And so I started to try to tell them about CRISPR, tell them about gene editing and all the potential applications.
[00:42:02.360 --> 00:42:04.040] So, keep going down the story now.
[00:42:04.040 --> 00:42:06.520] So, it's the early 2010s.
[00:42:06.520 --> 00:42:10.920] What are the next steps that you take to develop this technology?
[00:42:10.920 --> 00:42:14.800] So, when I first started my lab, I was working on talons.
[00:42:14.440 --> 00:42:21.840] And then, very quickly, I had learned about CRISPR, and I started to also get CRISPR projects going in the lab.
[00:42:22.160 --> 00:42:25.360] And we worked on both systems for a while at the same time.
[00:42:25.360 --> 00:42:34.160] We pretty quickly realized that Talons were difficult to use because of the cumbersome nature of how to make them.
[00:42:34.160 --> 00:42:39.760] And then, because of that, the promise of CRISPR was much more apparent.
[00:42:39.760 --> 00:42:42.560] So, maybe I'll give another analogy.
[00:42:42.560 --> 00:42:54.480] So, we now have a mobile phone, and on a phone, there are many different apps: apps that help you book trips, apps that help you send messages to your friends and family, apps that allow you to take photos.
[00:42:54.480 --> 00:42:56.640] You have a phone, and a phone can do everything.
[00:42:56.640 --> 00:42:58.320] You just load an app onto it.
[00:42:58.320 --> 00:43:05.040] With Talons or Zinc Fingers, the analogy would be you have to build a different device for each one of these functions.
[00:43:05.040 --> 00:43:07.600] Meaning, you would need a different phone for each app.
[00:43:07.600 --> 00:43:08.720] You need different hardware.
[00:43:08.720 --> 00:43:14.000] Yeah, so for every gene you're trying to target, you have to build a brand new protein to be able to target that gene.
[00:43:14.000 --> 00:43:18.320] And that is a very cumbersome and not very effective process.
[00:43:18.320 --> 00:43:23.200] With CRISPR, the promise is that CRISPR is like the smartphone.
[00:43:23.200 --> 00:43:27.120] You can load software onto it to recognize different genes.
[00:43:27.120 --> 00:43:29.840] And the software is the CRISPR RNA.
[00:43:29.840 --> 00:43:41.040] These RNAs are very easy to chemically synthesize, and you can define the gene by reading off of the sequence of the gene, which is already completed through the Human Genome Project.
[00:43:41.040 --> 00:43:48.480] So, all that was just a step function improved over the zinc finger and Talon technology.
[00:43:48.480 --> 00:44:04.040] So, we realized that if we can make CRISPR work, not only in a bacteria, but put it into a human cell and get it to recognize genes in the human cell, then we can have a much more powerful and much more democratized gene editing system.
[00:44:04.520 --> 00:44:12.200] So, what was the breakthrough or set of breakthroughs that led to the utility of this?
[00:44:12.200 --> 00:44:16.680] And did it start with let's just silence a gene first?
[00:44:16.680 --> 00:44:22.760] I mean, let's figure out how to go in and with precision bombing silence one gene.
[00:44:22.760 --> 00:44:29.640] Was that the first problem before the let's actually take a strand of novel DNA and put it in?
[00:44:29.640 --> 00:44:32.200] CRISPR is a natural nucleus.
[00:44:32.200 --> 00:44:37.000] So, in bacteria, it uses the guide RNA to recognize the virus DNA.
[00:44:37.000 --> 00:44:42.920] And then, once it recognizes it, it will cleave the virus DNA, so make a double-stranded DNA break.
[00:44:42.920 --> 00:44:46.360] And so, that's what we're trying to make happen in the human cell.
[00:44:46.360 --> 00:44:55.000] We try to program Cas9 with a guide RNA to go and recognize a specific gene in the human genome and then be able to cut it.
[00:44:55.000 --> 00:44:55.960] Which is valuable.
[00:44:55.960 --> 00:45:00.120] I mean, there are certain cases where overexpression of a gene is pathologic.
[00:45:00.120 --> 00:45:03.800] And if we silence a gene, we fix a disease.
[00:45:03.800 --> 00:45:04.840] Exactly.
[00:45:04.840 --> 00:45:11.640] So, there were a lot of studies done on how breaks in the DNA would get repaired.
[00:45:11.640 --> 00:45:20.920] So, Maria Jason, Jim Haber, they had studied maybe a couple decades before that how DNA repairs would get processed.
[00:45:20.920 --> 00:45:28.600] And so, what they found is that when you make a cut in the DNA, so when you make a break, it will activate repair processes in our cell.
[00:45:28.600 --> 00:45:31.960] So, in fact, our DNAs get DNA breaks all the time.
[00:45:31.960 --> 00:45:36.440] And we have a robust process to be able to fix them to prevent mutations.
[00:45:36.440 --> 00:45:46.960] So, what Maria, Jason, and Jim Haber found is that if you make a cut in the DNA, that cut will activate one of two different repair processes.
[00:45:46.960 --> 00:45:57.440] The first repair process will glue the DNA together, usually correctly, but in a very, very small number of instances, it will introduce a mistake.
[00:45:57.440 --> 00:46:01.040] And that mistake will inactivate the gene.
[00:46:01.040 --> 00:46:04.880] So, it will no longer make the protein product that it's supposed to make.
[00:46:04.880 --> 00:46:08.640] And this is very useful if you wanted to inactivate something in the cell.
[00:46:08.640 --> 00:46:17.040] Sometimes there are mutations that are deleterious, and if you can inactivate that deleterious mutation, then you can make the cell healthy again.
[00:46:17.040 --> 00:46:19.520] And so, that was a very powerful method.
[00:46:19.520 --> 00:46:25.280] The second repair process is called homology-directed repair, HDR.
[00:46:25.600 --> 00:46:32.800] And this relies on a template DNA that carries the sequence that you're trying to repair with.
[00:46:32.800 --> 00:46:43.760] And so, if you make a cut and you also provide a template DNA, then the repair process will copy whatever that's on the template into the DNA break site.
[00:46:43.760 --> 00:46:49.600] And this is a more powerful way to be able to change the DNA sequence in a design fashion.
[00:46:49.920 --> 00:47:00.320] Is there a risk when you cut the DNA and it repairs that it repairs in a manner that remains pathologic, even if it's distinct from the path that was already there?
[00:47:00.320 --> 00:47:04.080] It is possible, but the probability is much, much lower.
[00:47:04.400 --> 00:47:11.920] What about the holy grail, which is to literally edit a new gene to put something in that didn't exist?
[00:47:11.920 --> 00:47:14.400] What was required to take that leap?
[00:47:14.720 --> 00:47:18.000] There are different ways that you may want to change the DNA sequence.
[00:47:18.000 --> 00:47:22.880] You may want to inactivate something, you may want to delete something, and you may want to insert something.
[00:47:22.880 --> 00:47:29.360] So, to do each one of these, you need to have a machinery that will allow you to do that.
[00:47:29.400 --> 00:47:37.480] So, the holy grail would be to be able to insert a gene into anywhere you want precisely and also very efficiently.
[00:47:37.480 --> 00:47:41.400] And to date, that ability is still not quite there yet.
[00:47:41.400 --> 00:47:50.760] We're still working on, and many other groups are working on developing technologies to make that happen with high enough efficiency.
[00:47:50.760 --> 00:47:57.960] We can do it now with very low level, maybe less than a percent or maybe just single-digit percent.
[00:47:57.960 --> 00:48:02.040] But for a big gene that we're trying to put in, we don't have a good way to do that yet.
[00:48:02.360 --> 00:48:10.440] Okay, so currently, is it possible to SNP DNA anywhere you want with the current technology?
[00:48:10.440 --> 00:48:16.520] Yeah, with CRISPR with Cas9, we can pretty much target throughout the genome and make cuts.
[00:48:16.840 --> 00:48:21.800] So, what diseases are currently amenable to that type of gene therapy?
[00:48:21.800 --> 00:48:24.360] And we'll put aside the regulatory stuff, which we can talk about.
[00:48:24.360 --> 00:48:34.360] But if we pretended that humans were laboratory animals where we could do an experiment and actually do this, what are some of the diseases that we could directly affect in that manner?
[00:48:34.680 --> 00:48:40.360] So, there are a lot of genetic diseases where there is a mutation that is pathogenic.
[00:48:40.360 --> 00:48:41.640] Overexpression.
[00:48:41.640 --> 00:48:45.240] In this case, overexpression if you want to deactivate it, but some would be underexpressed.
[00:48:45.400 --> 00:48:55.560] Yeah, so these are genes that are usually important for the body, but there's a mutation in it, and that makes the resulting protein, the mutant protein, deleterious for the patient.
[00:48:55.560 --> 00:48:59.400] So, if you can inactivate these genes, then you can treat disease.
[00:48:59.400 --> 00:49:08.680] So, for example, there are diseases in the liver where there are certain proteins that cause amyloidosis, and that can lead to serious problems.
[00:49:08.680 --> 00:49:16.480] So, by using CRISPR, you can go in and cleave these genes, inactivate them, so that they no longer produce these toxic gene products.
[00:49:16.720 --> 00:49:24.400] Huntington is another example where there are mutations that are occurring in the gene that makes the gene deleterious.
[00:49:24.400 --> 00:49:30.320] And so, if you can go and try to inactivate these deleterious mutations, it may be possible to treat the disease.
[00:49:30.640 --> 00:49:46.000] Is it generally the case that autosomal-dominant diseases are an overexpression problem or an expression of something harmful problem, whereas recessive diseases are the opposite, where you tend to not be producing enough or something of that name?
[00:49:46.000 --> 00:49:46.960] Is that overly simplistic?
[00:49:47.520 --> 00:49:48.960] Yeah, that's a good explanation.
[00:49:49.280 --> 00:50:00.000] So, many people are probably familiar with Huntington's because it is one of the most devastating neurodegenerative diseases imaginable, perhaps second only to Lou Gehrig's disease or ALS.
[00:50:00.000 --> 00:50:07.120] But unlike ALS, where we don't really know the etiology, we clearly know the etiology of Huntington's disease, which is a gene.
[00:50:07.120 --> 00:50:08.160] It's a genetic disease.
[00:50:08.160 --> 00:50:10.560] It's an autosomal dominant gene.
[00:50:10.880 --> 00:50:14.400] And sadly, it doesn't present until later in life.
[00:50:14.400 --> 00:50:27.440] So, many times individuals will pass this gene on prior to the symptoms being manifest, and therefore they go on to suffer the fate of this disease having already passed it on.
[00:50:27.440 --> 00:50:32.480] Tell us a little bit about Huntington's disease and why it may or may not be amenable to this type of treatment.
[00:50:32.800 --> 00:50:37.040] So, Huntington's disease is caused by mutations in a gene called Huntington.
[00:50:37.040 --> 00:50:39.360] This is a gene that's expressing the brain.
[00:50:39.360 --> 00:50:46.720] And in mutated form, this gene accrues an expansion of repeat sequences within the gene.
[00:50:46.720 --> 00:50:51.520] And the longer the repeat is, the more deleterious it is for the patient.
[00:50:51.520 --> 00:51:04.440] And so, the idea would be to try to shorten these repeats, or maybe if you can reduce the amount of repeated sequence of the RNA that's expressed, you could also get a cell to be healthier.
[00:51:04.680 --> 00:51:09.800] The challenges are the repeats happen within the coding region of the gene.
[00:51:09.800 --> 00:51:13.000] So the region that is important for making the protein sequence.
[00:51:13.000 --> 00:51:19.960] So in order to make the resulting edit successful, you have to do it very precisely.
[00:51:19.960 --> 00:51:24.600] You have to do it exactly three letters at a time from the repeat sequence.
[00:51:24.600 --> 00:51:26.520] Otherwise, you would shift the frame.
[00:51:26.520 --> 00:51:29.400] And the gene, remind me, has how many base pairs?
[00:51:29.400 --> 00:51:31.000] The gene has thousands of base pairs.
[00:51:31.000 --> 00:51:32.120] Okay, so it's a huge gene.
[00:51:32.120 --> 00:51:32.600] Yeah.
[00:51:32.600 --> 00:51:38.280] And these repeats can also be several hundred or maybe a thousand repeats long.
[00:51:38.280 --> 00:51:41.080] And so you want to be able to delete them very precisely.
[00:51:41.080 --> 00:51:42.760] So that is one challenge.
[00:51:42.760 --> 00:51:49.000] The other challenge is to be able to deliver the gene editing machineries into the brain and get to enough cells.
[00:51:49.000 --> 00:51:57.320] And there are some virus-based technologies that are coming along, but still we don't have probably the most suitable method yet.
[00:51:57.640 --> 00:51:59.880] So let's talk about delivery a little bit.
[00:51:59.880 --> 00:52:01.800] How do you do this?
[00:52:01.800 --> 00:52:03.160] We now have this idea.
[00:52:03.160 --> 00:52:09.080] Hopefully people can wrap their head around this somewhat challenging idea of what CRISPR is.
[00:52:09.080 --> 00:52:11.160] And maybe we can again just sort of summarize it, right?
[00:52:11.160 --> 00:52:15.960] You have a CRISPR vehicle would be a Cas9 protein.
[00:52:15.960 --> 00:52:33.400] And you also have a guide RNA that is made up of both the piece that you actually want to put in wrapped around another sort of tracer piece that holds it firmly in the Cas9 protein.
[00:52:33.400 --> 00:52:37.400] That Cas9 protein, by the way, does it require its own HeLa case to open that?
