1 00:00:00,000 --> 00:00:04,000 But what really it taught me is that we have to be open-minded. 2 00:00:04,000 --> 00:00:10,000 For the little bit that we know through our studies, there's just so much more that we do not know. 3 00:00:13,000 --> 00:00:16,000 The human brain is the most complex structure in the known universe, 4 00:00:16,000 --> 00:00:21,000 and we are in the middle of a scientific revolution to understand its inner workings. 5 00:00:21,000 --> 00:00:25,000 Join us for a conversation with world-renowned neuroscientists as they visit Rochester. 6 00:00:25,000 --> 00:00:31,000 I am Dr. John Foxe, Director of the Del Monte Institute for Neuroscience at the University of Rochester, 7 00:00:31,000 --> 00:00:34,000 and you are listening to Neuroscience Perspectives. 8 00:00:36,000 --> 00:00:41,000 I'm John Fox. I'm the Director of the Del Monte Institute for Neuroscience at the University of Rochester, 9 00:00:41,000 --> 00:00:44,000 and I'm welcoming you to Neuroscience Perspectives. 10 00:00:44,000 --> 00:00:50,000 I'm profoundly honored today to have a guest from the University of Wisconsin-Madison, 11 00:00:50,000 --> 00:01:01,000 Professor Qiang Chen, who is a neurodevelopmentalist, a neurodevelopmental researcher, scholar, and let's start, Qiang. 12 00:01:01,000 --> 00:01:03,000 Welcome to the University of Rochester. 13 00:01:03,000 --> 00:01:05,000 Thank you for the invitation. Glad to be here. 14 00:01:05,000 --> 00:01:07,000 It's absolutely fantastic to have you here. 15 00:01:08,000 --> 00:01:12,000 Let's talk about your trajectory into science first, 16 00:01:12,000 --> 00:01:18,000 and we'll get into the meat and potatoes of the science that you do fairly quickly. 17 00:01:18,000 --> 00:01:22,000 You hail from China. Do you want to tell us a little bit, just like your little biography, 18 00:01:22,000 --> 00:01:26,000 how you got into science, how you started out, how did you end up in the United States of America? 19 00:01:26,000 --> 00:01:33,000 So I was born in Beijing, China, and I grew up in the city and stayed in the city, 20 00:01:33,000 --> 00:01:41,000 go through all the schools, including college, Peking University, one of the famous ones, and never left the city. 21 00:01:41,000 --> 00:01:47,000 And then the next move I made is across the ocean and coming to the United States for graduate school. 22 00:01:47,000 --> 00:01:55,000 At the time, the United States was certainly viewed as the place if you want to pursue research, 23 00:01:55,000 --> 00:01:58,000 pursue a science education at the highest level. 24 00:01:58,000 --> 00:02:04,000 And it's a very natural move to say I want to go to graduate school in the United States, 25 00:02:04,000 --> 00:02:10,000 and I was fortunate to get into University of Pennsylvania School of Medicine to pursue my PhD. 26 00:02:10,000 --> 00:02:21,000 And that's where I got interested into neuroscience and got a broad training in neuroscience, neurobiology, neurodevelopment, and then went on from there. 27 00:02:21,000 --> 00:02:33,000 And I think the hook there was a very interesting neurodevelopment class, and my PhD mentor, Rita Balich Gordon, 28 00:02:33,000 --> 00:02:39,000 fantastic, outstanding molecular and cellular neuroscientist. 29 00:02:39,000 --> 00:02:45,000 She was studying synaptic elimination using the neuromuscular junction system. 30 00:02:45,000 --> 00:02:54,000 They have developed this nice imaging tool where you could label these different neurons with different color, 31 00:02:54,000 --> 00:03:00,000 and then at the neuromuscular junction that synapse between muscle fiber and the neurons, 32 00:03:00,000 --> 00:03:05,000 you can see the input from these different neurons, and they compete with each other, 33 00:03:05,000 --> 00:03:12,000 because initially you have multiple neurons innovating a single muscle fiber, and then later on through development, you get one. 34 00:03:12,000 --> 00:03:19,000 It's a one-on-one relationship. That's to ensure function. Whenever the neuron fires, the muscle contracts, you can breathe. 35 00:03:19,000 --> 00:03:27,000 So that imaging experiment was really what hooked me because you could see such an intricate process right in front of your eyes, 36 00:03:27,000 --> 00:03:32,000 and with this developmental trajectory and with this important functional implication. 37 00:03:32,000 --> 00:03:36,000 So it's like, wow, you could do that, and the neuroscience is so cool. 38 00:03:36,000 --> 00:03:43,000 So then I wanted to join her lab to study that process, and it was a really amazing journey. 39 00:03:43,000 --> 00:03:46,000 Amazing. I want to pick up on two things that you said there. 40 00:03:46,000 --> 00:03:55,000 One that struck me, of course, is an individual in your life that really inspired you, and you mentioned Professor Rita Balich Gordon. 41 00:03:55,000 --> 00:04:03,000 And then the other piece would be arriving across the ocean, as you put it, from China to the University of Pennsylvania. 42 00:04:03,000 --> 00:04:10,000 So you arrive in Philadelphia, and sort of the cultural change, and how was it navigating that? 43 00:04:10,000 --> 00:04:12,000 So maybe you could speak to those two things. 44 00:04:12,000 --> 00:04:20,000 To get at the first question, the first thing, my PhD mentor, I think that is quite remarkable. 