Transcript of Episode 4: Jason Stein

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Jason Stein. Jason, would you like to introduce yourself?

Jason: Sure. Hey Grant, my name is Jason Stein. I’m an assistant professor at UNC Chapel Hill, in the department of genetics and in the neuroscience center. And I’ve been here for just a little under five years.

Grant: Fantastic. So tenure review is coming up soon?

Jason: It is. 

Grant: Good times.

Jason: I submitted my materials in February. 

Grant: Fantastic. So, What’s your research program about? 

Jason: My research is to try to understand how genetic variation that’s present in human populations, influences brain developments and brain structure, and then leads to risk for neuropsychiatric disorders like autism and schizophrenia.

Jason: So we have several model systems that we use to study this. The major thing that we do is using human neural stem cells. So we have a population of human neural stem cells, each of which is genetically diverse. And then we try to see how genetic variation within our population is associated with differences in the development and the differentiation of these neural progenitor cells.

Grant: What have you found? 

Jason: Oh, so what have we found in that? So we recently put some pre-prints up in the bio archive (“Evaluating brain structure traits as endophenotypes using polygenicity and discoverability”, “Cell-type specific effects of genetic variation on chromatin accessibility during human neuronal differentiation”). 

Grant: And Grace can link those on the transcript. 

Jason: Oh, nice. Yeah. Link them up. Get that–what’s that rating system–the altimetric. Yeah. You got to get that altimetric up.

Jason: So what have we found? So we found that, there’s many sites in the genome where genetic variation influences chromatin accessibility and gene expression. So chromatin accessibility is a measure of the function in the non-coding genome, probably largely due to differences in transcription factor binding.

Jason: So the genome is more open and accessible in certain regions of the non-coding genome, and that allows transcription factors to bind. So genetic variation within those open regions can impact transcription factor binding, and then lead to differences in chromatin accessibility. We found, you know, many thousands of different sites where genetic variation affects chromatin accessibility. And interestingly, they’re very cell type specific. 

Jason: So we did, we did the study in two different cell types: progenitors and their differentiated neuronal progeny. And if you do a chromatin accessibility QTL–so you find genetic variation affecting chromatin accessibility–very few are shared between neurons and progenitors.

Jason: But if you look at expression, like eQTL, there are much more that are shared between, genetic variation, affecting gene expression between two cell types. So we thought that was really interesting. You may have more cell type specificity for chromatin accessibility than you do for gene expression.

Grant: That is weird. Why do you think that is? 

Jason: So, you don’t totally know. So these are just hypotheses. 

Grant: But speculation is good. That’s why you go on a podcast. You can speculate. 

Jason: You can say whatever you want and no one cares, right? 

Grant: No reviewers will reject this. 

Jason: So this is inspired a lot by the work of Daniel Gaffney. He had a paper in Nature, Genetics, and the basic hypothesis for this paper was–and I think was well demonstrated in this paper– that if you have genetic variation, it can impact chromatin accessibility cause it’ll impact like different transcription factors binding to a certain region. But only in the presence of other sort of helpful transcription factors are you actually getting an effect on transcription. 

Jason: So say in one cell type that that other transcription factor is not expressed, well then you can still have an effect on chromatin accessibility, but you wouldn’t have an effect on gene expression. So this stimulus dependence of eQTLs may not be there for caQTLs or maybe less so for caQTLs.

Jason: And that’s kind of a hypothesis that we’re going with. We haven’t really demonstrated it though. Cause you kind of have to do a lot of ChIPseq to demonstrate that, and that’s expensive and very hard as we’re trying to find out. Yeah. 

Grant: So what do you envision as the ultimate practical application of this broad line of work?

Jason: Yeah. Yeah. So I was on a paper with a colleague just down the hallway, Mark Zylka, and this paper was focusing on Angelman’s syndrome and Angelman’s syndrome is like one variant or a variant of large effect mutation, which creates a big change in development and behavioral changes.

Jason: And through Mark’s work for like 10 years, and other people’s work too, they found a molecular mechanism whereby you actually get decreased expression of UBE3A in the paternal chromosome. I’m not the best person to explain this, but I’m on a podcast, so I’m just going to go for it–the basis of Angelman’s syndrome is that usually it’s an imprinted locus in UBE3A in neurons. You usually get expression in just the maternal allele in neurons, but if you have a mutation in the maternal allele in neurons, then you have no expression of UBE3A in neurons.

Jason: Okay. So that’s bad. And that leads to Angelman syndrome. So why is the paternal allele not expressed? Well, his work over the last 10 years has found this molecular mechanism. So they found this long non-coding RNA that seems to silence the paternal allele. So his hypothesis: let’s turn on the paternal allele. So they did this CRISPR screen and found a region of the genome that you can target that decreases that lncRNA, that long non-coding RNA, and increases the expression of the paternal allele. So now you have some expression of UBE3A. 

Jason: Okay, cool. So understanding the molecular mechanism took 10 years, like a long time. So you went from mutation to understanding molecular mechanisms and now with this CRISPR design, they have an actual treatment. And it works in mice and human cells. It hasn’t worked in humans, right? So it’s not like ready to go, but you know, it’s getting there. 

Jason: And so my kind of feeling on this is the same way. If we understand how genetic variation creates risk for psychiatric diseases, then we can begin to say, okay, so we know the genetic variation–and there’s a lot of consortia that are doing this, like the Psychiatric Genomics Consortium–they’re creating risk for psychiatric diseases. Then, if we can understand what the mechanism is–that’s where we see these QTL type of papers come in– then we can sort of start to develop treatments for the diseases. 

Jason: Now it’s different from Mark’s work to the work that we’re doing, because that’s one variation of a very large effect. And the stuff that we’re working on is polygenic effects. Each of which are very small. But still, I have hope that if we can target some of these pathways, maybe multiple of these pathways, that it can lead to some alleviation of symptoms. So that’s kind of like what I envision obviously hasn’t been done yet.

Jason: So just because I envisioned it doesn’t mean that it’s going to happen, but that sort of pathway from finding a genetic variation, understanding the mechanism, developing a treatment is like something I’m hopeful for. 