[00:52:37.560 --> 00:52:39.000] No, it can do it itself.
[00:52:39.240 --> 00:52:39.880] Beautiful.
[00:52:39.880 --> 00:52:47.040] So it's a one-man shop that runs up the host DNA, opening it and waiting to find its match.
[00:52:47.280 --> 00:52:54.480] And it waits and waits and waits, and then it finds its match, holds the strands of DNA at the Cas9, and then clip.
[00:52:54.480 --> 00:52:55.040] Correct.
[00:52:55.360 --> 00:52:58.240] And obviously, if you then put in something, it can open it.
[00:52:58.400 --> 00:53:02.800] So, how do you actually deliver the Cas9 protein to an individual?
[00:53:02.800 --> 00:53:04.560] That is really the big challenge.
[00:53:04.560 --> 00:53:16.160] So, right now, there are clinical applications of CRISPR for treating different diseases, diseasing the blood, like sickle cell disease or diseasing the liver, diseasing the eye, and many other places.
[00:53:16.160 --> 00:53:22.080] And so, depending on where you're trying to deliver CRISPR into, there are different technologies that people use.
[00:53:22.080 --> 00:53:23.920] Maybe the simplest might be in the blood.
[00:53:23.920 --> 00:53:24.400] Sure.
[00:53:24.400 --> 00:53:30.800] Okay, so tell people what sickle cell anemia is and why it's amenable to this type of therapy.
[00:53:30.800 --> 00:53:31.280] Right.
[00:53:31.280 --> 00:53:36.800] So, sickle cell anemia is caused by a mutation that causes the red blood cell to sickle.
[00:53:36.800 --> 00:53:38.640] So, they form a sickle form.
[00:53:38.640 --> 00:53:41.120] And so, they're not able to properly function.
[00:53:41.120 --> 00:53:48.400] And sometimes they can aggregate, and then this can cause occlusion in the blood vessel and can cause serious problems.
[00:53:48.400 --> 00:53:56.000] And so, these red blood cells are made by progenitor or stem cells in the body that produce these red blood cells.
[00:53:56.000 --> 00:53:57.600] And it's a simple mutation.
[00:53:57.600 --> 00:54:01.760] It's a single-point mutation that changes one amino acid.
[00:54:01.760 --> 00:54:05.920] And that's one amino acid based on one base pair change.
[00:54:05.920 --> 00:54:07.040] I can't remember what it is.
[00:54:07.040 --> 00:54:09.120] It's an alanine to a glycine or something.
[00:54:09.120 --> 00:54:14.000] It's quite trivial, is what leads to all of this downstream badness you talk about.
[00:54:14.000 --> 00:54:17.600] But now, current treatment for these patients is blood transfusions, right?
[00:54:17.600 --> 00:54:22.240] I mean, it's an awful disease, and these patients experience unbearable pain.
[00:54:22.240 --> 00:54:26.400] As an aside, some might ask: why does this disease exist?
[00:54:26.400 --> 00:54:28.640] Why didn't Darwin get rid of this?
[00:54:28.640 --> 00:54:31.320] Well, it turns out there's an advantage to having the trait.
[00:54:31.320 --> 00:54:32.280] For malaria.
[00:54:29.840 --> 00:54:32.520] Right.
[00:54:32.760 --> 00:54:40.840] So it turns out that if you have one copy of the sickle gene and the other one is normal, you have normal-looking red blood cells.
[00:54:40.840 --> 00:54:45.080] You don't get the disease, but you actually get protection, as you said, from malaria.
[00:54:45.080 --> 00:54:50.520] So that would keep this propagating, particularly in a malaria-rich area like Africa.
[00:54:50.520 --> 00:54:56.360] This is why it's much more prevalent in a black population than a white population, because it offered some benefit.
[00:54:56.360 --> 00:54:59.480] But if you have two copies of the gene, you get the sickling.
[00:54:59.480 --> 00:55:02.360] Those are not the people that are passing on their genes historically.
[00:55:02.360 --> 00:55:08.120] They would have perished before reproduction and also just perished in a great deal of pain.
[00:55:08.120 --> 00:55:09.880] But nevertheless, here we are today.
[00:55:09.880 --> 00:55:16.520] So you have to be able to go into the bone marrow to make this change because there's no point in doing this if you have to do this every week.
[00:55:16.520 --> 00:55:18.280] You want to do it one and done, right?
[00:55:18.280 --> 00:55:19.000] That's right.
[00:55:19.000 --> 00:55:25.080] One of the promises of gene editing is that it can provide a single treatment that is a cure for the disease.
[00:55:25.080 --> 00:55:37.160] And so in the case of sickle cell, what happens is that the doctor will mobilize the stem cells, the bone marrow cells, from the patient, get them to come out and be able to harvest these bone marrow cells.
[00:55:37.480 --> 00:55:40.600] And they don't necessarily do this with a bone marrow aspirate.
[00:55:40.600 --> 00:55:46.360] They do it by giving them medications that cause them to secrete more progenitor cells into the plasma.
[00:55:46.360 --> 00:55:46.920] That's right.
[00:55:46.920 --> 00:55:50.600] This is a current practice in the medical field.
[00:55:50.600 --> 00:55:54.600] And so they will get the patient, harvest their bone marrow cells.
[00:55:54.600 --> 00:56:03.960] And these cells are going to be modified in the laboratory where researchers will take the messenger RNA for Cas9.
[00:56:03.960 --> 00:56:07.000] So this will allow the cell to produce the Cas9 protein.
[00:56:07.000 --> 00:56:09.000] And they will also take guide RNA.
[00:56:09.000 --> 00:56:18.080] Maybe just tell folks really quickly, sorry to interrupt, Feng, but maybe just explain really quickly for people the relationship between DNA, RNA, messenger RNA, protein.
[00:56:18.080 --> 00:56:23.280] It's the central dogma, but that way when you say what you're about to say, they'll know why it works.
[00:56:23.600 --> 00:56:26.640] So there are different ways to get a protein to the cell.
[00:56:26.640 --> 00:56:31.200] And the way that proteins are made in the cell is that they're encoded in DNA.
[00:56:31.200 --> 00:56:36.080] And the DNA has to be transcribed into messenger RNA.
[00:56:36.080 --> 00:56:41.200] And that RNA is then translated by the ribosome into the protein.
[00:56:41.200 --> 00:56:54.480] And so if you can put into a cell either the DNA, the gene for Cas9 or the messenger RNA for Cas9 or the protein for Cas9, the cell will eventually have Cas9.
[00:56:54.480 --> 00:56:58.960] Because if you put in the DNA, the cell will start to make mRNA based on it.
[00:56:58.960 --> 00:57:01.520] And that mRNA will get translated into the protein.
[00:57:01.520 --> 00:57:02.880] But how do you put it in the DNA?
[00:57:02.880 --> 00:57:05.200] Isn't that the whole problem that we're trying to solve?
[00:57:05.200 --> 00:57:05.360] Right.
[00:57:05.360 --> 00:57:08.000] So there are different ways to put these things in.
[00:57:08.000 --> 00:57:09.040] So for DNA.
[00:57:09.360 --> 00:57:10.560] Just use a virus.
[00:57:10.560 --> 00:57:19.040] You can use a virus, or you can, if you're working with cells in a petri dish, you can directly electroporate the DNA into the cell.
[00:57:19.040 --> 00:57:22.160] And this is done by zapping the cell with the electrical current.
[00:57:22.160 --> 00:57:24.080] It will rupture the membrane.
[00:57:24.080 --> 00:57:26.720] When the membrane ruptures, things can leak in.
[00:57:26.720 --> 00:57:29.040] So you have DNA that's outside the cell.
[00:57:29.040 --> 00:57:35.360] When the cell membrane ruptures, the DNA that's outside the cell will flow into the inside of the cell and get into the cell.
[00:57:35.360 --> 00:57:38.560] And this is actually how people treat sickle cell disease.
[00:57:38.560 --> 00:57:40.320] Except they're not putting DNA into the cell.
[00:57:40.320 --> 00:57:42.160] They're putting mRNA.
[00:57:42.160 --> 00:57:54.240] So they incubate these bone marrow stem cells that have the sickle cell mutation in a bath of mRNA for Cas9 and the guide RNA separately.
[00:57:54.240 --> 00:58:08.280] And it's amazing that once those cells acquire both the mRNA for Cas9 and the mRNA that is corresponding to the guide, it will translate into a Cas9 protein.
[00:58:08.280 --> 00:58:08.680] Yes.
[00:58:08.680 --> 00:58:11.880] It doesn't translate guide RNA into a protein.
[00:58:11.880 --> 00:58:14.360] It just stays there and they find each other.
[00:58:14.360 --> 00:58:15.320] That's correct.
[00:58:15.320 --> 00:58:21.080] It is so remarkable new that anything works in this universe, but that's up there is one of those things that kind of just amazes me.
[00:58:21.240 --> 00:58:32.040] Well, I mean, this is really the result of the biotechnology revolution, the molecular biology discoveries that have really under sort of paved the biotechnology revolution.
[00:58:32.040 --> 00:58:35.400] And now, what is the efficiency of that process?
[00:58:35.400 --> 00:58:46.360] So once you go ahead and put those two bits of very different RNA into the proximity of these bone marrow cells.
[00:58:46.680 --> 00:58:51.880] This can be quite efficient in the laboratory or in these petri dish settings.
[00:58:51.880 --> 00:58:54.360] This can be approaching 100%.
[00:58:54.680 --> 00:58:55.320] Okay.
[00:58:55.320 --> 00:59:03.800] So now within a very short period of time, you have cut out and also reinserted.
[00:59:03.960 --> 00:59:04.920] It's a single base pair.
[00:59:04.920 --> 00:59:05.560] So what are they doing?
[00:59:05.560 --> 00:59:07.400] What's the exact thing you're asking Cas9 to do?
[00:59:07.560 --> 00:59:13.480] Actually, for sickle cell, the treatment that has recently been approved in the last year is actually different.
[00:59:13.960 --> 00:59:37.320] The way the treatment works is that for sickle cell patients, it's been found that for some individuals who have the sickle cell mutation, if they also carry another mutation, or if they somehow is able to express the fetal version of hemoglobin, then their sickle cell symptoms are much, much less.
[00:59:37.320 --> 00:59:51.680] And so, for treating sickle cell patients with gene editing, the therapy actually goes and modifies a different gene, modulates its expression to then allow the fetal hemoglobin gene to turn on.
[00:59:51.680 --> 00:59:58.960] And why is that an easier solution than simply changing the one amino acid that's broken in the first place?
[00:59:58.960 --> 01:00:06.480] Because the way it works is that it simply makes a cut and it doesn't require that's right.
[01:00:06.480 --> 01:00:06.960] I see.
[01:00:06.960 --> 01:00:20.160] So, we're still at the point where in vitro, we're still better off with a cleavage, just a straight cut of the DNA, than even a single base pair switch to fix one amino acid.
[01:00:20.160 --> 01:00:22.240] That latter is a harder problem.
[01:00:22.240 --> 01:00:27.040] The latter is a harder problem, but there's also very good progress on that front.
[01:00:27.040 --> 01:00:30.240] So, for example, David Liu developed a methodology.
[01:00:30.640 --> 01:00:35.920] Yeah, also, yeah, he's a colleague of mine, and he developed a technology called base editing.
[01:00:35.920 --> 01:00:43.840] And base editing allows you to use Cas9, you get rid of the DNA cleaving activity of Cas9.
[01:00:43.840 --> 01:00:46.720] So, it simply goes and binds the DNA.
[01:00:46.720 --> 01:00:57.440] So, you use it as kind of a guidance system to direct a different enzyme called a deaminase to be able to go and chemically modify a single base.
[01:00:57.600 --> 01:01:11.280] It's amazing to me Feng that that is easier than what we just wish we could do, which is change that C to a G, take out the C, make it a G, and that will give me the amino acid I want.
[01:01:11.280 --> 01:01:15.840] Like the fact that we're chemically having to modify it with a deaminase.
[01:01:15.840 --> 01:01:18.800] What do you think is necessary to take this next?
[01:01:19.040 --> 01:01:25.280] Because we've already had, as you described it, a big step function from where we were 10 years ago.
[01:01:25.280 --> 01:01:31.080] What's going to be required for the next step function for the science fiction to start?
[01:01:31.400 --> 01:01:37.000] Yeah, so I think this really goes back to the division of genetic medicine.
[01:01:37.000 --> 01:01:40.120] Genetic medicine is very powerful.
[01:01:40.120 --> 01:01:44.760] CRISPR is part of it, but it's really a two-component system.
[01:01:44.760 --> 01:01:48.840] There is the medicine itself, and then there's also the delivery technology.
[01:01:48.840 --> 01:01:56.440] So you need to have the right vehicle for delivery and the right payload to be able to treat the disease in the right cell.
[01:01:56.440 --> 01:01:59.480] And we're limited more on the delivery than delivery.
[01:01:59.480 --> 01:02:01.800] So the payload technology has come a long way.
[01:02:01.800 --> 01:02:11.720] We now have mRNA, we have Cas9, we have base editing, we have prime editing, we have a lot of different types of editing technology and even epigenetic editing.
[01:02:11.720 --> 01:02:19.880] But the bottleneck is how do we put these really powerful payloads into the right cells in the right tissue in the body?
[01:02:19.880 --> 01:02:20.760] Say those again.
[01:02:20.760 --> 01:02:23.320] What were our tools, Cas9, base pair editing?
[01:02:23.480 --> 01:02:24.760] Yeah, we have Cas9.