45 00:04:20,000 --> 00:04:29,000 When I defended my thesis, I thanked a few people. I said there's this famous saying in Chinese that for every successful man, 46 00:04:29,000 --> 00:04:37,000 there's a woman behind him. So I said in my career up to this point, there are these significant people. 47 00:04:37,000 --> 00:04:45,000 And I thanked my mom, and I thanked Rita. I said this is really fortunate because if you don't have that one person in your career, 48 00:04:45,000 --> 00:04:56,000 of course many people, but those important moments, we're lucky to have those people to guide us and to lead us and to support us. 49 00:04:56,000 --> 00:05:06,000 And I cannot say how much I'm grateful for those people along the way. So that's really, really important. 50 00:05:06,000 --> 00:05:12,000 But I think what she taught me is not only how to think like a scientist, how to do critical thinking. 51 00:05:12,000 --> 00:05:19,000 I think the thing she said the most to me is to get a life. Don't be in the lab all the time. 52 00:05:19,000 --> 00:05:26,000 And experiments may work or may not work, and you have your frustrations, your fair share of that. 53 00:05:26,000 --> 00:05:32,000 But if you don't have a life outside the lab, this is really going to be difficult for you in the long run. 54 00:05:32,000 --> 00:05:41,000 So that actually saguaged into your second question really nicely, is that how I coped with the cultural change, 55 00:05:41,000 --> 00:05:47,000 well, I didn't have to because at that moment we were pretty much in the lab all the time. 56 00:05:47,000 --> 00:05:54,000 We didn't really go to a lot of concerts and we didn't really do a lot of these things outside the lab. 57 00:05:54,000 --> 00:06:00,000 So for me, it didn't really change much. Of course, your people, your surrounding changed. 58 00:06:00,000 --> 00:06:06,000 And I still remember that July the 4th concert in front of the Art Museum in Philadelphia, 59 00:06:06,000 --> 00:06:13,000 where you have these incredible band playing and you have the fireworks and then you have all of these sensory inputs 60 00:06:13,000 --> 00:06:18,000 all coming at you at the same time and the people around you drinking beers, dancing. 61 00:06:18,000 --> 00:06:24,000 I was like, oh, so this is a really interesting experience that we never had in China. 62 00:06:24,000 --> 00:06:32,000 So I mostly focused on the positive things at the get-go and then sort of spent most of the time in the lab 63 00:06:32,000 --> 00:06:36,000 and didn't really have much of a life outside. It's interesting, right? 64 00:06:36,000 --> 00:06:43,000 The lab provides that sort of warm space to transition. Yeah, it was really a great place. 65 00:06:43,000 --> 00:06:48,000 And I really like the point that you make about Rita, who is, of course, a giant in our field 66 00:06:48,000 --> 00:06:53,000 and known to many people in the neurosciences and the part of standing on the shoulder of giants. 67 00:06:53,000 --> 00:06:58,000 It's these mentors that pick you up, mentor you in your science career, but also in your life. 68 00:06:58,000 --> 00:07:03,000 And how to navigate the space is really, that's a beautiful story actually. Thanks for sharing that. 69 00:07:03,000 --> 00:07:06,000 So let's dive into your science. I have a little of my little crib notes here. 70 00:07:06,000 --> 00:07:16,000 I was wondering what role did your early work in BDNF protein play in the drug that's pending FDA approval for Rett syndrome currently? 71 00:07:16,000 --> 00:07:22,000 So actually, we have a bit of unpacking to do here. We need to start with some words about Rett syndrome, 72 00:07:22,000 --> 00:07:26,000 because not everybody who will be tuning in will know exactly what Rett syndrome is. 73 00:07:26,000 --> 00:07:29,000 And what drew you to studying that particular disease? 74 00:07:29,000 --> 00:07:33,000 Right. So this is actually a quite devastating neurodevelopmental disorder, 75 00:07:33,000 --> 00:07:39,000 and it's caused by a mutation in a single gene called methyl CBG binding protein 2. 76 00:07:39,000 --> 00:07:46,000 The gene is on the X chromosome, and so mostly the patients we have are girls. 77 00:07:46,000 --> 00:07:52,000 And it's not that uncommon. One out of 10 to 15,000 girls, you have one Rett syndrome girl. 78 00:07:52,000 --> 00:07:56,000 And when I say it's devastating, it's that in twofold. 79 00:07:56,000 --> 00:08:04,000 One is the disease obviously affects many of the functions of the person, and these people are highly debilitated. 80 00:08:04,000 --> 00:08:12,000 But the other aspect of this is a heart disease is that most of these girls, they are born grossly normal, so just like any other girl. 81 00:08:12,000 --> 00:08:20,000 And as parents, you have a lovely daughter, and then between maybe three months to three, five years, 82 00:08:20,000 --> 00:08:30,000 there is that onset of the disease that's quite sudden in that they almost feel like overnight things changed. 83 00:08:30,000 --> 00:08:38,000 It's like a switch. And then these girls, they will lose many of the developmental milestones they have obtained and then just regress. 84 00:08:38,000 --> 00:08:44,000 So that's a key feature of the disease. And it's very hard for people to lose something. 85 00:08:44,000 --> 00:08:49,000 If you never had it, that's fine, but if you had something great and then you lost it, it's so hard to take. 86 00:08:49,000 --> 00:08:57,000 So it's a heart disease. And then the way I got into the disease is a little bit by luck, 87 00:08:57,000 --> 00:09:04,000 because after my PhD training, I was interested in neurodevelopment and neuroscience, how the brain works. 