Grant: Nice. What do you think is the most exciting area of work in biomedical research today? What do you think’s the most promising? 

Jason: Oh that’s a good question. I think this tech development stuff in the biomedical world is really exciting and tech development, not in a computer way, but in a biological way, like creating biological systems to solve scientific problems.

Jason: One of them being gene therapy approaches. If you can actually make a virus that contains gene editing proteins that target your gene of interest and can have some functional effects. That’s awesome. That’s really amazing. I think some other work too, like Jay Shendure. It doesn’t have an immediate practical outcome, but work that they’re doing is trying to make mutations or make recordings of cells as they perform some biological process.

Jason: So for example, they’re taking a cell as an embryo and then making a mutation sort of every time it divides. And then every time that divides, then you can create a linear trajectory of how one cell forms many other progenitor cells, which make many other progenitor cells, which then lead to the formation of an entire organism.

Jason: If you can take that same sort of idea and then move it to measuring each time a cell does something. So for example, fires an action potential, like George Church proposed. He calls it a ticker tape recording of action potentials, or you can record gene expression through development.

Jason: You’re developing technology to perform longitudinal recordings of biological processes. I think this is going to be amazing at the single cell resolution. That’ll be really cool because once you have a ticker tape, you can sort of fast forward that ticker tape and move cells quicker through a biological process or develop cells quicker, which I think will be really important. 

Grant: Cool. So Grace here is a UNC senior, just getting kind of started with her career if you were in her position, deciding what area of work and laboratory and so on to start in 2021 or 2022. What would you do? 

Jason: Yeah, well, I guess can I ask Grace first what are you interested in? What do you like doing? Because that’s kind of the most important thing. 

Grace: Yeah. I work in the lab of Dr. Ian Carroll and I work with the microbiome, which I’ve been obsessed with since probably junior year of high school. Yeah. I definitely do that in the future.

Jason: Okay. We, I mean, that’s the most important thing. Do the work that you’re passionate about because if you’re going to go into graduate school or you’re going to start being maybe a technician after undergrad–that’s what I did–then go to graduate school and then go to postdoc and walk your way along the academic path. It gets super frustrating at certain moments because science is difficult and the experiments don’t necessarily turn out as you expect them to. And so what gets you through that is like actually caring about what you’re doing.

Jason: The most important thing is that you really care and that in those sort of low lows that you’re going to have, no matter what field you go into, that you’re going to be like, “you know, I’m doing this for a reason. This sucks, but like, I’m just going to keep working at it.”

Jason: I think that’s kind of  the most important thing. Obviously, I think towards Grant’s question, he wants like, what do I think are important skills and things like that. I think having bioinformatics skills, especially for microbiome work is going to like make your life so much better and easier because instead of just doing an experiment and handing it off to somebody else, which I think is what Grant’s organization allows people to do, it allows you to do both of those things.

Jason: And having the ability to do both of those simultaneously really gives you a lot of skill sets. That will be super valuable because you’ll know because you did the experiment that like “I isolated this sample, and something was weird with this one. I don’t know if the concentration was low, but the quality control metrics didn’t look right.”

Jason: And then you see it in the data and you’re like, “okay this isn’t right. This is an outlier. I know why this is an outlier.” So you can throw that out. But if you’re only doing one of those things–like you’re only doing experiments or the bioinformatics–it becomes more difficult because then you’ve got to go back to the guy who did the experiment and go be like, “do you remember anything weird about the sample?” And they may or may not remember.

Grant: That’s a good segue to our next topic. Can you maybe tell us a bit about your path? Add some color. You know, start in Dayton, Ohio. What motivated you? What surprises did you find at different stages of your career? What do you maybe wish you had done differently? 

Jason: So, yeah, I was born and raised in Dayton, Ohio, the birthplace of aviation. Where the Grant Belgard–if you didn’t know for all those podcast listeners the T stands for The Grant Belgard–was also stationed for a little while. I went to high school at a public school. There were some pretty good science teachers that I had when I was in high school. Mr. Protrusio and Mr. Martin. If they’re listening, shout out. 

Grant: You could send them the transcript after.

Jason: If I can even remember how to spell Mr. Protrusio’s name, which I a hundred percent do not remember. They were good. They were inspirational and helped guide me along the path. 

Jason: When I went into college, I went to Northwestern. I studied at this program called the Integrated Science Program. And this was kind of an amazing place. Like we were somewhat segregated from the rest of the Northwestern population, which was kind of weird, but actually kind of good because we had our own little community. So we had the integrated science program, the ISP, house which was this little, somewhat crappy house.

Grant: Sounds like a real party house.

Jason: There were parties there. Yeah. But you know, people would study there, they would hang out there and we’d have parties there. We would have our classes there. So it was like your little community. It was only about 20 people or something like that. Coming from Dayton, we had very smart people that were at the high school, but like, there were some, really brilliant people in this ISP program.

Jason: And it was cool to just be around these people, interact with these people and have the attention of the teachers too, who were teaching these very small classes to us. So I liked it a lot and enjoyed the classes. 

Jason: One of the most useful things was they had this–I forget the name of the class–but they taught us basic computer programming skills like Unix, some Pearl. I don’t think R was even a big thing back then. So we didn’t learn R, but like a little bit of Python and things. And that was very useful because that has helped me in the future so much. I had a little bit of computational skills. 

Jason: So when I was there, I did research. But I did research in physics. So I worked at the Fermilab, which my friends called Fermi camp, which was a very weird, but kind of amazing place. So if you ever been, it’s outside of Chicago in Batavia, Illinois, and they have these roaming Buffalo that are just around on this large cut area and underneath there’s a particle accelerator, which was then the largest particle accelerator in the world before the large hadron collider. And they shot particles at each other and then they got a whole bunch of data and they needed some way to visualize that data. 

Jason: And to be honest, I had no idea what I was doing, but they were like, “can you program a website to make all these graphs?” And I was like, “cool, sure.” So I worked on that and I didn’t probably do a great job, but they seemed to want to keep me around. So that was good. That was really good. 

Jason: So then after that, I didn’t know what I wanted to do. I did all kinds of stuff. I took the LSAT, the GRE, the MCAT, because I didn’t know.