[01:02:24.760 --> 01:02:25.880] We have base editing.
[01:02:25.880 --> 01:02:27.240] We have prime editing.
[01:02:27.240 --> 01:02:29.480] We have different recombinases.
[01:02:29.480 --> 01:02:34.520] We have epigenetic modifiers that also are based on Cas9.
[01:02:34.520 --> 01:02:35.560] We have mRNA.
[01:02:35.560 --> 01:02:36.920] We have siRNA.
[01:02:36.920 --> 01:02:41.000] We have a lot of different things that can modulate and modify cells.
[01:02:41.000 --> 01:02:46.200] I want to actually come back and talk about the epigenetic modification using Cas9.
[01:02:46.200 --> 01:02:48.520] It's not a payload problem, it's a delivery problem.
[01:02:48.520 --> 01:02:48.920] Right.
[01:02:48.920 --> 01:02:49.560] Yeah.
[01:02:49.560 --> 01:02:51.800] And it's also a biological problem.
[01:02:51.800 --> 01:02:54.200] Right, because many diseases will not be amenable to this.
[01:02:54.200 --> 01:02:59.160] So I'm sure everybody thinks, well, Peter, we've heard you say that cancer is a genetic disease.
[01:02:59.160 --> 01:03:03.320] Does that mean that once we solve the delivery problem, we solve cancer?
[01:03:03.320 --> 01:03:05.240] Why is that not necessarily true?
[01:03:05.240 --> 01:03:10.120] That is because cancer is caused by many different risk mutations in the cell.
[01:03:10.120 --> 01:03:20.560] And so it's difficult to treat cancer by correcting the mutation because you really have to be able to correct at a very, very high efficiency.
[01:03:20.880 --> 01:03:31.600] If you have a few cells, a few cancer cells that have not been corrected, those cells will continue to divide and replicate and form tumors and even metastasize.
[01:03:31.600 --> 01:03:33.040] That is really challenging.
[01:03:33.040 --> 01:03:38.000] But people are using gene editing in the cancer therapeutic context.
[01:03:38.000 --> 01:03:48.240] What they're doing is that they are using gene editing to engineer immune cells so that the immune cells are more potent at recognizing and killing cancer cells.
[01:03:48.240 --> 01:03:50.880] And this is part of what's called immunotherapy.
[01:03:50.880 --> 01:03:51.360] Yeah.
[01:03:51.360 --> 01:03:53.360] Everybody's talking about AI.
[01:03:53.360 --> 01:03:59.920] Does AI enable this any better on either side, on the payload side or on the delivery side?
[01:03:59.920 --> 01:04:02.960] AI is very powerful for protein engineering.
[01:04:02.960 --> 01:04:10.560] In the past few years, there's been amazing breakthrough in the use of AI for predicting protein structures.
[01:04:10.560 --> 01:04:20.000] Each protein is made of a unique sequence of amino acids, and the unique sequence allows a protein to fold into a specific shape.
[01:04:20.000 --> 01:04:30.560] And one of the holy grail problems for a long time has been how do you take just the letters of a protein and predict what the shape of the protein looks like?
[01:04:30.560 --> 01:04:37.600] And this is something that many, many scientists have worked on for a long time, but hasn't been able to come up with a good solution.
[01:04:37.600 --> 01:04:48.400] But it was really in 2020-21, the use of AI by this group called DeepMind that was able to come up with a solution called AlphaFoll2.
[01:04:48.400 --> 01:04:57.520] And AlphaFold2 is an AI-based system that has learned from all of the structures of proteins that scientists have experimentally determined.
[01:04:57.520 --> 01:05:01.880] And when they solved those structures, there's a huge database of them.
[01:05:01.880 --> 01:05:10.440] And they were able to use AI to look at all of them and learn from that large database to then come up with a prediction system called AlphaFollow 2.
[01:05:10.440 --> 01:05:12.040] It's amazing to me.
[01:05:12.040 --> 01:05:14.200] Maybe just explain to people again.
[01:05:14.200 --> 01:05:15.480] Pick your favorite protein.
[01:05:15.480 --> 01:05:18.760] How many amino acids are in it in the primary sequence?
[01:05:18.760 --> 01:05:22.920] We can pick the green fluorescent protein from jellyfish, maybe 300 amino acids.
[01:05:23.080 --> 01:05:23.480] 300.
[01:05:23.480 --> 01:05:24.840] It's a relatively small protein.
[01:05:24.840 --> 01:05:25.320] Yeah.
[01:05:25.320 --> 01:05:25.960] Okay.
[01:05:25.960 --> 01:05:30.600] So it has a primary structure, which is like, what are the actual amino acids?
[01:05:30.600 --> 01:05:31.000] Correct.
[01:05:31.000 --> 01:05:35.880] It has a secondary structure, which is like, well, when does it actually form a helix?
[01:05:35.880 --> 01:05:37.720] When does it form a sheet?
[01:05:37.720 --> 01:05:42.440] It has a tertiary structure, which is kind of like how it starts to bend.
[01:05:42.760 --> 01:05:48.920] And then it has this quaternary structure, which is how the whole thing fits together in complicated three-dimensional folds.
[01:05:48.920 --> 01:05:49.800] Exactly.
[01:05:49.800 --> 01:05:58.840] What you said a moment ago is if a scientist wants to make a protein and they know what it needs to look like, they kind of know what the quaternary structure is.
[01:05:58.840 --> 01:05:59.800] I got to get this protein.
[01:05:59.800 --> 01:06:02.920] I got to design a molecule that fits in that receptor.
[01:06:02.920 --> 01:06:08.040] It's almost a trial and error problem to go from the primary sequence to that.
[01:06:08.040 --> 01:06:08.520] Right.
[01:06:08.520 --> 01:06:20.520] You're saying that AI is really good at doing this thing where it knows the relationship between primary sequence and final quaternary structure.
[01:06:20.520 --> 01:06:26.600] Is that just literally linear regression at a level we've never understood it because humans can't do it?
[01:06:26.600 --> 01:06:31.320] But is it basically just solving the world's most complicated linear regression problem?
[01:06:31.320 --> 01:06:34.520] I think at a very fundamental level, that is what is happening.
[01:06:34.520 --> 01:06:39.320] But with the human brain, we can't process so much data very effectively.
[01:06:39.320 --> 01:06:45.840] But with AI, you can have these massive neural networks that can really process this.
[01:06:45.840 --> 01:06:48.320] Let's go back to animal models for a moment.
[01:06:44.840 --> 01:06:50.880] You touched briefly on the idea of a transgenic mouse.
[01:06:51.040 --> 01:06:56.560] And again, people who have listened to this podcast for years are no stranger to the transgenic mouse.
[01:06:56.560 --> 01:07:00.880] So many amazing breakthroughs in science have come through these.
[01:07:00.880 --> 01:07:06.000] And frankly, just through genetic understanding of mice, making changes to a mouse gene.
[01:07:06.000 --> 01:07:08.880] You know, we recently had Dina Dubal on the podcast.
[01:07:08.880 --> 01:07:13.040] We talked about Clotho, to me, one of the most interesting proteins out there.
[01:07:13.040 --> 01:07:16.800] And of course, we learned the story of how Clotho came about.
[01:07:16.800 --> 01:07:21.440] Silencing a gene, found this thing, overexpressed the gene, found this thing.
[01:07:21.440 --> 01:07:24.640] Okay, how does CRISPR enable that today?
[01:07:24.640 --> 01:07:33.920] Has it changed the ability of people working on totally other problems to get there quicker using laboratory animals?
[01:07:33.920 --> 01:07:34.640] Right.
[01:07:34.640 --> 01:07:39.440] So the transgenic mouse technology really revolutionized biology.
[01:07:39.440 --> 01:07:46.640] And when the transgenic mouse technology was developed, the way it worked is that you would start with stem cells for mice.
[01:07:46.640 --> 01:07:52.800] You would modify these stem cells and then you put the stem cells back into an embryo.
[01:07:52.800 --> 01:07:59.760] And then you transplant the embryo into a mouse so that the embryo can develop into fetus and then be born as a new mouse.
[01:07:59.760 --> 01:08:01.680] That is a very long process.
[01:08:01.680 --> 01:08:09.760] And then once the mouse is born, usually that mouse does not have 100% of that genetic modification.
[01:08:09.760 --> 01:08:12.000] So the mouse is called a mosaic.
[01:08:12.000 --> 01:08:24.960] And then you have to take that mouse and breed the mouse again with another mouse, and hoping that the mosaic part is the one that gets expressed and you basically try to concentrate the mosaic portion of it.
[01:08:24.960 --> 01:08:25.760] Exactly.
[01:08:25.760 --> 01:08:35.800] So, when you use this methodology, it can take probably a year or maybe even longer to be able to generate a specific transgenic mouse.
[01:08:29.600 --> 01:08:37.880] So, it used to take a long time to do this.
[01:08:37.880 --> 01:08:49.720] But now, with CRISPR technology, what people can do is they can directly inject the gene editing Cas9 guide RNA into an embryo, into a single-cell embryo, and then modify it there.
[01:08:50.040 --> 01:08:54.040] So, again, this is so brilliant because we can't do this in humans, of course.
[01:08:54.040 --> 01:08:57.560] I mean, we could, but that's a whole ethical discussion we should talk about.
[01:08:57.560 --> 01:08:58.920] But we can directly do that.
[01:08:58.920 --> 01:09:01.160] So, we can make a transgenic mouse in one go.
[01:09:01.160 --> 01:09:01.640] Exactly.
[01:09:01.640 --> 01:09:04.280] So, the mouse gestation period is 21 days.
[01:09:04.280 --> 01:09:06.760] So, after 21 days, you have a transgenic mouse.
[01:09:06.760 --> 01:09:09.320] What has this done to the field of biomedical research?
[01:09:09.400 --> 01:09:12.360] It has accelerated biomedical research dramatically.
[01:09:12.360 --> 01:09:18.280] So, imagine yourself as a graduate student, and usually a PhD will take five years.
[01:09:18.280 --> 01:09:23.960] And a lot of that time is the rote work of transfecting mice and waiting for the.
[01:09:24.040 --> 01:09:24.280] Yeah.
[01:09:24.280 --> 01:09:24.440] Yeah.
[01:09:24.440 --> 01:09:30.120] So, if you have to wait two years to get a mouse so that you can begin your experiment, that is a long time.
[01:09:30.120 --> 01:09:35.560] So, now with CRISPR or gene editing, you can get a mouse in two, three months.
[01:09:35.880 --> 01:09:38.120] So, let's play devil's advocate for a moment.
[01:09:38.120 --> 01:09:42.520] When you did your PhD, you had to slog through the old-fashioned way.
[01:09:42.520 --> 01:09:44.520] Were there hidden benefits of that?
[01:09:44.520 --> 01:09:49.480] In other words, did it give you more time to read, more time to be curious, more time to fail?
[01:09:49.480 --> 01:10:04.120] Again, it's a tangential discussion, but do you worry that with this remarkable precision tool that budding scientists are missing out on an experience that you and your entire generation and everyone before you had?
[01:10:04.120 --> 01:10:10.120] Or do you think that that's a relatively small price to pay for the pace of development?
[01:10:10.120 --> 01:10:11.880] I'm not too concerned about it.
[01:10:11.880 --> 01:10:20.560] I think if we can accelerate the accumulation of knowledge, the acquisition of data, I think that will really help science move a lot faster.
[01:10:20.560 --> 01:10:35.840] I think in the future, especially with higher throughput technologies, CRISPR, DNA sequencing, and many other things, together we're going to be accumulating new data for biology at an exponential pace.
[01:10:35.840 --> 01:10:47.840] And we're not only going to be relying on our own ability to analyze the data, but we're going to have AI to help us, these large systems that can draw much, much larger regression analysis.
[01:10:47.840 --> 01:10:55.040] And with that, I think biology discovery and disease treatment development will really accelerate.
[01:10:55.040 --> 01:10:57.200] I think that's a really exciting future.
[01:10:57.200 --> 01:11:06.880] Now, we've talked a lot about Cas9, but you've also specifically done quite a lot with another protein, another CRISPR-associated protein called Cas13.
[01:11:06.880 --> 01:11:12.160] What's the difference, and how does this potentially impact future work?
[01:11:12.160 --> 01:11:18.240] CRISPR is a bacterial immune system, and there are many, many different types of CRISPR.
[01:11:18.240 --> 01:11:25.040] And so in nature, bacteria are invaded by DNA viruses, by RNA viruses, all sorts of different viruses.
[01:11:25.040 --> 01:11:33.120] Cas9 protects bacteria against DNA viruses, but then there also needs to be a CRISPR system that protects against RNA viruses.
[01:11:33.120 --> 01:11:37.760] So Cas13 is the RNA analog of Cas9.
[01:11:37.760 --> 01:11:43.760] It uses a guide RNA to recognize the RNA virus and then cleave the RNA genome.
[01:11:43.760 --> 01:11:57.840] And what is really interesting about Cas13 is that, unlike Cas9, it not only cleaves the recognized RNA, but once it recognizes a piece of RNA, it also turns on almost a suicidal function.
[01:11:57.840 --> 01:12:01.960] It goes and cleaves any other RNA that's in the bacterial cell.
[01:12:02.280 --> 01:12:19.400] And so, in the infection cycle, you can think of this as an altruistic system where when the Cas13 recognizes my cell has been infected by RNA virus, I'm going to shut myself down, kill the cell, and save the population.
[01:12:19.720 --> 01:12:21.880] So, hang on, why is that the case?