88 00:09:04,000 --> 00:09:17,000 And I thought for my independent career, the best way to settle on important question is to find out what are the functionally most important genes. 89 00:09:17,000 --> 00:09:25,000 And the way we do that is mostly in science is to take it away, use genetic approaches to remove the gene 90 00:09:25,000 --> 00:09:33,000 and see whether you cause severe phenotypes. And then if you do something very important, if you don't, maybe it's not so much. 91 00:09:33,000 --> 00:09:40,000 So I wanted to have a genetics toolbox. And that's where I did my postdoc with Rudolf Janisch, 92 00:09:40,000 --> 00:09:49,000 really a giant in mouse genetics and many, many things. And I went to his lab to learn mouse genetic toolbox. 93 00:09:49,000 --> 00:09:54,000 And the time I joined the lab, they have just made the model for Rett syndrome. 94 00:09:54,000 --> 00:10:01,000 And that's a quite exciting time, I think, Huda Zoghbi's work in 1999, 2000. 95 00:10:01,000 --> 00:10:06,000 That's when they identified the gene as the single cause of the disease. 96 00:10:06,000 --> 00:10:12,000 And then a year later, that's when Rudolf's lab and Adrian Burr's lab, they published knockout mouse, 97 00:10:12,000 --> 00:10:17,000 MECP2 knockout and mimic the disease. So that's when I joined the lab. 98 00:10:17,000 --> 00:10:27,000 And then Rudolf's lab is again epigenetics, genetics. There's been neuroscientists in the lab, but not the main focus. 99 00:10:27,000 --> 00:10:31,000 As a neuroscientist coming in and they have this disease model, 100 00:10:31,000 --> 00:10:36,000 it's perfect for me to follow the previous postdoc's work and the study. 101 00:10:36,000 --> 00:10:42,000 I suppose one of the great things about the Rett mouse model is sometimes the mouse models can be a little bit disappointing 102 00:10:42,000 --> 00:10:47,000 in that they don't recapitulate the disease that we see in the human patients. 103 00:10:47,000 --> 00:10:56,000 But in the case of the Rett mouse, the phenotype is very strongly similar to what we see in the patients with the midline ringing and so on. 104 00:10:56,000 --> 00:10:58,000 Yeah, that's really small brain. 105 00:10:58,000 --> 00:11:01,000 Gives you something to shoot at as well. 106 00:11:01,000 --> 00:11:11,000 Yeah, I think we have as a field benefited tremendously from those models because if you search Rett syndrome in 2001, 107 00:11:11,000 --> 00:11:16,000 you probably get double digit papers, mostly talking about the disease itself. 108 00:11:16,000 --> 00:11:22,000 But now if you search, you probably got tens of thousands of papers because the field just ballooned 109 00:11:22,000 --> 00:11:25,000 because the availability of those models, you have a handle, like you said. 110 00:11:25,000 --> 00:11:30,000 You can really study the mechanism and then you can generate new knowledge. 111 00:11:30,000 --> 00:11:38,000 So I was fortunate, again, you know, you got to be lucky sometimes to be at the right time and the right place. 112 00:11:38,000 --> 00:11:40,000 And this is one of those examples. 113 00:11:40,000 --> 00:11:48,000 And going back to what you're saying, my earlier work during postdoc training in the end user's lab studying the role of BDNF in Rett syndrome. 114 00:11:48,000 --> 00:11:56,000 And that, again, is another teaching moment, I think, for me going into my independent career. 115 00:11:56,000 --> 00:11:59,000 I have to say that's the paper actually got me a job. 116 00:11:59,000 --> 00:12:03,000 But what I learned from it, it's not just the work itself. 117 00:12:03,000 --> 00:12:05,000 It's more than that. 118 00:12:05,000 --> 00:12:13,000 This is a gene at the time believed to be a gene that represses transcription. 119 00:12:13,000 --> 00:12:18,000 And then if you don't have it, then you probably have genes whose level will go up. 120 00:12:18,000 --> 00:12:23,000 And what I saw in the Rett syndrome mouse is that the BDNF level is down. 121 00:12:23,000 --> 00:12:24,000 It's decreased. 122 00:12:24,000 --> 00:12:27,000 This is obviously a very important neurotrophic factor. 123 00:12:27,000 --> 00:12:29,000 Do you want to quickly say something about what BDNF does? 124 00:12:29,000 --> 00:12:32,000 Oh, yeah. It's a brain derived neurotrophic factor. 125 00:12:32,000 --> 00:12:37,000 It's a major trophic factor for the brain development and function. 126 00:12:37,000 --> 00:12:45,000 And that factor being lower certainly makes sense for the disease. 127 00:12:45,000 --> 00:12:50,000 There's a lot of developmental delays and sort of atrophic phenotypes. 128 00:12:50,000 --> 00:12:56,000 But from the fundamental aspect, when you think of MSP2 as a repressor, 129 00:12:56,000 --> 00:13:00,000 why would you have something that goes down when you don't have a repressor? 130 00:13:00,000 --> 00:13:05,000 So there's a lot of debate in the field at the time when I made the observation. 131 00:13:05,000 --> 00:13:08,000 I go to meetings, people ask me, well, it doesn't make sense, right? 132 00:13:08,000 --> 00:13:10,000 How could you make sense of your work? 133 00:13:10,000 --> 00:13:13,000 You don't have a repressor and then this gene goes down. 134 00:13:13,000 --> 00:13:17,000 And it was very hard for me actually to try to discuss. 135 00:13:17,000 --> 00:13:19,000 You could say, wave your hands. 