Grant: You were really undecided.

Jason: I was very undecided. And then, I ended up applying to a position at NIH and working in the intramural program in Bethesda for two years under Andreas Meyer-Lindenberg. And we would scan people with schizophrenia, like do MRI scans of people with schizophrenia and their siblings, and then analyze the data as well.

Grant: Was it on the main campus?

Jason: Yeah. It was on the main campus, building 10. Yeah and it was a great experience. There was again, a really good environment there. Like there were a whole bunch of people, right out of college that were interested in science, good people, friends and stuff. They were all working there at the same time. We were called IRTAs, which is intramural research training awards. Yeah. Weren’t you in IRTA, Grant? 

Grant: I was in the GPP, so in grad school I was flying back and forth between Oxford and NIH, but I was at an offsite location around Twin Brook, where the intramural sequencing facility was.

Jason: Oh, cool. 

Grant: So it was kind of a cutting edge of the genome technology branch. 

Jason: Oh, nice. Very cool. Twin Brooks, so that’s not too far, but it’s like just not on the main campus. 

Grant: Yeah, it’s on the red line. So it was close enough. 

Jason: Nice. 

Grant: You didn’t have to fight as bad traffic. 

Jason: What year did we overlap? What years were you there? 

Grant: 2008 through 2012, but not really full time, except 2010 through 2012, but I would pop back and forth for like a week at a time. 

Jason: Okay, cool. We probably did overlap just like a tiny bit then. Okay. Yeah. So I scanned people with psychiatric disorders. That was really interesting. My computational skills were super valuable to those people. They found them valuable. 

Jason: You know, one thing I really hated was calling subjects. Like a big part of our job was recruitment, which means that you get on the phone and you say, “Hi, my name’s Jason. I’m calling from the NIH.”

Grant: Did you start robocalls spamming them? 

Jason: I didn’t, I didn’t make that. I should have made that. It would have made everyone’s life easier. So we had those assignments, recruitment assignments, scanning assignments, and then we would also do analysis, and I wrote scripts to make everyone else’s analysis easier. And then I was like, “if I do this, will you call my subjects?” And they’re like, “sure.” So that was great because I really hated doing that.

Grant: Motivation.

Jason: Yeah. So I worked for Andreas Meyer-Lindenberg, who’s now in Germany, but then was at the NIMH and he told me that I should work with Paul Thompson at UCLA for graduate school. And so basically, I applied to several places for graduate school, but I emailed Paul beforehand and I was like, “Hey, Andreas told me I should work with you. Can I work with you?” And then he called me and basically said, “sure, you can work with me.”

Grant: So, weather it had nothing to do with it. 

Jason: No, actually I was pretty anti-LA the beginning. You know, LA has a bad rap. 

Grant: You’re like the anti Neil.

Jason: Neil is totally the opposite. I think growing up in West Virginia, Neil must have really hated it.

Jason: So yeah, I was not into LA. And I was like, “Oh, this place is lame, but you know, whatever. The neuroscience is supposed to be good.” But I eventually grew to really appreciate it. There’s a lot of good things there, especially hiking. Hiking is pretty amazing there. And so close to the city.

Grant: And did the weather grow on you? I mean, would you have considered taking a position in Chicago afterwards? 

Jason: Oh, a hundred percent. Yeah. I like Chicago, actually. I don’t mind the Chicago weather and I very much appreciate changes in seasons. Like here in North Carolina, it’s beautiful. Like I love the springtime, which you don’t get. Grace is agreeing with me. Springtime here is amazing. Like you just get blooming of everything after the winter. It’s just such a contrast. So beautiful. So nice. 

Grant: Yeah. Florida is just one big, hot, hurricane season. 

Jason: It’s very muggy down there. Yeah. So I worked with Paul Thompson. 

Grant: Tell us a little bit about Paul Thompson. 

Jason: Paul Thomas is an interesting human. He is a very brilliant man, very, very smart in mainly math and figuring out topologies of brain structures. He really led. He did really an amazing job at that and he is highly motivated to publish papers and you can see from his publication record. I think he’s in the thousands of papers (1250 Publications). So that was interesting. 

Jason: His lab environment was also kind of weird in the sense that we didn’t have lab meetings. There was no room. He was getting grants like crazy and recruiting many students and post-docs, and we were all shoved into his office.

Jason: There were like eight people maybe in his office. And I remember I was sitting on one side of a desk. And then on the other side of a desk, there was somebody else, like just facing you. Now that you think with COVID protocols, you’re just like breathing into the face of someone else. It was just crazy.

Jason: It was a good experience because Paul had access to so much data, and he really needed people that were willing to analyze the data. So he was really looking for people to analyze that data. And this was also at the time that GWAS was starting. I started grad school, I think in 2007, and 2007 was one of the first welcomes to GWAS.

Jason: There were a lot of candidate gene studies, which are, for those who don’t know, this sort of old school thing you don’t do now, versions of association studies. 

Grant: All BS. 

Jason: All BS. None of it has ever replicated. I would say that. The need for doing GWAS and large consortia was clearly there, but those didn’t really exist yet for brain structure traits like for things you can measure with MRI. That’s basically what I got involved in. So, Paul met with this guy, Nick Martin from Australia, who was one of our collaborators, over dinner. They basically said like, “Hey, we should form a consortium.” And then I was basically the one to lead that consortium, which eventually we called the Enigma consortium: Enhancing neuroimaging genetics through meta analysis. 

Grant: Who came up with the acronym? 

Jason: Paul came up with the acronym, which is a great acronym, but also the wrong side. You know, like if you think about it, like he keeps saying, it’s like the code breaking for the brain, which is great. I kind of like that, but it’s the wrong site. It’s the Nazi side that came up with the name. Like we should call it like Bletchley or something, something more positive, but unfortunately it’s Enigma. I mean, nobody remembers that. So I think it’s kind of great. 

Grant: Nobody knows history. 

Jason: Nobody knows history. It’s a great name. So yeah, I ended up leading this consortium and along with people from Australia, Sarah Medland, people from Holland, and they would all fly to LA and we would do all of this stuff together. It was a good experience because we were one of two consortia that were doing this at the time. We found a bunch of genetic associations to bring structure.