[01:12:21.880 --> 01:12:25.160] So, why does the cell not choose to do that with the DNA virus?
[01:12:25.160 --> 01:12:29.000] Are RNA viruses necessarily more lethal to bacteria?
[01:12:29.000 --> 01:12:31.960] RNA is usually more abundant.
[01:12:31.960 --> 01:12:35.640] Once the RNA gets produced, there are many copies.
[01:12:35.640 --> 01:12:39.640] And so, it's difficult to shut down every single copy of RNA.
[01:12:39.640 --> 01:12:42.120] Whereas with DNA, there's usually just one copy of DNA.
[01:12:42.120 --> 01:12:43.160] So, if you can shut it down.
[01:12:43.480 --> 01:12:51.480] So, when a DNA virus infects a bacteria, let's just talk about a very specific bacteriophage latches on the outside of an E.
[01:12:51.480 --> 01:12:54.920] coli, it's going to shoot in a piece of DNA.
[01:12:54.920 --> 01:12:59.160] We've already talked at length about how that works and the role Cas9 plays in that.
[01:12:59.160 --> 01:13:07.560] When a different bacteriophage comes along, you're saying it inserts a lot of RNA, or just the RNA replicates much quicker.
[01:13:07.720 --> 01:13:08.520] RNA replicates quicker.
[01:13:08.840 --> 01:13:11.240] Okay, so it inserts a small amount of RNA.
[01:13:11.240 --> 01:13:17.400] But remind me why a small amount of RNA will be amplified much quicker than a small amount of DNA.
[01:13:17.400 --> 01:13:19.480] Is it because it's one step less?
[01:13:19.480 --> 01:13:24.040] It's already made the machinery to go in front of the translating ribosome.
[01:13:24.040 --> 01:13:24.440] Exactly.
[01:13:24.440 --> 01:13:29.080] Because DNA has to go to RNA to make proteins that go back to make DNA.
[01:13:29.080 --> 01:13:33.160] So you're saying Cas13 also has a suicide feature.
[01:13:33.160 --> 01:13:38.520] And the suicide feature is actually quite useful for developing diagnostic technologies.
[01:13:38.520 --> 01:13:46.880] And so, especially during COVID, we use CAS13 to develop a way to detect coronavirus RNA.
[01:13:43.240 --> 01:13:51.520] And it provided a way to have a simple and rapid detection method.
[01:13:51.840 --> 01:13:52.800] So say more about that.
[01:13:52.800 --> 01:13:54.480] Was that really the first application?
[01:13:54.640 --> 01:13:57.360] That was, I think, one of the first applications.
[01:13:57.360 --> 01:13:58.000] Yeah.
[01:13:58.000 --> 01:13:59.520] How would that have been done before?
[01:13:59.520 --> 01:14:03.280] And what was the speed and accuracy of it being done in that way?
[01:14:03.280 --> 01:14:07.200] So the most widespread method for diagnostics is using PCR.
[01:14:07.200 --> 01:14:09.760] So it checks for the nucleic acid sequence.
[01:14:09.760 --> 01:14:12.080] But PCR is a laboratory-based test.
[01:14:12.080 --> 01:14:17.600] It requires a machine called a PCR machine, which is complicated to run.
[01:14:17.600 --> 01:14:21.600] And you have to be in a laboratory environment to go through the test.
[01:14:21.600 --> 01:14:32.320] What CAS13 provided is something that's more similar to an antigen test, where you can run it at a point of care or even potentially at home.
[01:14:32.320 --> 01:14:36.560] And it will simply require you to take a swab to have the sample.
[01:14:36.560 --> 01:14:44.240] You mix the sample into a buffer, and then Cas13 would react in that buffer to be able to detect the virus sequence.
[01:14:44.240 --> 01:14:49.920] Then you load it onto a paper strip, it will run, and then you see whether or not a band shows up.
[01:14:50.240 --> 01:15:00.880] When we think now about gene editing going forward, and again, we've already established that the payload is not the problem, it's the targeting and the delivery.
[01:15:00.880 --> 01:15:05.360] What are the relative advantages and disadvantages of Cas9 versus Cas13?
[01:15:05.360 --> 01:15:12.160] And are there other CAS or CRISPR-associated proteins out there that might even be better than both of them?
[01:15:12.480 --> 01:15:17.280] So CAS9 and CAS13 both have therapeutic applications.
[01:15:17.280 --> 01:15:20.960] But one of the challenges is that they are large proteins.
[01:15:20.960 --> 01:15:27.600] Cas9 is 1,300 amino acids long, and Cas13s are usually around 1,000 amino acids long.
[01:15:27.600 --> 01:15:29.040] So they're fairly large proteins.
[01:15:29.280 --> 01:15:30.600] How long was Cas9?
[01:15:30.600 --> 01:15:32.440] 1300 amino acids.
[01:15:32.440 --> 01:15:40.760] And so, in order to get Cas9 into a cell, you have to be able to fit Cas9 and the guide RNA into your delivery system.
[01:15:40.760 --> 01:15:48.440] And if you're using viral vectors to deliver, they are usually very compact and you can barely fit Cas9 in.
[01:15:48.440 --> 01:15:52.120] So then the nice thing is, what if you have something that's smaller?
[01:15:52.120 --> 01:16:01.480] And so we and also many other groups have worked on trying to discover new proteins that are more compact and more easily packageable.
[01:16:01.480 --> 01:16:08.920] And there are some out there, but sometimes usually they're not as specific or not as active as Cas9.
[01:16:08.920 --> 01:16:11.960] And so there are trade-offs to using some of those systems.
[01:16:11.960 --> 01:16:20.920] And we're working on engineering them, and there's a lot of good progress turning those systems into specific and comparably active systems as Cas9.
[01:16:20.920 --> 01:16:32.680] So what would be, if you could wave a magic wand, how big a Cas protein would you tolerate such that your Cas protein plus your guide RNA would easily fit into your delivery vehicle?
[01:16:32.680 --> 01:16:35.400] Would you want to be half that size and be 500 amino acids?
[01:16:35.400 --> 01:16:36.920] Do you need to be smaller than that?
[01:16:36.920 --> 01:16:40.280] If you could shrink it down to 1,000 base pairs, so 300 amino acids.
[01:16:40.440 --> 01:16:41.560] 300 amino acids.
[01:16:41.560 --> 01:16:44.520] That would be ideal, but I think that's challenging to do.
[01:16:44.680 --> 01:16:46.760] Let's maybe make sure I understand why.
[01:16:46.760 --> 01:16:59.720] You're saying, look, at the end of the day, I need a protein that has the structural integrity to hold the guide RNA in place, make its way down into the nucleus, open up the DNA.
[01:16:59.720 --> 01:17:03.240] So it's basically two enzymes: it's a helicase and a nuclease.
[01:17:03.240 --> 01:17:07.640] And it has to be able to march along the DNA, recognize while holding.
[01:17:07.640 --> 01:17:15.000] Again, it's a mechanical problem if you really stop to think about it, just on the smallest level, and then hold, cut, boom, insert eventually.
[01:17:15.200 --> 01:17:18.080] And you're saying, at some point, we just abut the limits of physics.
[01:17:18.080 --> 01:17:21.920] I need a certain amount of amino acids to make that structure.
[01:17:21.920 --> 01:17:26.640] But it sounds to me like you're saying you don't believe that that exists in nature.
[01:17:26.640 --> 01:17:31.760] You're not out there looking for a 300-amino acid Cas protein anymore.
[01:17:31.760 --> 01:17:35.680] You're now going to say, let's use AI to help us build one.
[01:17:35.680 --> 01:17:41.040] There are natural forms of proteins that are small and are like Cas9.
[01:17:41.040 --> 01:17:50.320] In one of our projects, trying to look for a small Cas9, we thought, let's look at the evolutionary origin of where Cas9 came from.
[01:17:50.320 --> 01:18:02.560] By tracing the evolutionary history of Cas9, we found that there is a very large family protein called ISCB that is the ancestral form of Cas9.
[01:18:02.560 --> 01:18:05.760] ISCB is a very small protein.
[01:18:05.760 --> 01:18:08.400] It's only about 450 amino acids.
[01:18:08.400 --> 01:18:11.280] So it's about a third of the size of Cas9.
[01:18:11.280 --> 01:18:13.200] And it does exactly what Cas9 does.
[01:18:13.200 --> 01:18:15.120] It's got a helicase activity.
[01:18:15.120 --> 01:18:17.200] It's got nuclease domains.
[01:18:17.200 --> 01:18:21.760] And it also works with the RNA guide to recognize and cleave DNA.
[01:18:21.760 --> 01:18:28.800] But what is different between ISCB and Cas9 is that the guide RNA for ISCB is much, much larger.
[01:18:28.800 --> 01:18:31.280] So you rob Peter and you got to pay Paul.
[01:18:31.280 --> 01:18:32.320] Exactly.
[01:18:32.320 --> 01:18:35.840] And RNA is not as stable, not as robust.
[01:18:35.840 --> 01:18:38.080] It's more prone to degradation.
[01:18:38.080 --> 01:18:40.640] Yeah, you don't want to go any bigger on guide RNA.
[01:18:40.640 --> 01:18:42.320] You need the best of both worlds.
[01:18:42.320 --> 01:18:49.240] You need a Cas-associated protein that is small and you want to be able to keep the guide RNA.
[01:18:49.240 --> 01:18:49.800] Small.
[01:18:49.800 --> 01:18:50.400] Small.
[01:18:50.400 --> 01:18:50.720] Right.
[01:18:50.720 --> 01:18:52.160] That's the holy grail.
[01:18:52.160 --> 01:18:52.640] Okay.
[01:18:52.640 --> 01:18:57.600] And is it your prediction that that will have to be developed synthetically?
[01:18:57.600 --> 01:19:09.560] I think through engineering, it's not clear that such a compact system has been developed by nature, but we can start from nature's Cas9 or ISCB as a scaffold and then begin to engineer.
[01:19:09.960 --> 01:19:11.800] So what kind of race is on for that?
[01:19:11.800 --> 01:19:15.480] Because I can't even fathom the commercial value of that.
[01:19:15.480 --> 01:19:30.120] If there was a new protein called the Casalpha or CasOmega, whatever, the synthetic version that was small enough to now easily deliver payload, this is a trillion-dollar product.
[01:19:30.360 --> 01:19:31.720] A lot of people are working on it.
[01:19:31.720 --> 01:19:33.960] I think it's certainly going to be very, very useful.
[01:19:33.960 --> 01:19:39.800] Is the lion's share of this work being done in universities or is it being done inside of biotech companies?
[01:19:39.800 --> 01:19:41.400] It's in both places now.
[01:19:41.400 --> 01:19:46.840] Is this one of the biggest races going on in CRISPR biology today?
[01:19:46.840 --> 01:19:48.680] I'm not sure this is the biggest race.
[01:19:48.680 --> 01:19:51.480] There are a lot of other capabilities that we want to realize.
[01:19:51.480 --> 01:19:51.880] Okay.
[01:19:51.880 --> 01:19:57.480] For example, how do you insert large genes into the genome precisely and efficiently?
[01:19:57.480 --> 01:19:59.880] I think that is just as important of a problem.
[01:19:59.880 --> 01:20:04.680] Cas9, even though it's large, we can deliver it to some cells already.
[01:20:04.680 --> 01:20:05.480] So there's already.
[01:20:05.720 --> 01:20:07.240] In vivo or only in vitro?
[01:20:07.320 --> 01:20:08.200] In vivo.
[01:20:08.200 --> 01:20:11.480] What cells can be delivered in vivo using Cas9 currently?
[01:20:11.480 --> 01:20:12.440] So liver.
[01:20:12.440 --> 01:20:12.840] Okay.
[01:20:12.840 --> 01:20:19.640] Yeah, liver is a place where we can use lipid nanoparticles to deliver Cas9 and guide RNA into.
[01:20:19.640 --> 01:20:24.680] The COVID vaccine is made using mRNA and lipid nanoparticle.
[01:20:24.680 --> 01:20:32.440] And so it's a very similar approach where you formulate these lipids with the Cas9 mRNA and guide RNA.
[01:20:32.440 --> 01:20:36.600] And what is it about the liver that makes it amenable to a lipid nanoparticle?
[01:20:36.600 --> 01:20:39.320] There's a lot of lipid recycling happening in the liver.
[01:20:39.320 --> 01:20:45.000] And so when you have a lipid nanoparticle, they get bound by these recycling proteins and get taken up.
[01:20:45.280 --> 01:20:45.920] Interesting.
[01:20:45.920 --> 01:20:48.080] Does it matter the manner in which it's given?
[01:20:48.080 --> 01:20:50.000] Intravenous, intramuscular?
[01:20:50.000 --> 01:20:56.240] Do all lipid nanoparticles end up there provided they're not ingested orally and presumably digested?
[01:20:56.240 --> 01:20:57.600] A lot of it goes to liver.
[01:20:57.600 --> 01:21:01.920] Yeah, because liver also is one of the areas in our body that filter out all the toxins.
[01:21:01.920 --> 01:21:02.320] Yeah.
[01:21:02.320 --> 01:21:03.520] So everything gets trapped there.
[01:21:03.520 --> 01:21:19.200] So in other words, if you were thinking about one of the rare inborn diseases of metabolism, these are the really rare diseases where children are born without a particular enzyme that allows them to either metabolize a certain protein or glucose even for that matter.