136 00:13:19,000 --> 00:13:22,000 This is an indirect effect and this and that. 137 00:13:22,000 --> 00:13:25,000 But I think what people were questioning more is, 138 00:13:25,000 --> 00:13:32,000 if you don't have something that's logical, how would it even be useful? 139 00:13:32,000 --> 00:13:39,000 So how would it really make it easier to develop a therapy in the future? 140 00:13:39,000 --> 00:13:44,000 So I was really thinking hard. 141 00:13:44,000 --> 00:13:52,000 But BNNF is a very hard molecule to make a therapy out of in terms of the kinetics, 142 00:13:52,000 --> 00:13:55,000 in terms of the pharmacological property. 143 00:13:55,000 --> 00:14:03,000 And so Mir Gangar Soor at the MIT that time, who is an expert in BNNF, 144 00:14:03,000 --> 00:14:09,000 and he told me, well, you know, this BNNF is never going to be a therapy. 145 00:14:09,000 --> 00:14:17,000 But this general direction of trophic support is probably a valid direction. 146 00:14:17,000 --> 00:14:19,000 Right, it's practical there. 147 00:14:19,000 --> 00:14:23,000 Yeah, and then IGF, for example, is a trophic factor, 148 00:14:23,000 --> 00:14:28,000 a growth factor that has been approved by FDA as a drug to treat something else. 149 00:14:28,000 --> 00:14:33,000 You know, let's look at the general effect and try IGF-1 and see if this works. 150 00:14:33,000 --> 00:14:38,000 And he actually started a collaboration with Ruel's lab to look at the role of IGF-1. 151 00:14:38,000 --> 00:14:47,000 And another surprise is that, well, it did have some modest effect, but it's modest. 152 00:14:47,000 --> 00:14:55,000 And then they tested the tripeptide of IGF-1, just 3-peptide of IGF-1, 153 00:14:55,000 --> 00:15:00,000 known to in some cases have similar effect as IGF-1. 154 00:15:00,000 --> 00:15:04,000 And they tried that in the mouse model and also had a modest effect. 155 00:15:04,000 --> 00:15:06,000 There were only those two papers. 156 00:15:06,000 --> 00:15:11,000 And again, the field is questioning the validity of the study. 157 00:15:11,000 --> 00:15:14,000 You know, mouse work, you know, you could see effect, 158 00:15:14,000 --> 00:15:17,000 but who knows whether it's going to happen in humans. 159 00:15:17,000 --> 00:15:20,000 And you want to have a strong foundation of your mouse work. 160 00:15:20,000 --> 00:15:24,000 And then people were saying, well, you don't have any change in expression of IGF-1. 161 00:15:24,000 --> 00:15:29,000 You don't have any change of IGF-1 receptor expression in the mouse model. 162 00:15:29,000 --> 00:15:32,000 How could this even work conceivably? 163 00:15:32,000 --> 00:15:34,000 Right, so there's a lot of doubt. 164 00:15:34,000 --> 00:15:40,000 So then, you know, what happened was because IGF-1, there's a drug available, 165 00:15:40,000 --> 00:15:45,000 tripeptide, there's Neurin, a company then later on bought by Acadia. 166 00:15:45,000 --> 00:15:50,000 They had a drug that's a modified version of the tripeptide, that's a truffinidide. 167 00:15:50,000 --> 00:15:53,000 That's a drug now pending approval at FDA. 168 00:15:53,000 --> 00:15:55,000 Because those things were all available, 169 00:15:55,000 --> 00:16:00,000 and there's some preclinical data in mouse models suggesting they may work. 170 00:16:00,000 --> 00:16:03,000 They just went on to do the trial. 171 00:16:03,000 --> 00:16:06,000 And then the IGF-1 trial didn't pan out, 172 00:16:06,000 --> 00:16:11,000 but the truffinidide trial actually gave pretty promising results. 173 00:16:11,000 --> 00:16:16,000 So you can think of all of these effects as indirect. 174 00:16:16,000 --> 00:16:20,000 But what really it taught me is that we have to be open-minded. 175 00:16:20,000 --> 00:16:23,000 For the little bit that we know through our studies, 176 00:16:23,000 --> 00:16:27,000 there's just so much more that we do not know. 177 00:16:27,000 --> 00:16:33,000 So if you are certain of a result, and it's fundamentally important, 178 00:16:33,000 --> 00:16:34,000 you should publish it. 179 00:16:34,000 --> 00:16:36,000 You should let people see it and debate it. 180 00:16:36,000 --> 00:16:38,000 Get it out there and allow it to be debated, yeah. 181 00:16:38,000 --> 00:16:40,000 And let people lean on it, and that's perfectly acceptable. 182 00:16:40,000 --> 00:16:43,000 And also people can do their own thing, 183 00:16:43,000 --> 00:16:45,000 make their own call based on their judgment. 184 00:16:45,000 --> 00:16:48,000 Pharmaceutical companies or biotech, they made their call. 185 00:16:48,000 --> 00:16:51,000 They said, this is enough evidence, and we're going to try it. 186 00:16:51,000 --> 00:16:55,000 Because if you didn't do those things, then you wouldn't even have a drug. 187 00:16:55,000 --> 00:16:59,000 This drug, I think, really has a very, very good chance of getting approved. 188 00:16:59,000 --> 00:17:04,000 Well, it must be extremely satisfying to do the basic fundamental work. 189 00:17:04,000 --> 00:17:06,000 And as you say, you haven't tied it up in a bow. 190 00:17:06,000 --> 00:17:08,000 There's a lot of unknowns in there. 191 00:17:08,000 --> 00:17:12,000 But it gets you right to the doorstep of delivering a potential treatment 192 00:17:12,000 --> 00:17:14,000 to children who need something now. 193 00:17:14,000 --> 00:17:19,000 These are children who have really a severe developmental disorder. 194 00:17:19,000 --> 00:17:21,000 And there's a philosophy in the two, right? 