Grant: And then?

Jason: And then, I finished with Paul, although I didn’t really finish. I was still working with Paul on all these Enigma projects, but I was looking for a postdoc. I was trying to find a postdoc that I could not just find genetic variants associated with different traits, but what to do next.

Jason: And so I read some papers online about what to do next. And it seemed like things were converging towards using STEM cells to model variance. And I was like, “okay, cool. I want to use STEM cells to model variance. I have zero experience in wet lab biology. I don’t know how to hold a pipette. I don’t know how to run a Western blot. How can I do this? I need somebody who values computational experience and will allow me in a postdoc to like transition a little bit and learn some new things.” And so I emailed Dr. Daniel Geshwind.

Grant: I’m trying to  get him on. He said he had to listen to a few first

Jason: Did he? That’s funny. So yeah, maybe he’ll listen to this. So he wrote a Nature paper with Jenna Konopka, basically about hypothesis discovery research. Instead of all the classic wet lab scientists who were poo-pooing like, “Oh, you’re just on a fishing expedition. All bioinformatics work is a fishing expedition,” which was pretty prominent back in 2011. That’s what they would say. 

Jason: He was like, “Okay, there is hypothesis-generating research and that’s what we do. And we generate new hypotheses. And then we can validate those hypotheses, test them with model systems.” And I was like, “Cool. That makes sense. You’re not, poo-pooing all of the giant amount of work that all of this discovery science is doing.”

Jason: So then I tried to apply to his lab and I emailed him once. No response. I emailed him a second time. Maybe I got a response. I don’t really remember.

Grant: Three letters

Jason: Yeah. It never worked. Then a third time I was like, “Dan do you want to meet me or not” And then he’s like, “Oh yeah that’s fine.” And then we talked and I think this was when he was in London, maybe at the Institute of psychiatry. And so it was very difficult to get an appointment with him. But eventually I got an appointment with him. He allowed me to be a postdoc and yeah. That is where I met the Grant Belgard. 

Grant: So tell us about Dan.

Jason: I don’t know. What do you want to know about Dan? He’s a very brilliant man. He has a million projects running simultaneously. Somehow he knows and can provide useful insight to each of those different projects. He also promised me that I could meet Bob Dylan if I got a paper in a fancy journal and that has not happened. His neighbor is Bob Dylan. So I’m still waiting for that. And I hope that happens soon. 

Grant: You’re on the spot, Dan.

Jason: Yeah, I would really like to meet Bob Dylan. I think that that would be–other than meeting Grant–really a defining feature of my life.

Grant: So, what did you do in Dan’s lab? 

Jason: So in Dan’s lab, I worked with this other postdoc named Luis. Who’s now an assistant professor at UCLA. Luis and I formed a team, which we call team middle earth because we had the middle bay and we’re both nerds. And we basically did everything together. So Luis is a very brilliant molecular and cell biologist. He was trained at Harvard. He’s the most careful scientist you will ever meet. And he knew nothing about bioinformatics and was very curious about bioinformatics.  And I knew nothing about wet lab biology and he knew everything. So he taught me everything I know about wet lab biology. And then I taught him a little bit about bioinformatics and how to code. And I think mainly like how to interpret what the possible confounds are for bioinformatics experiments and stuff. 

Jason: That combination was amazing. I can’t speak for him, but it really helped me out. Like really helped me develop into a much, much, much better scientist because now I have more skillsets. I’m not just analyzing other people’s data. We can generate our own data in the lab. That was really great. 

Jason: So Luis and I worked on developing human brain tissue. We acquired that. And then we studied multiple aspects of it: how well STEM cells model the actual development of the in vivo brain. And then we also studied how chromatin accessibility changes during neural development and the developing human brain.

Grant: We use those data sets. I feel like everyone does.

Jason: You do use those data sets? Oh, nice. I’m glad. I’m glad to hear that. That’s cool. 

Jason: Luis is great. You should have Luis on. I like Luis’s insights into anything. Anytime I try to do something new, I always ask Luis. I’m like, do you think this is a good idea? If he thinks it’s a good idea, then I do it. If he thinks it’s a bad idea. Then it’s usually a bad idea.

Grant: Cool. So, what’s the wet versus dry lab balance in your lab now? 

Jason: So I’d say it’s pretty 50:50, and leading a little more wet now. So it was leaning a little more dry before. The initial experiments in our lab were growing these hundred different stem cell lines that Luis and I generated in Dan’s lab. And then, I shipped here to UNC and then we ran these QTL studies here and now we have a lot of data.

Jason: So we have a lot of hypotheses because you can get co-localizations between your eQTL and caQTL with GWAS data. Okay. So now we have both the system for discovery and an experimentally modifiable system to see what the effects are and why those effects exist. So now we’re in that stage where we analyzed a whole lot of data, and then we have all these different experimental hypotheses. So now we need to do all the validation for those experimental hypotheses. 

Jason: Yeah. So I’ve been really fortunate to get students who are interested in both sides that were willing to come with me, even though I’m not the best wet lab biologist in the world, but I still have decent resources.

Grant: They don’t know any better. 

Jason: They don’t know any better. That is the definite truth. 

Grant: Yeah. I think back to when I was a student, I mean, I had no clue what I was doing. It was just pure luck. 

Jason: So yeah, I’m definitely taking advantage of that, you know? The main thing is being nice to people. I think if you’re nice to people and you try to be a good mentor, then word gets around that like, “Hey, this guy’s not a jerk. He cares about science.” And so other people hear that from the other grad students and they were like, “Oh, maybe I want to work with this guy who is not a jerk.”

Jason: I think we’re like 60, 40 now. I mean, it’s still pretty heavy for dry lab stuff, but. A lot of, even the bioinformatics students are doing wet lab experiments now to try to validate their hypotheses. 

Grant: Nice. So tell us about getting started. What were your biggest surprises? Obviously you would have been as well prepared as anyone going into it, but what weren’t you expecting?