[01:21:19.200 --> 01:21:28.880] Do we think that we're on the cusp of being able to use, I don't want to suggest that Cas9 is primitive, but in the context of our discussion, we've realized it has some limitations.
[01:21:28.880 --> 01:21:36.160] But do we think that the CRISPR-Cas9 system is already good enough and sufficient to cure some of those diseases?
[01:21:36.160 --> 01:21:38.000] For some of them, potentially.
[01:21:38.000 --> 01:21:44.640] It will depend on the mutation, whether or not we can use Cas9 or base editing or prime editing to be able to fix the innovation.
[01:21:44.800 --> 01:21:46.480] You have to figure out the payload problem.
[01:21:46.480 --> 01:21:46.800] Right.
[01:21:47.280 --> 01:21:55.840] But if it's in the liver and it can be addressed by targeting hepatocytes in the liver, then the delivery problem is already addressable.
[01:21:55.840 --> 01:21:56.480] Okay.
[01:21:56.480 --> 01:21:58.560] What about genetic diseases of the eye?
[01:21:58.560 --> 01:22:03.600] That's also a place that's easy to reach and you don't require systemic administration.
[01:22:03.600 --> 01:22:09.200] What are the genetic conditions of the eye that might be amenable to this treatment as it stands today?
[01:22:09.200 --> 01:22:14.240] Yeah, there are different eye diseases that are affected by single gene mutations.
[01:22:14.240 --> 01:22:21.840] For example, LCA10 is one of the eye diseases that there's already been a CRISPR strategy being developed for.
[01:22:21.840 --> 01:22:41.880] The way that these diseases in the eye are treated is by designing Cas9 and a guide RNA to be able to knock out the gene that is causing degenerative conditions in the eye, and then using a viral vector to deliver the Cas9 gene and also the guide RNA intraocularly into the patient.
[01:22:41.880 --> 01:22:48.840] And so once the virus gets into the cells in the eye, they will make Cas9, they'll make the guide RNA, and then that will carry out the modification.
[01:22:49.160 --> 01:22:51.560] So in that sense, it's a bit of the old meets the new.
[01:22:51.560 --> 01:23:03.720] You're taking the oldest trick in the book that was the original, original gene therapy vehicle, the virus, and you're combining it with a far smarter payload, which is Cas9 and Guide RNA.
[01:23:03.720 --> 01:23:06.280] Where do these stand in clinical trials right now?
[01:23:06.280 --> 01:23:10.520] Both the liver and the eye, which would obviously be the leading edge of this.
[01:23:10.520 --> 01:23:15.640] So the eye is the first place where in the U.S., a gene therapy was developed and approved.
[01:23:15.640 --> 01:23:17.720] And so this is a drug called Loxterna.
[01:23:17.720 --> 01:23:23.480] Loxerna puts a gene into the eye to be able to treat a disease called LCA2.
[01:23:23.480 --> 01:23:33.960] And so basically the virus provides a gene that is missing in the cells in the eye, and that is able to allow the patients to regain some light sensitivity.
[01:23:33.960 --> 01:23:35.800] And so that was the first gene therapy.
[01:23:35.800 --> 01:23:38.520] And these are patients that are completely blind without it?
[01:23:38.520 --> 01:23:39.160] That's right.
[01:23:39.160 --> 01:23:42.680] And then how much light sensitivity do they get once this gene is inserted?
[01:23:42.680 --> 01:23:46.840] They get some so that they can move around in a room with large obstacles.
[01:23:46.840 --> 01:23:47.400] Wow.
[01:23:47.400 --> 01:23:48.040] Yeah.
[01:23:48.040 --> 01:23:51.240] And this is all done just through a single injection in the eye.
[01:23:51.240 --> 01:23:52.360] A single injection.
[01:23:52.360 --> 01:23:53.000] Wow.
[01:23:53.000 --> 01:23:55.640] What are other ocular targets?
[01:23:55.640 --> 01:24:01.320] So LCA10 is another one that was being developed by Edithask medicine.
[01:24:01.320 --> 01:24:08.360] And the way this disease developed is that there is a mutation that causes degeneration in the eye.
[01:24:08.360 --> 01:24:21.120] And the idea is to use Cas9, use the same viral vector system to deliver it into the eye and have Cas9 inactivate this mutant gene so that it can slow down or stop degeneration.
[01:24:21.440 --> 01:24:27.840] So LCA2, you have to put an active gene in that was missing.
[01:24:27.840 --> 01:24:28.240] Right.
[01:24:28.560 --> 01:24:32.640] LCA, was it 9 or 12, 10, you have to deactivate a gene?
[01:24:33.120 --> 01:24:33.760] That's right.
[01:24:33.760 --> 01:24:34.160] Okay.
[01:24:34.160 --> 01:24:35.520] Where is that in clinical trials?
[01:24:35.520 --> 01:24:36.560] What phase is that in?
[01:24:36.560 --> 01:24:38.400] So it went through phase one, two.
[01:24:38.400 --> 01:24:38.800] Okay.
[01:24:38.800 --> 01:24:40.640] What was the efficacy in phase two?
[01:24:40.640 --> 01:24:41.920] There was some efficacy.
[01:24:41.920 --> 01:24:44.400] What's the phenotype of this patient without gene therapy?
[01:24:44.400 --> 01:24:45.120] Is it total blindness?
[01:24:45.600 --> 01:24:48.320] Also blindness or deteriorating vision.
[01:24:48.320 --> 01:24:49.920] So blind by what age?
[01:24:49.920 --> 01:24:51.280] Probably 30s.
[01:24:51.520 --> 01:24:52.400] Okay, wow.
[01:24:52.400 --> 01:24:54.640] And then what did the phase two find?
[01:24:54.640 --> 01:24:56.960] How much eyesight were they able to restore?
[01:24:56.960 --> 01:25:02.560] They found that there's some improvement in vision, but I think it wasn't as robust as they had hoped for.
[01:25:02.560 --> 01:25:03.120] Okay.
[01:25:03.120 --> 01:25:04.400] Why do you think that is?
[01:25:04.400 --> 01:25:08.400] Why do you think that these therapies are not fully restoring vision?
[01:25:08.400 --> 01:25:18.560] In the case of the LCA2, is it because by the time you treat these people, the neuroplasticity part of it has lost its window of development?
[01:25:18.560 --> 01:25:27.360] In other words, is the problem that if you treat these people late in life, the part of the brain that receives the visual signal isn't developed?
[01:25:27.360 --> 01:25:30.800] Or is it literally that we're just not fixing the eye?
[01:25:30.800 --> 01:25:32.720] The retina doesn't regenerate.
[01:25:32.720 --> 01:25:37.440] So if it's already degenerating, you have to deal with what is left in the retina.
[01:25:37.440 --> 01:25:43.520] And so what you can restore is really capped by whatever that's already left there.
[01:25:43.520 --> 01:25:46.960] And plus there's also inefficiencies in the delivery systems.
[01:25:47.040 --> 01:25:50.080] You're not restoring 100% of the cells.
[01:25:50.080 --> 01:26:01.400] And if you're layering on gene editing on top of it, which also has some less than 100% efficiency, and when you multiply that all together, that's why you're not getting surfaced restoration.
[01:26:01.720 --> 01:26:07.560] So, what would have to be true to fully restore vision in those patients?
[01:26:07.560 --> 01:26:10.360] What set of things would have to be true therapeutically?
[01:26:10.680 --> 01:26:13.560] I think it's only very hard to fully restore vision.
[01:26:13.560 --> 01:26:18.280] If you really want to fully restore vision, you probably have to regenerate the retina.
[01:26:18.280 --> 01:26:21.320] So, you have to replenish cells that are missing.
[01:26:21.320 --> 01:26:23.400] Could that be done epigenetically?
[01:26:23.400 --> 01:26:31.000] What do we know about the epigenome of the retina in the adult versus the infant?
[01:26:31.000 --> 01:26:47.240] Do we know that if we could simultaneously, or even in two treatments, correct the genetic defect, but then return the epigenome to the milieu of embryogenesis or early life?
[01:26:47.240 --> 01:26:49.160] Could that fix the problem?
[01:26:49.480 --> 01:26:56.040] That could potentially work, although this is not my expertise, so I don't know all the processes involved there.
[01:26:56.040 --> 01:27:09.720] If you can recapitulate development in the eye and allow cells to redevelop the retina as it was developing during development, then potentially you can regenerate the eye.
[01:27:09.720 --> 01:27:15.960] On the side of the liver, what has been the success rate of the Cas9 delivery system there?
[01:27:15.960 --> 01:27:19.480] The lipid nanoparticle delivering the Cas9 payload, to be more exact.
[01:27:19.720 --> 01:27:21.400] In the liver, it's quite robust.
[01:27:21.400 --> 01:27:24.200] You can probably get 80-90% in the liver.
[01:27:24.200 --> 01:27:28.920] And that's sufficient to correct certainly any underexpression, right?
[01:27:28.920 --> 01:27:29.400] Right.
[01:27:29.720 --> 01:27:39.160] In the liver, there's also an interesting development where scientists are trying to target a gene called PCSK9 to be able to treat cardiovascular disease.
[01:27:39.160 --> 01:27:41.720] So, they're going to use a lipid nanoparticle.
[01:27:41.720 --> 01:27:49.520] You're going to put in a new PCSK9 gene that's inactive, or you're just going to deliver a vehicle that paralyzes PCSK9?
[01:27:49.520 --> 01:27:56.400] Yeah, the strategy is to paralyze PCSK9 so that you can inactivate PCSK9 and then reduce cholesterol.
[01:27:56.400 --> 01:27:59.360] Any idea how much a treatment like that would cost?
[01:27:59.360 --> 01:28:00.400] I don't know.
[01:28:00.400 --> 01:28:04.800] Initially, maybe tens of thousand dollars.
[01:28:05.120 --> 01:28:16.560] Well, I mean, to be honest, that would be really cheap because the drug that inhibits PCSK9 inhibitor, and there are three of them out there, when they came out, they were $15,000 a year drugs.
[01:28:16.560 --> 01:28:19.360] Now they're $6,000 a year drugs.
[01:28:19.360 --> 01:28:22.320] So call it $10,000 a year as the current drug cost.
[01:28:22.320 --> 01:28:24.560] So you're saying that.
[01:28:24.560 --> 01:28:25.520] Oh, it might be that much more.
[01:28:25.520 --> 01:28:30.560] Okay, again, still relatively inexpensive compared to the whole thing to a lifetime of therapy.
[01:28:30.560 --> 01:28:38.080] Is there any particular reason it costs that much if you actually just look at the drug and don't try to bake in the cost of the last 10 years of developing it?
[01:28:38.080 --> 01:28:53.680] In other words, if a company today came along and said, I'm interested in developing a drug that one and done treats PCSK9 and lowers LDL cholesterol indefinitely, how much would it actually cost to make that drug under GMP conditions at scale?
[01:28:53.680 --> 01:28:58.240] I don't know the exact cost of good, but it's probably much lower than that.
[01:28:58.240 --> 01:29:02.400] But in order to get to that, we need to get the processes developed.
[01:29:02.400 --> 01:29:06.640] And so I think over time, we're definitely going to be able to get down the cost.
[01:29:06.640 --> 01:29:11.760] Large-scale mRNA production, large-scale formulation of nanoparticles.
[01:29:11.760 --> 01:29:15.200] I think those are the processes that people are making really good progress on.
[01:29:15.200 --> 01:29:17.680] So what is driving the cost right now?
[01:29:17.680 --> 01:29:20.320] What is making it expensive today?
[01:29:20.320 --> 01:29:32.360] I think there are a lot of factors, including development cost and also the fact that these drugs, it's the first time developing these modalities of drugs.
[01:29:29.520 --> 01:29:35.800] And so the manufacturing processes need to get developed.
[01:29:36.280 --> 01:29:38.680] And what specifically is it?
[01:29:38.680 --> 01:29:43.000] Presumably we're just making synthetic Cas9 and Cas13 all day long, right?
[01:29:43.000 --> 01:29:45.560] In the laboratory at laboratory scale.
[01:29:45.560 --> 01:29:47.560] I see, but commercially not.
[01:29:47.560 --> 01:29:48.120] Right.
[01:29:48.120 --> 01:29:51.240] So you make your own Cas9 and Cas13 in your lab.
[01:29:51.240 --> 01:29:51.800] We do.
[01:29:51.800 --> 01:29:52.040] Okay.
[01:29:52.040 --> 01:29:52.760] Yeah, we do.
[01:29:52.760 --> 01:29:58.360] You can also buy it from commercial companies, but scale is usually for laboratory use.
[01:29:58.360 --> 01:30:01.080] If you think about it, a mouse is 10 grams.
[01:30:01.080 --> 01:30:04.360] A human can be 40 kilograms.
[01:30:04.360 --> 01:30:07.560] So that's, you know, a thousand times larger in body weight.
[01:30:07.560 --> 01:30:10.440] So you need a thousand times more material.
[01:30:10.440 --> 01:30:16.920] And I think it's developing the process to scale that to that level to be able to treat human beings.
[01:30:16.920 --> 01:30:20.040] That is where the expensive process is.
[01:30:20.040 --> 01:30:24.040] Is there any foundational roadblock to doing this?
[01:30:24.040 --> 01:30:34.600] Or is there a Moore's Law aspect to this where it's just going to get significantly cheaper over time in the same way that transistors just get significantly cheaper over time?
[01:30:34.600 --> 01:30:40.040] I'm not an expert on this, but I think there will definitely be efficiencies from scale.
[01:30:40.040 --> 01:30:45.080] Enzymatic processes for making these RNAs is getting much better.