195 00:17:21,000 --> 00:17:24,000 You can't wait for everything to be perfect. 196 00:17:24,000 --> 00:17:27,000 Don't let perfect be the enemy of the good, right? 197 00:17:27,000 --> 00:17:29,000 Yeah, yeah, that's precisely what I was going to say. 198 00:17:29,000 --> 00:17:34,000 But it must be very satisfying to see this now at the doorstep of treatment in patients. 199 00:17:34,000 --> 00:17:37,000 Yeah, I'm actually very excited, because in my line of work, 200 00:17:37,000 --> 00:17:40,000 we interact with families as well. 201 00:17:40,000 --> 00:17:45,000 I think that really, from time to time, refocuses our effort. 202 00:17:45,000 --> 00:17:46,000 You have a meaning. 203 00:17:46,000 --> 00:17:51,000 So what you're doing is not like three years later, it may or may not help something. 204 00:17:51,000 --> 00:17:53,000 Some people are waiting, and they really need this. 205 00:17:53,000 --> 00:17:56,000 And then you're making an impact with your work. 206 00:17:56,000 --> 00:18:02,000 And that really is very important for me, and I'm sure for many of our colleagues as well. 207 00:18:02,000 --> 00:18:03,000 Exactly. 208 00:18:03,000 --> 00:18:09,000 One of the things, too, people will often ask me is that we work on Rett syndrome, as you do, 209 00:18:09,000 --> 00:18:14,000 but also even considerably rarer diseases, like some of the lysosomal storage diseases. 210 00:18:14,000 --> 00:18:19,000 Why are you working on a disease that impacts so few people? 211 00:18:19,000 --> 00:18:22,000 Why don't you work on autism or schizophrenia? 212 00:18:22,000 --> 00:18:24,000 But there's a very good reason. 213 00:18:24,000 --> 00:18:26,000 Do you want to say something about that? 214 00:18:26,000 --> 00:18:32,000 Obviously, for patients and their families, this is really devastating, and there's a need. 215 00:18:32,000 --> 00:18:39,000 For me, there's another angle than that, which is to going back to my training, 216 00:18:39,000 --> 00:18:42,000 to understand brain development and brain function. 217 00:18:42,000 --> 00:18:46,000 So for me, this is a way that the system is perturbed. 218 00:18:46,000 --> 00:18:52,000 And when the system is perturbed, it allows you to assess what those modulators 219 00:18:52,000 --> 00:19:00,000 and those factors are doing under a physiological condition, what they do normally. 220 00:19:00,000 --> 00:19:06,000 So that's an angle for me to get at how you understand this brain development and function. 221 00:19:06,000 --> 00:19:10,000 And there's a lot of interesting basic functions. 222 00:19:10,000 --> 00:19:15,000 This protein, for example, methyl-CBG-pyranate protein 2, it is so fascinating. 223 00:19:15,000 --> 00:19:16,000 It does so many things. 224 00:19:16,000 --> 00:19:21,000 People thought it was just binding to methylated Cg and be a repression of transcription, 225 00:19:21,000 --> 00:19:22,000 but it does so many more things. 226 00:19:22,000 --> 00:19:25,000 It's such a plutotrophic factor. 227 00:19:25,000 --> 00:19:29,000 And every time you learn a new function of this protein, 228 00:19:29,000 --> 00:19:35,000 you get excited about potentially learning new things about how the system works, 229 00:19:35,000 --> 00:19:38,000 the brain, the neurons, and the synapses. 230 00:19:38,000 --> 00:19:43,000 And one of the things we did early on unexpectedly is to find out that 231 00:19:43,000 --> 00:19:48,000 this protein can be modified post-translationally by fosylation. 232 00:19:48,000 --> 00:19:54,000 And that is a dynamic process, and it can be induced by extracellular signals, 233 00:19:54,000 --> 00:19:56,000 depending on the cell type. 234 00:19:56,000 --> 00:20:03,000 If you are a neuron, you express M-HCP2, and then the neural activity comes down the axon, 235 00:20:03,000 --> 00:20:10,000 and then the neuron gets excited, and this protein in the nucleus, M-HCP2, gets fosylated. 236 00:20:10,000 --> 00:20:17,000 And then it changes it properly and regulates a downstream effect through kinase pathways 237 00:20:17,000 --> 00:20:23,000 and then generates synaptic changes and circuit level changes as a switch. 238 00:20:23,000 --> 00:20:27,000 And then in different cell types, like neuroprogenitor cells, 239 00:20:27,000 --> 00:20:31,000 this same protein is expressed, and it can be fosylated, 240 00:20:31,000 --> 00:20:35,000 but this time it's triggered by a different signal, it's a growth signal. 241 00:20:35,000 --> 00:20:38,000 And again, once it's fosylated at the same site, 242 00:20:38,000 --> 00:20:41,000 then it triggers a different kinase pathway, 243 00:20:41,000 --> 00:20:45,000 and then it has different downstream transcriptional changes and effectors, 244 00:20:45,000 --> 00:20:52,000 and then the outcome would be proliferation and differentiation of that neuroprogenitor cell. 245 00:20:52,000 --> 00:20:55,000 So it's a very interesting molecular switch. 246 00:20:55,000 --> 00:20:59,000 And then if you think of this protein coding one-third of the genome everywhere, 247 00:20:59,000 --> 00:21:03,000 it could serve as another layer of epigenetic regulation. 248 00:21:03,000 --> 00:21:09,000 You know, you talk about histone code, what about an M-HCP2 fosylation code on top of that? 249 00:21:09,000 --> 00:21:13,000 And then you can have finer regulation of these downstream outputs. 250 00:21:13,000 --> 00:21:18,000 So that is a fascinating question that we have studied for a long time and published many papers. 