Jason: Yeah. I mean, I think how long it takes to do anything is like a big surprise and kind of a disappointment. Also how lonely it is at the beginning. First of all, imposter syndrome is overwhelming. When you come in and you have an empty lab that’s a little bit dirty and you have your office space and you have nothing. No people, no experiments going, no data. It’s just you. And it’s like, awful. I think when you just start out that first week.  The process of building the lab, getting equipment, getting people takes a very, very long time, especially because you want to hire the right people. You want to make that lab environment good, so people actually enjoy working there.

Jason: You want to have very competent people who have skill sets that are complementary and not identical to your skill sets. And the major recruitment that you get is graduate students who have a defined schedule of being able to join your lab. So you can only get graduate students on the rotation schedule and then they join your lab.

Jason: So all of that leads to huge delays in the ability to make a functional lab with enough people and enough equipment. And enough, whatever the experiments are to do anything. So you can have all these ideas, but you can’t do anything at the beginning. So that was like a six month process.

Jason: And, you know, the people here were really nice and they also explained this to me. They’re like, “Oh, it took me six months to run my first gel” or something like that. And I was like, “Oh, thank goodness.” That makes you feel much better when you hear that. 

Jason: I mean, that took a while, especially the first project. We had this QTL project, which was my R00 project. It took a long time to get running, but it finished and was running and I had a great technician to help me out with that. Now we have all this data that allowed me to get more grants.

Jason: And now with the students and stuff, I feel like I don’t have to be here. I mean, I still have to be here, but they’re rolling. And they’re smarter than me and they can just do it. You know what I mean? I’m just providing advice. It’s not quite at the point where Dan’s lab is: like a super mature lab where he has like many postdocs who have really, really strong experience. Like I have a very grad-student-heavy lab. People still need to be trained and stuff like that, but it is much more to that point than it was five years ago, which feels good. That feels good. 

Grant: So going back to that, that startup phase, would you ever consider starting a company? 

Jason: You know, I’d consider it, but I don’t know what I have to offer yet, The Grant Belgard. I feel like my end goal would be wanting to make some sort of therapeutic. That would be an ideal for me. And I don’t have that yet. Like my research hasn’t led to that yet. So if I ever do feel like I have something to offer that I can sell that other people would want to buy that I think can help the world. Then yes, I want to do that. But right now I don’t have that. I don’t have that, that thing. 

Grant: Yeah. It’s interesting. These days the virtual biotech model is in Vogue. So. It’s quite common for a very small company. One, two, three, four people to develop an asset and essentially get to the stage where it’s ready for later stage clinical trials and have pretty much all the work for it outsourced. And so really the core team is finding organizations for that, interpreting the data, raising money and so on, as the asset progresses. 

Jason: That’s interesting. 

Grant: And there even organizations today that do that with pretty complex biologics. So, you know, gene therapies and things like this. So it could happen one day, maybe Jason Stein. 

Jason: Yeah. I mean, yeah maybe, I don’t know. That sort of idea is quite different from the way academia works, where you farm out the experiments to other people. So in that case, you’re mainly doing either bioinformatics or just even interpretation of the experimental outcomes? Like how does that work?

Grant: Interpretation, deciding what to do next, there’s a lot of coordination that has to happen and quickly and it’s very interdisciplinary, right? So you’re working, not even primarily with bioinformaticians. You’re working with chemists with biologists, with biostatisticians, with clinicians. You have to draw on a lot of different skill sets. 

Jason: Yeah. That’s cool. For me, first I don’t know anything about business. Like you know a lot about business. I don’t really. I don’t know how to do that, but I feel like the essential thing of a business is that you need to have something that you’re going to sell to other people that they want to buy.

Jason: And so like right now, I feel like I have research. I have research ideas. I have the ability to do research. I have people and the resources to do right now. I hope that that research leads me to something that someone else wants to buy and that would be helpful to the world. But immediately, I don’t see it. I don’t have that right now. 

Grant: You could always spin something out. 

Jason: Yeah, maybe. For example, I talked about the Angelman’s syndrome gene therapy treatment. That’s a thing where it took 10 years. He has something that’s a gene therapy in mouse and human cell lines. I can definitely see that moving towards a company or even farming it out to other people to do experiments because there’s a very clear path. I participated in that, but I’m not like leading. So, for my own research, I don’t really have that yet.  I hope one day to get it. And if I do, it’d be great to either form a company or work with somebody who does. 

Grant: Sweet. So 30 or 40 years from now, what would you like to have done? 

Jason: Like in my career? 

Grant: Yeah or actually more broadly.

Jason: I think about this a decent amount. So I think first it just to be remembered as a decent human to other humans. I feel like, especially now with political situations, that simple kindergarten thing is totally forgotten and is totally the most critical thing for a functioning society. And just the most basic thing is to treat other people with respect. 

Jason: I think in terms of career aspects, if I can, I would like to be a part of developing treatments for psychiatric disorders, which right now don’t have treatments like schizophrenia. I mean, they have treatments, but they’re not good. And a lot of people don’t do well. And there’s nothing really for these people. Like they’re homeless. They’re in state hospitals. It’s a huge burden on society and people are just suffering. But there’s so much good neuroscience that’s happening right now. So much good genetics that I feel optimistic about what could happen for these people. And so if I can help contribute to that, I feel like that would, that would be amazing, like an amazing cherry on top of being a nice person and my career, 

Grant: Very nice. How do you like UNC?

Jason: UNC is a good place. A lot of, a lot of nice people, not a lot of like ego. I mean, there’s still some ego. Science has that for sure. But like people work together really well. Everybody’s been really supportive from a young assistant professor kind of thing, so that’s been great. There’s some administrative things and things that definitely could be corrected or made better, but I don’t really want to be the administrator to do that. And usually the way they have it here and I’m sure in many places is the squeaky wheel gets voluntold to fix the problem. So you’re limited in your squeaks based on how much time you want to spend fixing the problem, which kind of makes sense. 

Grant: Well, you know you’re getting someone who’s passionate about it. 

Jason: Right. Yeah, exactly. Exactly. We only have time to do so much. And if you want to spend your time doing research, then you spend your time during that research and find alternative solutions to problems.