[01:30:45.080 --> 01:30:46.760] Purity is improving.
[01:30:46.760 --> 01:30:52.360] So all those things, I think they will compound and that will result in significant cost reduction.
[01:30:52.360 --> 01:30:58.840] How much do you follow slash pay attention to the regulation of gene editing?
[01:30:58.840 --> 01:31:23.920] I know that, gosh, sometime in the last six, seven years, there was a very controversial case in China with a scientist who had, it seems somewhat nefariously from my reading of the story, edited the embryos of kids to render them, on the surface, this sounds like a great idea, but rendered them immune to HIV.
[01:31:23.920 --> 01:31:26.160] And I believe one of their parents was HIV positive.
[01:31:26.160 --> 01:31:28.640] This was an IVF case.
[01:31:28.640 --> 01:31:46.000] But it turned out that there was an enormous amount of backlash from the scientific and medical community because I guess, one, he did it without the full consent of the medical community, and also the belief that the risk of transmission of HIV was quite low to the child in the first place.
[01:31:46.000 --> 01:31:55.920] Can you say more about that case in particular, and then also what the current state of genetic manipulation is from a sort of ethical legal standpoint?
[01:31:55.920 --> 01:31:57.920] So this was back in 2018.
[01:31:57.920 --> 01:32:00.880] There was a couple who wanted to have children.
[01:32:00.880 --> 01:32:08.960] The father was HIV positive, and so they wanted to make sure that their children are not infected by HIV.
[01:32:08.960 --> 01:32:16.960] And so scientists made a mutation in the embryos that is a gene called CCR5.
[01:32:16.960 --> 01:32:20.800] A CCR5 mutation is a naturally occurring mutation in the human population.
[01:32:20.960 --> 01:32:28.640] Single digit percent of individuals carry this mutation, and these individuals are naturally immune to HIV infection.
[01:32:28.640 --> 01:32:30.720] This is the magic Johnson mutation.
[01:32:30.720 --> 01:32:31.040] Right.
[01:32:31.040 --> 01:32:36.400] They don't have this specific protein on the surface to allow HIV to bind and enter the cell.
[01:32:36.400 --> 01:32:42.560] And so the scientists edited the embryo, human embryo, to remove this CCR5 gene.
[01:32:42.560 --> 01:32:46.240] But it turned out that that edit wasn't complete.
[01:32:46.240 --> 01:32:48.640] Earlier, we're talking about mosaicism.
[01:32:48.640 --> 01:32:56.480] So, the result of his edit was actually two girls who were mosaics for the CCR5 mutation.
[01:32:56.480 --> 01:33:02.760] So, the editing happened, and the embryos were implanted in the mother and carried to term.
[01:32:59.360 --> 01:33:04.760] And so, two baby girls were born.
[01:33:05.080 --> 01:33:14.520] And when the genetics of these baby girls were analyzed, it turned out that they were highly mosaic, suggesting that the editing wasn't very efficient.
[01:33:14.520 --> 01:33:17.960] And the edit was done at how many cells in theory?
[01:33:17.960 --> 01:33:18.760] Was it one?
[01:33:18.760 --> 01:33:22.840] I don't know exactly how many cells, but very early on, post-fertilization.
[01:33:22.840 --> 01:33:34.760] But to have guaranteed no mosaicism, would you need to do this edit at a single cell and then proliferate only that cell and discard the rest of the blastocytes?
[01:33:34.760 --> 01:33:36.360] In theory, that's the way to do it.
[01:33:36.360 --> 01:33:38.760] And do we know what this person did?
[01:33:38.760 --> 01:33:43.480] I don't know exactly how much of the technical details were released.
[01:33:43.480 --> 01:33:48.840] So, these girls are born mosaic, but what's their phenotype with respect to CCR5?
[01:33:48.840 --> 01:33:52.520] So, some of their immune cells have CCR5 and some don't.
[01:33:52.520 --> 01:33:57.800] So, that means that they can be infected by HIV for those cells that are CCR5 positive.
[01:33:57.800 --> 01:34:05.400] But it probably means that they'll never die of AIDS because they will always maintain a population of T cells that are not susceptible.
[01:34:05.400 --> 01:34:15.320] They could be HIV-infected, but they'll never have a T cell level that falls so low that their CD4 count gets below whatever the threshold is for AIDS.
[01:34:15.320 --> 01:34:15.640] Right.
[01:34:15.960 --> 01:34:20.200] Okay, but the world really responded harshly to this, correct?
[01:34:20.200 --> 01:34:20.680] Correct.
[01:34:20.680 --> 01:34:21.640] That's right.
[01:34:21.640 --> 01:34:23.800] What was the fallout for this scientist?
[01:34:23.800 --> 01:34:29.160] The reason that there was a huge backlash is because there really wasn't any there was no medical need to do this.
[01:34:29.160 --> 01:34:29.480] Correct.
[01:34:29.720 --> 01:34:30.200] That's right.
[01:34:30.200 --> 01:34:31.560] It's not justified.
[01:34:31.560 --> 01:34:39.080] So, the scientific and also medical community, everyone voiced their concern about this ethical issue that just occurred.
[01:34:39.080 --> 01:34:43.400] And I think the scientist was put under house arrest for a while.
[01:34:43.400 --> 01:34:50.480] And this incident also really motivated much more ethical discussions around gene editing.
[01:34:50.640 --> 01:34:54.480] There was ethical discussion before, but it really focused the issue.
[01:34:54.480 --> 01:35:07.760] There were multiple working groups that were established, multinational working groups between the different national academies, U.S., China, UK, as well as the WHO had several working groups.
[01:35:07.760 --> 01:35:16.160] And I think in the U.S., there is legal regulation that prevents a germline modification, but it's certainly not the case internationally.
[01:35:16.480 --> 01:35:20.080] What do you think is being worked on outside of the U.S.
[01:35:20.240 --> 01:35:24.480] in places where there's no regulation with respect to germline mutation?
[01:35:24.480 --> 01:35:35.440] So, for example, are people trying to basically say, look, we're going to get rid of APOE4 isoform and make it APOE3 or 2 isoform?
[01:35:35.440 --> 01:35:37.840] I mean, what is the extent or ambition of people?
[01:35:37.840 --> 01:35:43.120] Are we going to delete LPA genes so that there's no LP little A phenotype?
[01:35:43.120 --> 01:35:49.360] I mean, you could make a case that a lot of these things would unidirectionally improve human health.
[01:35:49.360 --> 01:35:50.800] What are people working on?
[01:35:50.800 --> 01:35:53.200] And then let's talk a little bit about the ethics.
[01:35:53.200 --> 01:35:59.120] I don't know what is happening internationally because people are not publicizing their work.
[01:35:59.120 --> 01:36:09.520] But I think what is known is that there is international consensus that we don't want to do these types of embryo germline editing right now.
[01:36:09.840 --> 01:36:12.320] Even for things where it really matters.
[01:36:12.320 --> 01:36:15.200] So the APOE4 and the LPA don't.
[01:36:15.200 --> 01:36:16.480] You don't have to do that.
[01:36:16.480 --> 01:36:19.040] But what about Huntington's?
[01:36:19.040 --> 01:36:26.160] What about inborn errors of metabolism where children are going to probably die during infancy?
[01:36:26.160 --> 01:36:30.680] Cystic fibrosis, things for which we have no viable treatments.
[01:36:30.680 --> 01:36:37.400] Is the international consensus still that this type of gene editing will not be pursued?
[01:36:37.720 --> 01:36:41.800] I think there's a lot of discussion going on, so it's certainly not a settled issue.
[01:36:41.800 --> 01:36:54.840] But what I think scientists have all come to terms with is that the technology still needs much more validation and development before it's ready for application in this germline editing setting.
[01:36:54.840 --> 01:37:07.640] The efficiency, the specificity, both need to be optimized further in order for there to be any chance of a germline therapy having the intended effect.
[01:37:07.960 --> 01:37:21.240] And how much of that do you think is the main reason for the hesitation here versus the slippery slope argument, which says, yes, of course those applications are absolutely worth pursuing.
[01:37:21.240 --> 01:37:28.200] But if we do that, how far are we from doing APOE4, which may be a little bit gray?
[01:37:28.200 --> 01:37:41.960] And then what about identifying genes that are, frankly, not remotely related to lifespan or medical necessity, but instead relate to kind of the stuff that people talk about in science fiction?
[01:37:41.960 --> 01:37:45.160] Hey, this is a gene that might make my kid a little bit taller.
[01:37:45.160 --> 01:37:52.680] This is a gene that relates to intelligence or some other physical characteristic that is completely irrelevant to their health.
[01:37:52.680 --> 01:38:00.760] Is it more about this is dangerous, we don't know how to do it, or even if we figure this out, we don't want to open up a Pandora's box?
[01:38:01.080 --> 01:38:04.040] I think this is very much the debate that's happening right now.
[01:38:04.040 --> 01:38:11.800] There are patient groups who are advocates for using this, using gene editing in a germline setting to treat disease.
[01:38:11.800 --> 01:38:17.600] Assuming the technology is safe and efficacious, then we should do it because it will alleviate suffering.
[01:38:17.600 --> 01:38:19.440] So there is that argument.
[01:38:19.440 --> 01:38:32.480] There are also people arguing the slippery slope argument, which is if we allow for X and Y, then eventually we're going to be getting into uncharted territories with designer babies and so forth.
[01:38:32.480 --> 01:38:35.120] So that is very much the debate that's happening.
[01:38:35.120 --> 01:38:42.000] I think while that debate is happening, I think it's important to recognize that science is also progressing other fronts.
[01:38:42.000 --> 01:38:48.400] There are likely advances in science that will achieve the same outcome.
[01:38:48.400 --> 01:38:49.600] Without gene editing.
[01:38:49.600 --> 01:38:53.920] And so all those things, I think, are in competition with each other in the ethical debate.
[01:38:53.920 --> 01:38:56.560] And that's why this is such a complicated issue.
[01:38:56.880 --> 01:39:06.560] You're one of three or four people on the planet who not only know more about this technology, but have been personally responsible for it.
[01:39:06.560 --> 01:39:12.000] As such, I can't imagine you get to sit quietly during these debates.
[01:39:12.000 --> 01:39:18.080] Even though you're not an ethicist or a philosopher, I'm sure people want to understand what you think.
[01:39:18.080 --> 01:39:21.200] So how do you think about this?
[01:39:21.200 --> 01:39:26.720] Not from a technical standpoint, which we'll continue to talk about in a minute, but from an ethical standpoint.
[01:39:26.720 --> 01:39:41.040] Where do you think the line should be drawn in what we as a scientific or medical community permit with respect to gene editing in the germline, which is obviously the area where we're, I guess we should have made that point a little more clear at the outset.
[01:39:41.040 --> 01:39:45.040] We're talking about making an edit that will persist in perpetuity.
[01:39:45.040 --> 01:39:47.360] Yeah, I've thought about this issue a lot.
[01:39:47.360 --> 01:40:05.560] The things that are very easy to agree on is that if there is an obvious and important medical benefit, so especially for inborn aerosol metabolism or other known genetic mutations, if the technology is there, it's something that is definitely doable.
[01:40:05.560 --> 01:40:08.920] And I think it's okay to improve the lives of those patients.
[01:40:09.240 --> 01:40:10.760] Is that a mainstream view?
[01:40:10.760 --> 01:40:17.400] Or do you think that there is still a lot in the establishment who have not come around to that yet because of this slippery slope argument?
[01:40:17.720 --> 01:40:20.600] There are certainly people who have not come around to that.
[01:40:20.600 --> 01:40:29.800] And I think when making decisions like that, when making a medical decision like that, it's also important to consider what other alternative methods there may be.
[01:40:29.800 --> 01:40:38.920] For example, pre-implantation genetic testing is another method where you might be able to screen out embryos that have those mutations.
[01:40:38.920 --> 01:40:41.240] Which for IVF, we certainly can.
[01:40:41.240 --> 01:40:45.080] Obviously, for IVF, all of those diseases can be checked.
[01:40:45.080 --> 01:40:45.480] Right.
[01:40:45.800 --> 01:40:55.800] So then it gets to maybe a more complicated question, which is, is there a role for genetic engineering within in utero, for example?
[01:40:55.800 --> 01:41:02.120] Or is that just so technically challenging that it's much easier to focus on just the IVF side of things?
[01:41:02.440 --> 01:41:11.000] So certainly on the disease treatment, monogenic genetic disorder, IVF, I think that that is probably the most likely application.
[01:41:11.000 --> 01:41:14.680] Further down the road, there are some obstacles to solve.
[01:41:14.680 --> 01:41:17.000] One, we don't really understand biology enough.
[01:41:17.000 --> 01:41:23.000] So if you wanted to make our kid 30 IQ points smarter, we don't really know how to do that.
[01:41:23.000 --> 01:41:25.320] And so the biology needs to catch up.
[01:41:25.320 --> 01:41:37.400] But I do think that as society continues to develop, if the science is there and the technology is also there, I think people will opt to do that.
[01:41:37.720 --> 01:41:46.400] Sorry, meaning let's just assume, and by the way, I think this is personally, Feng, my view is I think these problems are way harder than people think.
[01:41:46.720 --> 01:42:07.120] I think these polygenic, nuanced traits like intelligence, athletic performance, resilience, happiness, all of those things I think are infinitely more complicated than we believe and probably just as environmental as they are genetic.
[01:42:07.120 --> 01:42:16.880] So even if you give somebody the right genetic template, if they're not in the right environmental surrounding, they won't necessarily even develop the way you hope.