251 00:21:18,000 --> 00:21:24,000 And then for me as a scientist, there are all these other things we could learn, 252 00:21:24,000 --> 00:21:31,000 getting in from the angle of this one disease and focusing on this one gene, this one protein. 253 00:21:31,000 --> 00:21:36,000 And that's the key, right? They're monogenic diseases, 254 00:21:36,000 --> 00:21:39,000 so you've got one gene that's been mutated, 255 00:21:39,000 --> 00:21:44,000 and it allows you to really focus right in on a cascade of events 256 00:21:44,000 --> 00:21:49,000 and a very elegant set of transcription patterns and codings and so on. 257 00:21:49,000 --> 00:21:53,000 But it gives you that ability to really winnow in on a distinct problem 258 00:21:53,000 --> 00:21:57,000 as opposed to trying to tackle, boil the ocean, as they say. 259 00:21:57,000 --> 00:22:04,000 Oftentimes, there are insights from studying one monogenic disease 260 00:22:04,000 --> 00:22:07,000 that then have implications for all the others as well, 261 00:22:07,000 --> 00:22:13,000 that you learn as you go that there are mechanisms and things that are generalizable. 262 00:22:13,000 --> 00:22:15,000 Yeah, precisely. 263 00:22:15,000 --> 00:22:19,000 So we're going to run out of time, and I want to ask you, what's on the horizon? 264 00:22:19,000 --> 00:22:24,000 What's next up for you? Where do you see the lab going and the work going over the next few years? 265 00:22:24,000 --> 00:22:26,000 Well, there's a lot of work going on. 266 00:22:26,000 --> 00:22:31,000 Obviously, we have these different platforms, experimental platforms like the mouse models, 267 00:22:31,000 --> 00:22:39,000 the human stem cell models, and slice culture models that we could use to study this one disease 268 00:22:39,000 --> 00:22:42,000 and apply on other fronts as well. 269 00:22:42,000 --> 00:22:51,000 But in terms of the disease, we're actually going more into treatment or drug screening. 270 00:22:51,000 --> 00:22:58,000 We have this human stem cell platform where we derive brain cell types from the patient cells, 271 00:22:58,000 --> 00:23:03,000 and then we do drug screens and RNAi screens to look at molecules and genes 272 00:23:03,000 --> 00:23:08,000 that could modify the disease outcome from the individual cell platform, 273 00:23:08,000 --> 00:23:10,000 and then we go back to the animal models to test. 274 00:23:10,000 --> 00:23:14,000 So that's certainly one major direction. There is a lot of ongoing work there. 275 00:23:14,000 --> 00:23:17,000 And another direction is integration. 276 00:23:17,000 --> 00:23:25,000 So obviously, we have done the genetic manipulation and looked at from the nucleus with the mutation 277 00:23:25,000 --> 00:23:31,000 to the cells, to the circuit, to the animal behavior of one level at a time. 278 00:23:31,000 --> 00:23:36,000 And recently, we have started work to look at multiple levels at the same time. 279 00:23:36,000 --> 00:23:42,000 So I'll talk a little bit about our recent work in calcium imaging in freely behaving animals, 280 00:23:42,000 --> 00:23:49,000 imaging relevant brain regions underlying certain behavior that is important for the disease, 281 00:23:49,000 --> 00:23:55,000 and then basically time-lock the two modalities at the same time. 282 00:23:55,000 --> 00:23:59,000 Basically, you look at the activities in the circuit. 283 00:23:59,000 --> 00:24:07,000 At the same time, the animal is doing a behavior, figuring out what the animal is thinking before it does it. 284 00:24:07,000 --> 00:24:10,000 So that type of integration. 285 00:24:10,000 --> 00:24:14,000 And then you could do, pair that with genetic and pharmacological manipulation 286 00:24:14,000 --> 00:24:20,000 to look at how these different modalities associate with each other and whether they change together. 287 00:24:20,000 --> 00:24:23,000 And that's from the outside. 288 00:24:23,000 --> 00:24:27,000 From inside the cells, we're doing some patch-seq experiment, 289 00:24:27,000 --> 00:24:32,000 where you'll be able to sequentially look at the electrical properties of the cell 290 00:24:32,000 --> 00:24:39,000 and the transcription program in that cell, and also the morphology of the cell through time and in space. 291 00:24:39,000 --> 00:24:44,000 So that really gives an opportunity to look at multiple levels at the same time 292 00:24:44,000 --> 00:24:49,000 and have a better understanding of the causality from one level to the next level. 293 00:24:49,000 --> 00:24:53,000 So that's where we're focusing our efforts, more integrated analysis. 294 00:24:53,000 --> 00:24:59,000 Of course, Rett syndrome is a focus of the lab, and a lot of the model we study has something to do with Rett syndrome. 295 00:24:59,000 --> 00:25:08,000 But that allows us to broaden our research into development and developmental disorders, such as autism. 296 00:25:08,000 --> 00:25:15,000 We live in a period now in the neurosciences where the tools from the molecular and cellular level 297 00:25:15,000 --> 00:25:21,000 right to the systems level are so exquisite that I don't know that people really appreciate what's ahead over the next decade. 298 00:25:21,000 --> 00:25:24,000 The insights that will undoubtedly come forward. 299 00:25:24,000 --> 00:25:30,000 And I'm so looking forward to continuing to follow the work from your lab. It's exceptional. 300 00:25:30,000 --> 00:25:32,000 I'm going to ask you one more question. 