Grant: The ego thing in science is pretty interesting. It’s been pervasive, from even quite early days, back to Newton and Hooke. Ego has always been a bit of a problem. For a lot of people they are quite ego motivated more than anything else, but not everyone. I mean, some people are just nerds and like doing it right. I don’t know. What was your sense of that? I would say probably more people are just nerds in like doing it, but ego thing is pretty common. I don’t know. 

Jason: Yeah. Yeah. It’s hard. I can’t say I’m immune to it. 

Grant: I feel like it’s enriched in academia. No offense. 

Jason: I think you’re absolutely right. There was this paper written–it wasn’t a paper more of a website–where somebody proposed a new model for academia. Basically how it is now, I’m supposed to form my own very small business where I get people to work in my lab. I get grants from the federal government or foundations to support my work. And like I had my own business and Hyejung or whoever’s next door has her own business. Mark Zilkha has his own business. 

Jason: There is not this overarching effort, as is seen in physics, for example, where we all work on the big problem and we all do our individual small part for that. And some organizations, like the Allen Institute, I think have done pretty well. They work on giant problems that cost crazy amounts of money and they have each little person contributing to that.

Grant: And then on the other hand, you have the human brain project. Right? 

Jason: Right. There’s definitely examples where it didn’t work, but the ideal situation for me would be like, I have Luis and we work really well together. If Luis and I could form our own lab where we’re just two people running a lab, that’d be cool. But universities don’t often do that. They don’t recruit two people together. It’s like, why? Why not? We worked so well together.

Grant: Bring middle earth to UNC.

Jason: Yeah. We have to get Selene to want to move to North Carolina. Yeah. I think those things would be really cool, like to try to make something like that or to have very large projects. I know the human brain project didn’t work, but if you have very large projects where you actually have a very clear definable goal and steps that you want to get there. Where everybody says, “I’m not going to try to be the one who discovered it. I’m just going to do my part of the bigger entity.”

Grant: That’s basically, what academics refer to as “industry.”

Jason: Is it? I’m not sure I believe that.

Grant: Yeah, you know, we are in the same boat rowing towards the same goal. At its best. 

Jason: Yeah at its best okay. Because I feel like industry is dominated by the pursuit to make money and increase stockholder wealth right?

Grant: If you don’t make anything that’s useful, then you’re not going to make any money. 

Jason: Right. But that’s the essential problem. With research we don’t necessarily know if we’re going to make anything. You have to figure out what’s wrong first. And then you can make something. With psychiatric illnesses you have to still figure out what’s wrong first. And then you make something. There’s been a lot of drug development where you don’t know what’s wrong. Just throw a lot of stuff at it. And then you, you see minor therapeutic advantages.

Grant: Well, a lot of big drugs have just been discovered through pure serendipity with no known mechanism of action. Right? I mean, a lot of the big discoveries of the fifties and sixties, that in many cases took a very long time to improve on

Jason: Yeah. But clearly improvements are needed and there hasn’t been much in a long, long time. Especially in psychiatry. Yeah. 

Grant: Yeah. A hundred percent. Well, we are at about time and we just need to upload. 

Jason: Okay, cool. 

Grant: Thank you so much for joining us today, Jason. That was very enjoyable. 

Jason: Absolutely. Thanks for having me on the second edition? Is that right?  

Grant: I think this is episode four episode four.

Jason: Well anyways, thanks for having me. I really appreciate it, Grant. Hopefully I didn’t talk your ear off. 

Grant: No, it was great. Thanks, Jason.

Transcript of Episode 3: Ben Logsdon

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and I’m here with Ben Logsdon. Ben, would you like to introduce yourself?

Ben: Hi Grant. Thanks for having me on the show today. I’m a computational biologist. I’ve been in the field of computational biology, professionally, I guess, for about six plus years now.  And before that did the whole, you know, postdoc, couple of postdocs and graduate school in upstate New York.

Grant: Great. Thanks.  So tell us more about your path. What made Ben Ben? Start from the beginning.

Ben: Yeah, definitely. Absolutely. So I was in college in undergrad and I was really into genetics and biochemistry and I ended up getting an undergrad BS in biochemistry, but also minored in mathematics.

Ben: So I’ve always kind of had multiple multidisciplinary interests. I’ve definitely pursued both of those going, throughout my sort of trajectory, both professionally and personally. I then went on to Cornell and got a PhD in computational biology, really focused on building new machine learning and high dimensional statistical methodologies to analyze genome wide association studies and high dimensional gene expression data sets. And really the driving purpose behind all of it was just wanting to understand these complicated systems, you know that in physics, there’s these simple rules. And, you know, humans have spent hundreds of years building better instruments to figure out what those rules are to try to understand them.

Ben: But biology is just this, like it’s the frontier man. Like we still don’t know the rules. I mean, we have ideas about pieces and parts of it, but I’ve always been fascinated by that. And it’s like one of those places where a lot of biology that’s being done right now, or has been done has not really focused on this sort of more quantitative side of things up until I would say relatively recently. You know, and there’s a lot of really good work you can do just at the bench doing Westerns and gels and all that good stuff. Like that’s been really powerful to help us understand and disentangle some of these systems, but I’ve always sort of been of the opinion that to understand these things you need a bigger tool set. 

Ben: So that was kind of the motivation to do more quantitative stuff at Cornell and get better chops on the stats and machine learning side. And then after that, you know, I went to the Fred Hutchinson Cancer Research Center, did a postdoc there with Charles Cooperberg looking more at genetic epidemiology, sort of like a method development for analysis of a whole exome sequencing and rare variant analysis type work. And then decided I wanted to do something with potentially more translational impact and did a second postdoc at University of Washington, focused on applying these sparse model building methods to gene expression, data sets in cancer to try to come up with alternative ways of identifying driver genes that, you know, wasn’t just based on mutations, but trying to use expression signatures or detecting within expression data, signatures of drivers.

Ben: But then I guess the real thing that I’m passionate about now, and I’m really grateful for. You know, I just left this job at Sage Bionetworks and I spent six and a half years there working in the neurodegenerative research space. And that’s just been an amazing experience. And, you know, as a computational biologist, oftentimes you kind of are like a hired gun, right? Like some principal investigator or in CRO-land, especially, some client brings you in and is like, “Hey, I’ve got some data to help me make sense of it.”