[01:42:16.880 --> 01:42:17.680] Absolutely.
[01:42:17.680 --> 01:42:19.920] You might give somebody 30 more IQ points.
[01:42:19.920 --> 01:42:25.440] It doesn't translate to a person being demonstrably more intelligent if they aren't in the right environment.
[01:42:25.440 --> 01:42:29.760] That's my prediction: that stuff is way, way, way harder.
[01:42:29.760 --> 01:42:33.040] And the biology is complicated.
[01:42:33.040 --> 01:42:36.400] But it's more complicated than any lay person is thinking about.
[01:42:36.400 --> 01:42:40.640] And also, another thing is biology has a lot of compensation.
[01:42:40.640 --> 01:42:50.080] You might make some mutations that make the person smarter, but that could also increase cancer risk or impair athletic capability, make a person live shorter.
[01:42:50.080 --> 01:42:56.400] Those complicated trade-offs are things that I think people maybe don't fully appreciate.
[01:42:56.400 --> 01:43:01.200] Yeah, for me, it's there's a reason I'm not an ethicist.
[01:43:01.200 --> 01:43:06.800] Things seem much more obvious to me sometimes, which means I'm probably not a nuanced enough thinker on these regards.
[01:43:06.800 --> 01:43:12.480] But when it comes to a disease that's going to kill you with no other treatment, it seems like a no-brainer.
[01:43:12.480 --> 01:43:18.400] Genetic editing and genetic engineering should absolutely be considered a part of the equation.
[01:43:18.400 --> 01:43:37.880] At the other end of the spectrum, when we're talking about things that are truly superfluous, like intelligence, height, athletic performance, eye color, any sort of trait, those seem clearly like a horrible reason, not for the ethical reasons, like, well, what does that say about disparity?
[01:43:37.880 --> 01:43:38.600] Forget all that.
[01:43:38.600 --> 01:43:40.280] It's the reasons you just said.
[01:43:40.280 --> 01:43:43.160] We have no idea how complicated the system is.
[01:43:43.160 --> 01:43:47.560] If you're going to take a huge risk, there has to be a huge payoff.
[01:43:47.560 --> 01:43:50.360] The asymmetry of that strikes me as too much.
[01:43:50.360 --> 01:43:51.720] And then there's the gray area.
[01:43:51.720 --> 01:43:54.600] And I guess on the gray area, I do feel a bit nuanced, right?
[01:43:54.600 --> 01:43:57.720] Which is, should we just get rid of LPA genes?
[01:43:57.720 --> 01:44:08.120] Well, maybe, but then to your point, we have anti-sense oligonucleotide inhibitors that are just around the corner that effectively will get rid of LP little A.
[01:44:08.120 --> 01:44:10.040] They shut the gene off.
[01:44:10.040 --> 01:44:20.040] So why would we risk trying to eradicate this from the population when we can just give you a drug that does the same thing with fewer side effects and less permanence?
[01:44:20.040 --> 01:44:20.920] Exactly.
[01:44:20.920 --> 01:44:24.760] Or you can do it not in the germline, but just in the somatic cells.
[01:44:24.760 --> 01:44:26.760] That could also be beneficial.
[01:44:26.760 --> 01:44:31.560] And then there's yet the other area, which is: look at something like autism.
[01:44:31.560 --> 01:44:45.960] Autism is a genetic disease, despite what many people will have you believe, but the heritability of autism is so high that we know it's largely a genetic disease, but very polygenic, very subject to other things.
[01:44:45.960 --> 01:44:48.280] And do we really want to get rid of it?
[01:44:48.280 --> 01:44:49.000] I don't know.
[01:44:49.000 --> 01:44:49.560] Maybe.
[01:44:49.560 --> 01:44:59.320] I mean, certainly when you see a child that's devastated by autism, who's nonverbal, who's harming themselves, it would be a very logical thing to say.
[01:44:59.320 --> 01:45:08.440] But when you start to move further from that end of the spectrum towards the end of the spectrum where people are quite functional with autism, you might say, well, I don't know.
[01:45:08.440 --> 01:45:11.160] It actually comes with some benefits as well.
[01:45:11.160 --> 01:45:16.320] And do we want to create a homogeneous society where everybody is the same?
[01:45:16.720 --> 01:45:18.000] I don't think we want to.
[01:45:18.000 --> 01:45:20.160] This to me is the crux of sophistication.
[01:45:20.160 --> 01:45:20.560] Yeah.
[01:45:20.560 --> 01:45:21.040] Yeah.
[01:45:21.040 --> 01:45:23.200] Right, whether it be mood.
[01:45:23.200 --> 01:45:27.440] Because that diversity is important for the human race as a whole.
[01:45:27.440 --> 01:45:33.680] That diversity is what will allow the human race to be resilient in the long run.
[01:45:33.680 --> 01:45:36.560] So I want to talk a little bit about your story going back.
[01:45:36.560 --> 01:45:38.000] So you grew up in China.
[01:45:38.000 --> 01:45:40.800] You came to the middle of the country, right?
[01:45:40.800 --> 01:45:41.680] Where did you grow up?
[01:45:41.680 --> 01:45:42.720] I grew up in Iowa.
[01:45:42.720 --> 01:45:46.000] So I moved to Des Moines, Iowa when I was 11 years old.
[01:45:46.000 --> 01:45:47.440] What brought your parents here?
[01:45:47.440 --> 01:45:48.800] My mother came to the U.S.
[01:45:48.800 --> 01:45:49.280] first.
[01:45:49.280 --> 01:45:52.640] She was an exchange scholar in computer science.
[01:45:52.640 --> 01:46:02.560] She had a chance to visit one of the schools in Iowa, and she saw that the educational system in Iowa was much more hands-on as opposed to Zhou memorization.
[01:46:02.560 --> 01:46:08.080] And because of that, she thought I would maybe benefit more from that type of instruction.
[01:46:08.080 --> 01:46:10.080] So she decided to stay in the U.S.
[01:46:10.080 --> 01:46:11.280] and immigrate here.
[01:46:11.600 --> 01:46:17.360] When did you start to become aware of your intellectual abilities, for lack of a better term?
[01:46:17.360 --> 01:46:19.520] I mean, was school always so easy for you?
[01:46:19.520 --> 01:46:23.600] Were you sort of breezing through high school, acing every test put in front of you?
[01:46:23.600 --> 01:46:24.960] Or what was it like?
[01:46:24.960 --> 01:46:27.440] I don't think I'm all that smart.
[01:46:27.440 --> 01:46:29.760] I've always been interested in science.
[01:46:29.760 --> 01:46:31.840] I've always been very curious.
[01:46:31.840 --> 01:46:38.080] So I think I've just always kind of followed my curiosity to do something that is enjoyable and fun.
[01:46:38.080 --> 01:46:41.920] What is really interesting is I didn't like biology at all.
[01:46:41.920 --> 01:46:44.000] You preferred physics and chemistry and mathematics?
[01:46:44.080 --> 01:46:44.720] Yeah, and math.
[01:46:44.720 --> 01:46:46.080] It was more logical.
[01:46:46.080 --> 01:46:50.960] Things that I can understand and build a mental model for, and then be able to predict things.
[01:46:50.960 --> 01:46:54.160] Biology was very much zoomed memorization.
[01:46:54.160 --> 01:46:59.360] So, when I first moved to Iowa in seventh grade, there was life science in middle school.
[01:46:59.360 --> 01:47:05.240] I remember it was really about memorizing trees and dissecting frogs and identifying anatomical parts.
[01:47:05.560 --> 01:47:08.360] It wasn't so much based on logic.
[01:47:08.360 --> 01:47:13.560] It was during seventh grade that I attended a Saturday enrichment class.
[01:47:13.560 --> 01:47:15.800] It was on molecular biology.
[01:47:15.800 --> 01:47:19.960] I didn't know what biology had to do with molecular things, so I went to it.
[01:47:19.960 --> 01:47:26.520] And in that class, that's when I started to learn about the advances in modern biology.
[01:47:26.520 --> 01:47:28.680] The teacher was very passionate.
[01:47:28.680 --> 01:47:33.080] He taught us about DNA, RNA, protein, essential dogma.
[01:47:33.080 --> 01:47:45.800] But he also had us do fun experiments like extracting DNA from strawberries and putting an antibiotic resistance gene into a bacteria so that it can survive on empicillin.
[01:47:45.800 --> 01:47:53.240] He also, at the same time, showed us this, I call it a documentary, but it's actually a Hollywood movie called Jurassic Park.
[01:47:53.240 --> 01:48:12.360] Because if you put yourself in my shoes at the time where you're learning all these molecular biology fundamentals, gene splicing, all that, and then watching Jurassic Park to see all those theories being so tangibly there to make a dinosaur.
[01:48:12.360 --> 01:48:17.960] You know, the movie really felt like a documentary, but it was also just so inspiring and exciting.
[01:48:17.960 --> 01:48:20.440] Because that's about when Jurassic Park came out.
[01:48:20.440 --> 01:48:21.720] It would have been the mid-90s.
[01:48:21.720 --> 01:48:22.200] Yes.
[01:48:22.200 --> 01:48:23.480] Which is when you were in middle school.
[01:48:23.480 --> 01:48:24.760] Yeah, it was 94.
[01:48:24.760 --> 01:48:30.280] So, anyway, so I saw that and I became really interested in learning more about molecular biology.
[01:48:30.280 --> 01:48:37.880] We also learned about gene therapy and the promise of using molecular biology to build rationally designed medicine.
[01:48:37.880 --> 01:48:40.600] So that really captivated my imagination.
[01:48:40.600 --> 01:48:42.360] The teacher was amazing.
[01:48:42.360 --> 01:48:44.040] His name is Ed Pelkington.
[01:48:44.040 --> 01:48:51.200] Was he only the teacher of the enrichment class, or did he also have a regular class that he taught in high school or middle school?
[01:48:51.200 --> 01:48:57.440] He was a consultant for school, and he was working with kids who are interested in science and technology.
[01:48:57.440 --> 01:49:04.640] But he remembered that both myself and also a few other students were really captivated by molecular biology.
[01:49:04.640 --> 01:49:14.080] And so by the time I started 10th grade, he came to us and he said, there's this really cool opportunity that you guys may want to take advantage of.
[01:49:14.080 --> 01:49:22.080] The Iowa Methodist Medical Center had just opened a gene therapy lab, and they have a volunteer program at a hospital.
[01:49:22.080 --> 01:49:27.600] And maybe you guys can apply and specify that you wanted to volunteer in the gene therapy lab.
[01:49:27.600 --> 01:49:29.120] And maybe you get to go there.
[01:49:29.120 --> 01:49:34.480] So we applied, me and three other kids, and we got admitted to the gene therapy lab.
[01:49:34.480 --> 01:49:36.960] They taught us how to do experiments.
[01:49:36.960 --> 01:49:46.960] As you remember, the very first experiment I did, where the scientist had me put the gene for a jellyfish protein called green fluorescent protein.
[01:49:46.960 --> 01:49:50.000] This is a protein that makes jellyfish glow at night.
[01:49:50.000 --> 01:49:52.480] I put this gene into a human cancer cell.
[01:49:52.480 --> 01:49:54.320] Just using an adenovirus?
[01:49:54.640 --> 01:49:56.000] Using just lipid.
[01:49:56.000 --> 01:49:59.760] We wrapped it up with a bit of fat, and then it got absorbed by the cell.
[01:49:59.760 --> 01:50:03.440] And so I did that the afternoon, the day before.
[01:50:03.760 --> 01:50:09.600] And then the following day, I went to the lab, and the scientist took me into this dark microscope room.
[01:50:09.600 --> 01:50:12.640] There was a beam of blue light coming out of the microscope.
[01:50:12.640 --> 01:50:17.280] We put the petri dish with the cells on it, and he told me to look into the eyepiece.
[01:50:17.280 --> 01:50:21.760] And I saw a field of green cells that were fluorescent.
[01:50:21.760 --> 01:50:22.720] That had taken up this gene.
[01:50:22.800 --> 01:50:24.160] It looked alien to me.
[01:50:24.160 --> 01:50:25.280] And you were in 10th grade.
[01:50:25.280 --> 01:50:26.480] I was in 10th grade.
[01:50:26.480 --> 01:50:33.080] And that moment just made me feel so inspired about what we were doing.
[01:50:33.560 --> 01:50:40.360] And this idea that we can use our knowledge to then start to engineer rationally new medicine.
[01:50:40.360 --> 01:50:42.520] From that moment on, I thought science is really cool.
[01:50:42.520 --> 01:50:45.160] I want to do more experimental science.
[01:50:45.160 --> 01:50:54.920] Do you ever just reflect on the direction your life took in response to something as seemingly arbitrary as where your parents chose to move?
[01:50:54.920 --> 01:51:10.920] Your good fortune to be in that enrichment class, and then your good fortune to have a very special teacher who not only did this incredible work with you, but then stayed as a mentor, got you to apply to this program as a sophomore in high school.
[01:51:10.920 --> 01:51:33.000] I mean, like, I'm sure you've heard the arguments for the remarkable set of circumstances that allowed Bill Gates and Paul Allen to be in the right place at the right time from the right high school that had a computer lab that took these people who were naturally quite brilliant, but more importantly, put them in an environment where they could sort of leapfrog ahead of the time.
[01:51:33.000 --> 01:51:35.240] I mean, do you see a parallel there?
[01:51:35.240 --> 01:51:37.400] I think I have been very fortunate.