301 00:25:32,000 --> 00:25:40,000 Go back to the start. I know you're a scientist, but maybe let's get a little political for a moment. 302 00:25:40,000 --> 00:25:48,000 Because you were talking about coming from China to the US, where it was recognized at that time. 303 00:25:48,000 --> 00:25:51,000 And this is very much the same for my own trajectory. 304 00:25:51,000 --> 00:25:54,000 This was the great biomedical research engine here. 305 00:25:54,000 --> 00:26:00,000 And young, talented scientists came from all over the world to here. 306 00:26:00,000 --> 00:26:04,000 We live in difficult political times. We live in a time now where there's a war again in Europe. 307 00:26:04,000 --> 00:26:06,000 Who would have thunk it? 308 00:26:06,000 --> 00:26:13,000 And a time of tension between this great country and the great country you hail from. 309 00:26:13,000 --> 00:26:17,000 Do you worry about the scientific engine and the interchange of ideas? 310 00:26:17,000 --> 00:26:20,000 Do you have thoughts about that? 311 00:26:20,000 --> 00:26:21,000 I do worry. 312 00:26:21,000 --> 00:26:30,000 Over the last, in particular, the last couple of years, the relationship between China and the US has deteriorated. 313 00:26:30,000 --> 00:26:36,000 And I think we are already seeing the impact of that. 314 00:26:36,000 --> 00:26:42,000 Many of my colleagues have left the US. 315 00:26:42,000 --> 00:26:48,000 And we, more importantly, have a problem with the pipeline. 316 00:26:48,000 --> 00:26:55,000 And now we don't get as many Chinese students and post-docs in the labs. 317 00:26:55,000 --> 00:27:00,000 Because they have viewed this political environment as not favorable. 318 00:27:00,000 --> 00:27:05,000 Because when we come here, we didn't care about politics. 319 00:27:05,000 --> 00:27:09,000 We had our eyes focused on science. 320 00:27:09,000 --> 00:27:24,000 But now that the political environment has become so, I guess, radical, that people cannot afford to not look at it. 321 00:27:24,000 --> 00:27:31,000 So many of these young people, this graduate student candidates and post-doc candidate, they turn their attention elsewhere. 322 00:27:31,000 --> 00:27:38,000 So I've heard from many of my colleagues that it's in the past you post an ad for a postdoc. 323 00:27:38,000 --> 00:27:41,000 A lot of those applicants are from China now. 324 00:27:41,000 --> 00:27:42,000 It's not. 325 00:27:42,000 --> 00:27:44,000 It will be known. 326 00:27:44,000 --> 00:27:46,000 Literally right now there's none. 327 00:27:46,000 --> 00:27:55,000 And I'm hoping because in science, where I am, certainly at the University of Wisconsin-Madison, 328 00:27:55,000 --> 00:28:03,000 that collaborative and collegial environment, that diverse background of people is really a strength. 329 00:28:03,000 --> 00:28:12,000 Because when people work together and bring your different perspective together, it actually helps science to move forward. 330 00:28:12,000 --> 00:28:16,000 And that dialogue should never stop. 331 00:28:16,000 --> 00:28:18,000 And the collaboration should never stop. 332 00:28:18,000 --> 00:28:27,000 Because in a sense, I don't think you can do this in an isolated way. 333 00:28:27,000 --> 00:28:33,000 It can be healthy competition, no doubt, and that brings the best of people for sure. 334 00:28:33,000 --> 00:28:44,000 But this collaboration and cooperation really is key to doing some of these really important science work. 335 00:28:44,000 --> 00:28:46,000 And it has been successful in the past. 336 00:28:46,000 --> 00:28:48,000 I mean, the U.S. has been the go-to place. 337 00:28:48,000 --> 00:28:57,000 And all of these talents coming in from all over the world really has helped to sustain the scientific engine in this country. 338 00:28:57,000 --> 00:29:02,000 I hope that will continue to be the same way in the foreseeable future. 339 00:29:02,000 --> 00:29:06,000 Because those bright minds, when you see them, you just want to work with them. 340 00:29:06,000 --> 00:29:10,000 You don't view them as enemies and you want them to be on your side. 341 00:29:10,000 --> 00:29:12,000 And they want to be on your side as well. 342 00:29:12,000 --> 00:29:21,000 I mean, from the people of these countries, I don't really see any issues between the people. 343 00:29:21,000 --> 00:29:27,000 No, but as you say, when youngsters come here to do science, it's science that they had in mind. 344 00:29:27,000 --> 00:29:29,000 They weren't thinking about the politics. 345 00:29:29,000 --> 00:29:31,000 And that's what has changed. 346 00:29:31,000 --> 00:29:40,000 And I suppose one of the—I don't know that the American public fully appreciates how much the science engine has driven American economy 347 00:29:40,000 --> 00:29:42,000 and the success of the country. 348 00:29:42,000 --> 00:29:48,000 And a lot of it is on the backs of these diverse minds that came from all over the world to be here. 349 00:29:48,000 --> 00:29:54,000 And one of the upshots of this is of the current political climate is that that's drying up. 350 00:29:54,000 --> 00:30:00,000 I don't think people really understand that that's one of the—some of the collateral damage going on here. 351 00:30:00,000 --> 00:30:06,000 Very quickly, I wanted to come back to—and it's highly related, actually. 352 00:30:06,000 --> 00:30:13,000 You said something very elegant about diverse minds being the strength of a scientific engine or something along those lines. 