Ben: But I do think I’m not just interested in doing data analysis. Like I think in the context of Alzheimer’s disease in particular, like, I really want to understand the biology as well. And really want to help sort of marry all of these different quantitative techniques with the right data sets to inspire the right question that then the folks doing the bench work can go track down, develop new assays, do the right experiments so we can actually like start figuring out these diseases.

Ben: It’s also been fascinating in the Alzheimer’s disease space, how the field has been very married to a very small set of hypotheses about like, what is driving this disease. And, you know, just looking at some of the data analysis of the new, the omic data coming out of Alzheimer’s, it’s not as simple. Like amyloid and Tau, you know, the signatures are there and there’s really interesting results or insights to be gleaned from that, but there’s so much other biology that’s going on and it’s very complicated.

Grant: You’re looking under the streetlights, right?

Ben: Yeah. I mean, the streetlamp effect is real. And, you know, you can talk a lot about misalignment of incentives in academia and industry, and why that leads to lack of diverse portfolios in terms of risk as well as the technology needed to generate data, to be able to even articulate some of the new hypotheses. Right? Like you think about for a long period of time, it’s just people looking at tissue slides under a microscope and saying “okay, well, we see these amyloid plaques and these neurofibrillary tangles, what’s going on there?” And then omics just opens up a whole new frontier of possibilities in terms of the biology and the molecular causes of the disease. And you can’t see it right under a microscope necessarily, unless you know what gene you want to look at.

Ben:  And really, I think a lot of it is like knowing what the players on the chess board really are and what the rules of engagement for those players are and how it relates to what we already know. 

Ben: The thing with Alzheimer’s disease that makes it very different from cancer, for example, is that Alzheimer’s, you can’t profile the tissue during the course of the disease, right? Like you can’t get antemortem tissue samples. And so all you see is what’s happened at the post-mortem. And so it really is like a Sherlock Holmes mystery, in some sense: you know what happened after the fact, but then you’re trying to like put the pieces together as to what the sequence of events that led to that is?

Ben: I think that makes it a very different type of problem than in cancer. And some sense it’s a lot harder because you’re having to do a lot more inference and we don’t have good model systems. There’s plenty of mouse models where you can just crank the amyloid to 11 and yeah, like things change, but that doesn’t mean if you cure Alzheimer’s in a 5XFAD mouse, that whatever that drug is, is going to work in phase three human trials. 

Ben: So, yeah. So I think, I guess to wrap up the answer to your question about my arc, I think it’s really been one of been really sort of generally curious and expansive in my interests, like wanting to understand biology and the quantitative mathematics/statistical side, but really sort of gaining a passion for their application to neurodegenerative disease in the last six years. 

Ben: And at Sage I’ve been working in these amazing National-Institute-of-Aging-funded consortia: the Accelerating Medicines Partnership in Alzheimer’s Disease (AMP-AD), the Model AD consortium, and most recently I was in the TREAT-AD consortium. And these are like multimillion dollar, multi-institutional, open-science consortia that are trying to pull back the curtain on other causes of the disease through new data generation and analysis of that data. So like AMP-AD was focused on generating data to do systems biology analysis like: WGCNA, or causal network analysis, those sort of things on gene expression from post-mortem brain to prioritize new targets and disease; model AD, building 50 new mouse models of late onset Alzheimer’s disease; and treat AD sort of like the open drug discoveries idea, where we actually would have medicinal chemist, structural biologists, people who had experience in developing high throughput, screens, and assays, and then marry that to everything upstream. 

Ben: Right. So it’s just been an amazing experience working with so many different types of people. I think that’s not something you would generally get to experience as much in academia, Like as a bioinformatics expert, you generally have the PI who has some biological question, and you’re asked to analyze some data. And in this case, there were a lot of different perspectives and language, how people talk about things.

Ben: And so it’s been great, it was a really amazing experience and definitely opened my eyes to like, you know, how complicated all these processes are. Like from a philosophy of science side of things, like all of this is open science. So like everything was being put out in the open through the AD knowledge portal that’s hosted at Sage. And I think that’s also something that the young guard is recognizing: how important it is as we go forward. That the actual value of any individual data set is usually–unless you’re talking like clinical trial data, obviously, but like preclinical/basic research– like the value of any of those datasets is actually pretty minimal on their own. And it’s only when you can start combining them and layering things up that you can really realize their potential. 

Ben: But a lot of people, in terms of incentives, are like “I’m going to like generate this data and then, you know, sit in my lab and have some postdoc crank on it for two years until they can hopefully find some gold and get a Nature, Science, or Cell paper right.

Grant: So following on the misalignments of incentives, what do you think are the strongest misalignments and what do you think might be some reasonable reforms that should be considered to mitigate them? 

Ben: I mean, I think a lot of it has to do with academic promotion, right. That basically people who are looking to get tenure, they’re being judged on two highly-related criteria. Actually really one criteria, which is how much indirect they bring into their institution, which is a function of how many successful R-level grants they are applying for and getting. And that’s all predicated then on how many publications they’re putting out because publications are kind of the raw material to demonstrate leadership in a particular field or domain.

Ben: I do think that, you know, in terms of the misalignment of incentives, I mean, the problem with that is that it sort of leads to a model where people are all trying to be an expert in one narrow thing and some of these problems, the scale of the problem, it’s not something that you can do if you just have one hat.

Ben: And so then it makes it much more difficult for the traditional R kind of awards, where you have the academic who has a lab that’s like cranking away, cranking out postdocs and graduate students who are all working on that one tiny little bubble on the edge of human knowledge that they’re trying to expand.  I’m less familiar with physics, like in actual experience with how it works in the world of particle physics. But in that case, there are papers with 10,000 authors on them and the instruments are just so big and expensive that in some sense, they have to work together with lots of people with different expertise in a lot more coordinated fashion, just because the scale of the problem is so big and complicated. 