[01:51:37.400 --> 01:51:44.600] And I think there are a couple of things that I, over time, have developed more and more gratefulness and appreciation for.
[01:51:44.600 --> 01:51:48.680] Number one, I think, is the importance of teachers.
[01:51:48.680 --> 01:52:03.640] I've been very fortunate that throughout my life, I've been just met with many teachers who care about their students and teachers who really want to find as many opportunities as possible to help their students develop.
[01:52:03.640 --> 01:52:06.440] They really cared about nurturing the next generation.
[01:52:06.440 --> 01:52:11.880] And I think having that and having experienced that was really a great fortune of mine.
[01:52:11.880 --> 01:52:14.360] The second, I think, is really about America.
[01:52:14.400 --> 01:52:17.360] I think having been able to immigrate to the U.S.
[01:52:17.360 --> 01:52:30.960] and to go through the American education system where there's really a mirror-based system where you can work hard and be able to learn and develop and have access to opportunities.
[01:52:30.960 --> 01:52:34.240] That is really special about America.
[01:52:34.240 --> 01:52:44.240] So I think education and having a system where it nurtures people who want to develop and work hard, I think has been my great fortune.
[01:52:44.240 --> 01:52:48.400] Do you think that had you grown up in China, it would have been different?
[01:52:48.400 --> 01:52:51.920] Not because obviously people in China don't go on to do amazing things.
[01:52:51.920 --> 01:52:53.760] It also produces great scientists.
[01:52:53.760 --> 01:53:07.280] But just again, speaking to the randomness of this beautiful situation with this amazing teacher and this program and all of the things that followed, do you think you could have still stumbled onto the direction you would have?
[01:53:07.280 --> 01:53:08.640] Maybe even in a different field?
[01:53:08.640 --> 01:53:14.080] I guess what I'm getting at is you're very modest, but obviously you're very special in what you've done.
[01:53:14.080 --> 01:53:23.040] I wonder if you think you would have done great things regardless, or do you think that, no, sometimes you have to be in the right place at the right time?
[01:53:23.040 --> 01:53:25.440] I think faith is important.
[01:53:25.440 --> 01:53:28.000] There was a lot of opportunity in China, too.
[01:53:28.000 --> 01:53:35.920] China from the 1980s all the way to now has developed exponentially, but I don't know where I would have ended up.
[01:53:35.920 --> 01:53:37.680] There were a lot more people in China.
[01:53:37.680 --> 01:53:39.040] Things were competitive.
[01:53:39.040 --> 01:53:40.960] And the education system is different.
[01:53:40.960 --> 01:53:42.800] I like to tinker with things.
[01:53:42.800 --> 01:53:45.760] And in China, it's much more memorization-based.
[01:53:45.760 --> 01:53:51.360] I don't know, but I'm very grateful for having had all of the teachers and opportunities here.
[01:53:51.360 --> 01:53:53.360] What did you study at Harvard for your undergrad?
[01:53:53.360 --> 01:53:55.520] I majored in chemistry and physics.
[01:53:55.520 --> 01:54:05.000] And you did that, obviously, knowing you were interested in genetics, but did you feel that you wanted a greater grounding in the physical sciences because of your ultimate ambitions?
[01:54:05.000 --> 01:54:08.840] Or why did you choose that as opposed to biology or molecular biology?
[01:54:08.840 --> 01:54:09.720] Exactly.
[01:54:09.720 --> 01:54:36.040] I knew I wanted to study biology and engineer biological systems, but I felt that biology was developing so rapidly and new information was accruing on a weekly basis with new papers and new studies that it's probably more beneficial if I study something that's not going to change very much from the time that I enrolled in college to the time I graduate.
[01:54:36.040 --> 01:54:39.960] And so chemistry and physics were much more established fields.
[01:54:39.960 --> 01:54:48.120] And it provided a scientific foundation that I think has been very tremendously helpful as I continue to work in biology.
[01:54:48.120 --> 01:54:58.200] And at the same time, I did work outside of classes in a biological lab where I was practicing biological experiments and reading the latest literature.
[01:54:58.200 --> 01:55:00.120] And I thought that was a nice combination.
[01:55:00.120 --> 01:55:05.960] And when you selected Stanford for your PhD, did you do so because of Carl?
[01:55:05.960 --> 01:55:07.320] Or was there another reason?
[01:55:07.320 --> 01:55:12.840] And then you ultimately, after a year or so of classwork, found your way into Carl's lab?
[01:55:12.840 --> 01:55:15.000] Carl hasn't started at Stanford yet.
[01:55:15.000 --> 01:55:26.280] When I was growing up, especially after moving to Iowa, I read about people like Steve Jobs and Bill Gates and internet revolution that was happening.
[01:55:26.280 --> 01:55:30.360] And so I had a special feeling about Silicon Valley.
[01:55:30.360 --> 01:55:32.120] I was very intrigued by it.
[01:55:32.120 --> 01:55:39.240] And so when I applied to graduate school, I thought Stanford, being in Silicon Valley, would be a really good place to be.
[01:55:39.400 --> 01:55:41.240] I made the decision that way.
[01:55:41.240 --> 01:55:55.680] How did you then make the decision to go back to the East Coast for your postdoc, back to Harvard MIT area, as opposed to stay in the Silicon Valley and pursue your passions there in closer approximation to industry?
[01:55:55.680 --> 01:56:02.640] Although, of course, everybody realizes Boston would clearly be not too far behind the Silicon Valley for biotech industry.
[01:56:02.640 --> 01:56:06.400] I think at the time it was probably the most interesting opportunity.
[01:56:06.400 --> 01:56:10.400] I should mention that Carl Dyseros is a phenomenal mentor.
[01:56:10.400 --> 01:56:25.440] I learned a tremendous amount from him, not only about doing science, but also how to just be a good contributing member of the scientific community with sharing of information and reagents and the transfer of knowledge to as many people as possible.
[01:56:25.440 --> 01:56:30.000] And he was also a great mentor where he just gave me an opportunity to try things.
[01:56:30.000 --> 01:56:36.720] So during graduate school, for many months, I was working on a biofuel project in Carl's lab.
[01:56:36.720 --> 01:56:37.760] What year was this?
[01:56:37.760 --> 01:56:40.240] This was 2007, 2008.
[01:56:40.240 --> 01:56:42.880] So it was an energy crisis, oil crisis.
[01:56:42.880 --> 01:56:44.640] Biofuel was an important thing.
[01:56:44.880 --> 01:56:48.320] That's what Alex Horavius and I were working at at the exact same time.
[01:56:48.320 --> 01:56:48.800] Right.
[01:56:48.800 --> 01:56:49.680] 2008.
[01:56:49.680 --> 01:56:52.240] Around that time, I thought this was an interesting problem.
[01:56:52.240 --> 01:56:54.560] So I explored that in Carl's lab.
[01:56:54.560 --> 01:57:01.120] We thought maybe we could even go and raise venture capital and if we had something working out well.
[01:57:01.120 --> 01:57:05.440] But it turned out that around 2008, the financial crisis also hit.
[01:57:05.440 --> 01:57:10.880] And so a lot of those opportunities at that moment seem to have just vanished.
[01:57:10.880 --> 01:57:15.760] But at that same time, I was contacted by someone from the Harvard Society Fellows.
[01:57:15.760 --> 01:57:18.880] And they said, We liked your work as a graduate student.
[01:57:18.880 --> 01:57:24.000] Would you be interested in coming to the Society of Fellows and just explore science here?
[01:57:24.160 --> 01:57:25.680] Thought that was interesting.
[01:57:25.680 --> 01:57:31.480] They said, We'll give you a stipend, and you're not expected to do anything specific.
[01:57:31.480 --> 01:57:35.240] You can just come and hang out and think about interesting things.
[01:57:29.920 --> 01:57:36.920] So I applied and went there.
[01:57:37.240 --> 01:57:41.320] That's where I started to explore the gene editing technology.
[01:57:41.640 --> 01:57:44.440] So you're barely 40 years old.
[01:57:44.440 --> 01:57:47.720] You're one of the most prominent scientists in your field.
[01:57:47.720 --> 01:57:50.760] Are you optimistic about the state of science?
[01:57:50.760 --> 01:57:55.960] And I'll point out two things that are negative that you don't need to comment on because I don't want to put you in hot water.
[01:57:55.960 --> 01:58:00.040] But there's been a clear attack on meritocracy in the United States.
[01:58:00.040 --> 01:58:04.680] So a lot of the opportunities that served you well might not have served you well today.
[01:58:04.680 --> 01:58:08.680] They might not have been available to you today for various reasons, including your race.
[01:58:08.680 --> 01:58:24.920] And similarly, I think despite all of the remarkable scientific advances during COVID, there was also a loss of public faith in science during COVID, where some of the lines between science and advocacy got blurred greatly, in addition to a whole bunch of other things.
[01:58:24.920 --> 01:58:35.640] So in other words, I guess what I'm saying is there are a lot of things today that don't look as promising as they did maybe 10 years ago vis-a-vis the field and just sort of the state of the art.
[01:58:35.640 --> 01:58:40.120] Despite those things, do you remain as optimistic as ever or do you have concerns?
[01:58:40.120 --> 01:58:41.320] I am an optimist.
[01:58:41.320 --> 01:58:48.040] I think that's the only way to be, because if you're not optimistic, then there's only a downside when you are like that.
[01:58:48.040 --> 01:58:49.800] But I am optimistic about science.
[01:58:50.120 --> 01:58:54.840] I'm so excited about all of the rapid advances that are happening.
[01:58:54.840 --> 01:59:01.640] Knowledge about biology, about the human body, physiology, is accumulating at a very rapid pace.
[01:59:01.640 --> 01:59:06.280] Every week, there are interesting and exciting discoveries that are being reported.
[01:59:06.280 --> 01:59:20.880] And our technologies for studying biological systems are also accelerating with sequencing technology development to protein mass spectrometry to gene editing to new microscopy.
[01:59:21.200 --> 01:59:28.080] That is allowing us to collect new and more rich data cheaper and faster.
[01:59:28.080 --> 01:59:29.360] And then there's AI.
[01:59:29.360 --> 01:59:45.840] With the advance of AI and larger computing platforms, we can analyze and learn and build models around those data that are much more powerful and much more predictive and generative than ever before.
[01:59:46.160 --> 02:00:03.680] And that, combined with future advances in robotics, where we can have automation of experimentation and maybe even building a closed-loop system where AI and with human intervention can design and iterate our experiments rapidly.
[02:00:03.680 --> 02:00:09.600] I think it's going to accelerate science and medicine and human health beyond our imagination.
[02:00:09.600 --> 02:00:11.840] So I think that's really exciting.
[02:00:11.840 --> 02:00:17.280] At the same time, I think we need to continue to motivate people's interest in science.
[02:00:17.280 --> 02:00:21.040] We need to make sure that the best talent are going into science.
[02:00:21.040 --> 02:00:24.400] Do you worry that we've lost the focus on STEM?
[02:00:24.400 --> 02:00:26.880] I mean, obviously, people talk about the heyday of science, right?
[02:00:26.880 --> 02:00:36.560] During the 1960s, when kids growing up saw the space program, saw the Cold War, the best and the brightest wanted to do science.
[02:00:36.560 --> 02:00:38.160] They wanted to do engineering.
[02:00:38.160 --> 02:00:39.760] Do you feel that's slipping away?
[02:00:39.760 --> 02:00:42.400] And if so, what do you think is necessary to bring it back?
[02:00:42.400 --> 02:00:45.280] How do we get the absolute best people into STEM?
[02:00:45.600 --> 02:00:47.680] I think we can do more for sure.
[02:00:47.680 --> 02:00:51.600] I think we need to get kids more excited about science.
[02:00:51.600 --> 02:01:07.880] We need an education system to support kids who are curious and who have special interests in the science and technical things and to give them opportunities like the ones that I had to really explore those curiosities and interests.
[02:01:07.880 --> 02:01:12.440] When they're young, they are optimistic and they have energy.
[02:01:12.440 --> 02:01:22.200] And if we can nurture that and help them maintain that curiosity and energy, I think that's how we get the best people to continue in science.
[02:01:22.200 --> 02:01:24.120] Yeah, I couldn't agree with you more.
[02:01:24.120 --> 02:01:26.840] And this is not an investment that pays off in a year.
[02:01:26.840 --> 02:01:32.360] You've got to be doing this today, and then you'll get your payoff in a decade and two decades.
[02:01:32.360 --> 02:01:41.240] And sometimes those investments are really hard for society to make because it's hard to say, I got to pay money now for something and I don't get paid back for 10 or 20 years.
[02:01:41.240 --> 02:01:51.240] Those are hard things to accept, but I hope that people listening to this podcast appreciate, certainly in your case, what an enormous value it was for that investment.
[02:01:51.240 --> 02:01:53.400] In many ways, it's a great story of lots of things.
[02:01:53.400 --> 02:01:54.760] It's a story about immigration.
[02:01:54.760 --> 02:01:56.760] It's a story about science education.
[02:01:56.760 --> 02:01:59.080] It's a story about curiosity.
[02:01:59.080 --> 02:02:01.000] It's a story about the American dream.
[02:02:01.000 --> 02:02:04.120] So anyway, I'm really glad we finally got a chance to sit down.
[02:02:04.120 --> 02:02:05.880] It was absolutely worth the wait.
[02:02:05.880 --> 02:02:11.000] I'm excited to follow your undoubted continued success over the coming decades.
[02:02:11.000 --> 02:02:13.480] Thank you so much for having me here today.
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