353 00:30:13,000 --> 00:30:22,000 And I know actually you serve as a leader, a chair for the diversity component of your—what's called the IDDRC. 354 00:30:22,000 --> 00:30:26,000 So I think we have to explain what IDDRCs are very quickly to our audience. 355 00:30:26,000 --> 00:30:33,000 Maybe you could just finish—close out, if you wouldn't mind, with some comments about your role in that—or that aspect of your role. 356 00:30:33,000 --> 00:30:39,000 Right. So IDDRC stands for Intellectual and Developmental Disability Research Center. 357 00:30:39,000 --> 00:30:45,000 It's a national network of research centers, and it's certainly started in the 60s. 358 00:30:45,000 --> 00:30:52,000 And really, at the time, JFK, John F. Kennedy convened a committee to study the landscape. 359 00:30:52,000 --> 00:30:58,000 And because at that time, people with intellectual disability, they don't get much service. 360 00:30:58,000 --> 00:31:00,000 They're mostly institutionalized. 361 00:31:00,000 --> 00:31:03,000 There's not much research and service going on. 362 00:31:03,000 --> 00:31:09,000 And the recommendation of that presidential committee was to invest in some of these centers, 363 00:31:09,000 --> 00:31:14,000 to establish some of these centers across the country to do more research, 364 00:31:14,000 --> 00:31:17,000 because that's going to be the driver and the engine for discovery. 365 00:31:17,000 --> 00:31:21,000 And then you will be able to provide better service and all of that. 366 00:31:21,000 --> 00:31:27,000 And so now they're starting in the 60s, and 56 years later, 367 00:31:27,000 --> 00:31:30,000 now we have 15 such centers across the United States. 368 00:31:30,000 --> 00:31:35,000 I know you guys have one right here in Rochester, and you are the director of that center. 369 00:31:35,000 --> 00:31:42,000 And that's really an amazing resource, because that really is a nucleus for each of these companies 370 00:31:42,000 --> 00:31:52,000 to sort of rally the troop around the center to do research related to brain development and developmental disorders. 371 00:31:52,000 --> 00:32:02,000 And this is a fantastic network, and that was born with the National Institute of Child Health and Human Development. 372 00:32:02,000 --> 00:32:14,000 And basically, over 60 years, provided outstanding resource to these sites across the nation to focus on research in this area. 373 00:32:14,000 --> 00:32:23,000 And in recent years, I think this network has had more collaboration, 374 00:32:23,000 --> 00:32:28,000 not only at their respective sites, serve as a nucleus, but across the entire nation. 375 00:32:28,000 --> 00:32:33,000 These centers have joined forces to tackle some of these important scientific questions, 376 00:32:33,000 --> 00:32:41,000 because team science now is more of a mainstream now, because the questions are complex. 377 00:32:41,000 --> 00:32:46,000 You're unlikely going to be able to address it with one approach. You need multiple approaches. 378 00:32:46,000 --> 00:32:49,000 And then these centers are joining forces to do many things. 379 00:32:49,000 --> 00:32:55,000 And one of the things the centers wanted to join forces to address is the diversity of the scientific workforce. 380 00:32:55,000 --> 00:33:02,000 And of course, for the general public, diversity is a topic that needs no introduction. 381 00:33:02,000 --> 00:33:12,000 I have served in the role of the co-chair of this diversity and equity inclusion workgroup across the IDDRCs. 382 00:33:12,000 --> 00:33:24,000 And I think it's a really worthwhile effort when we share our experience across these sites to learn from each other what works, what doesn't, 383 00:33:24,000 --> 00:33:32,000 what are the resources available, and what are the tricks you could do to increase diversity in our workforce. 384 00:33:32,000 --> 00:33:40,000 And that, I think, is going to be, I guess, something we need to work hard together for quite some time 385 00:33:40,000 --> 00:33:46,000 before we can see the impact, because that involves people and it's a longer timeline. 386 00:33:46,000 --> 00:33:49,000 And there's many challenges there. 387 00:33:49,000 --> 00:33:59,000 But I think it's really a worthwhile effort that I felt at the time quite important to work as a group. 388 00:33:59,000 --> 00:34:04,000 So I volunteered to organize that group along with some other people. 389 00:34:04,000 --> 00:34:07,000 And we're making some progress now, although slowly. 390 00:34:07,000 --> 00:34:09,000 That's fantastic. Well, that's super important work. 391 00:34:09,000 --> 00:34:13,000 I want to thank you for being here in Rochester, Dr. Chang. 392 00:34:13,000 --> 00:34:17,000 It's an absolute honor and pleasure to have you. And thanks for your time here today. 393 00:34:17,000 --> 00:34:22,000 Well, thank you for the invitation. I look forward to talking to your faculty here. 394 00:34:22,000 --> 00:34:28,000 They do outstanding work. And that's, as a scientist, what we enjoy the most is to talk to your colleagues and learn the new things. 395 00:34:28,000 --> 00:34:31,000 And then maybe some new collaborations could come out of this. 396 00:34:31,000 --> 00:34:40,000 And then we will be able to do some better job back at our own laboratories after the visit. 397 00:34:40,000 --> 00:34:44,000 But I think this is really a fantastic opportunity for me to be here. 398 00:34:44,000 --> 00:34:47,000 Outstanding. Great to see you. Thank you so much.