Ben: But in biomedical research, it’s still a little bit of the wild West for academic research labs. It’s kind of like having your own little company, where you’re trying to put in competitive bids to the federal government on research proposals and you’re trying to demonstrate that you can be out in front and push the boundary of human knowledge in a very specific way. But I think those incentives lead more towards putting out lots of papers and being able to secure a lot of indirect dollars to your parent institution and that doesn’t necessarily mean you’re going to be taking risks, right. You’re going to want to continue to keep your lab funded. 

Ben: I think one of the challenges is for some of these areas of biology, where we don’t really understand what’s going on and we have a lot of the streetlamp effect, as a community, we need to take more risks and we need to spread that risk around to a much broader pool of people working on these problems. We need a leaderboard of hypotheses and have people work cranking away on all of them. And then as a society, we’re investing proportionally across them. 

Ben: You can’t ask an early stage academic investigator to be like, “Oh, you should go after this target that nobody knows anything about. There’s 10 papers in PubMed on it.” They’re going to be like, “no, I’m going to go after the one where we have a lot of prior evidence and we can write a sweet R01, right? Yeah. 

Ben: So I think that’s one big misalignment of incentives where for people who want to get tenure, both in terms of the review process for grants, but also in terms of how they’re being assessed. There’s a general sort of necessary conservatism. Maybe that’s fine in academia. It can just be how it works, but then there does need to be some other outfits that can contribute to our collective knowledge and take some of those risks and push the boundaries a little bit more.

Ben: And a lot of that has to do with how academic organizations organize themselves. They’ve decided that they have this concept of tenure and that’s the big carrot they have for all these early stage investigators. 

Ben: It’s interesting. Cause I think once you get to someone who’s a later stage investigator who has already made their name and they have less to lose. They’re actually more likely to take some of these risks and go after like projects and ideas that are a little bit more on the frontier, a little bit more on the boundary, but 

Grant: Well, they certainly afford to do so. They typically have larger labs. They may have HMI funding or something like this, and the failures don’t really count against you. And the productivity per dollar I don’t think counts against you that much if you’re still publishing high-profile interesting papers. What I’ve seen from a lot of labs  is they’ll put postdocs and graduate students on fairly risky high risk, high reward projects, which are great when they work out.  And that kind of stuff is pretty important to move science forward, but it doesn’t necessarily always serve the postdocs well, who may have been put on an unsuccessful project given the rest of, of the system that’s currently in place. 

Grant: So if Francis Collins is–who knows why– listening to this podcast, driving into Bethesda what would be your message for him?

Ben: Oh, Oh man. Put it on the hot seat. Yeah. I mean, I think the way in which the labor market works in academia should be completely rethought. I think that postdocs are incredibly, on average, under compensated given their level of training and that you look at fields where there are good industry opportunities–I’d say more in this sort of machine learning area or EE or CS–you see this just brain drain from academia. And I think that’s a problem. I think for me personally, it’s super frustrating that on the biggest problems of our time, like curing Alzheimer’s disease or cancer or all these huge biomedical research problems, you have a huge brain drain of folks with quantitative skills. They’re all going out to Amazon or Facebook or Google or whatever because the financial compensation is just, it’s just not comparable, right? Why would you do a postdoc when you could get a six figure salary?

Ben: I think that’s one thing I’d say. And then in general, like postdocs, you can end up having folks be taken advantage of, because the actual academic job market is so absurdly expensive or absurdly competitive, and people just get stuck in a permanent postdoc, where they’re just in a lab. It’s comfortable, but there aren’t a lot of good opportunities to progress professionally. And so people will stay in postdocs for seven to eight years. 

Ben: So I think, you know, if I was talking to Francis, I’d say like, “Hey, there needs to be a complete rethinking of the training model to address the problems that we have. The old model doesn’t work. Like you don’t have this model where you can just have people come in as grad students, get their PhD, go do a postdoc with the one expert in the field and then have their own ideas and get that first R01 or do a K award or whatever, and then go off and start their own lab.

Ben: I just don’t think it’s going to work like that going forward. If we’re actually going to make progress on some of these problems, you need to be able to assemble teams of people with complementary expertise who can work together well as a team. And that’s just not something you’re trained for in the academic model necessarily. Like you have to figure out where you’re going to have the insight. You know, the lone genius in the tower, who’s going to figure it all out.

Ben:  Really thinking how to restructure the training model comes, at the end of the day, it comes down to the funders. Because the PIs are the ones that are applying for grants and those grants are being used to pay the postdoc or grad students salary. Yeah. Maybe that’s a little too radical of a take, but I do think it’s true.

Grant: Yeah. There definitely are some bad habits. We sometimes have to train people out of, when they come from academia. When you’re going to assemble teams with complementary expertise because I think there’s a lot more general teamwork in biotech. The incentives are set up in a very different way. Charlie Munger said, “you show me the incentives and I’ll tell you the outcome.”

Grant: So channeling Peter Thiel here, what’s something you believe is true, but where most people would disagree with you? 

Ben: I think we don’t talk or think enough about the long view in biomedical research. I’m not sure people would disagree with me on this necessarily. I think that they just haven’t really thought about it. Have you ever read the foundation novels by Isaac Asimov? 

Grant: Yeah. 

Ben: So just for people listening, in those novels you have this galactic empire, that’s hitting the end of its tenure, basically, and about to descend into some like 10,000 year dark ages or something. And this guy, Harry Selden’s like, “well, that sounds terrible. Let’s do something about it.” He creates this organization called the foundation. The long and short of it is that the foundation’s purpose is to marry changes in policy and technology and like all of the things that make a society work and come up with probabilistic models associated with those and make subtle changes. Putting off, pushing on all the levers so that humanity doesn’t go through another 10,000 year dark age. 

Ben: Basically, from my perspective, we think a lot about the short game–like going back to incentives in the private corporation world or public corporations. But in the private sector there’s a lot of focus on shareholder value, maximizing profits and like, those are fine. I think that having good incentives, having people be productive and produce goods and services that are valuable to the community are great. For a lot of areas in human society there’s problems that are very amenable to that solution. In my mind, it’s like those market forces are really good at finding local Maxima. But I think for the longer view problems, you need a little bit more than that.

Ben: The only thing we have now–for biomedical research to be specific–is the academic model where you’re funding people to satisfy their academic curiosity about little pieces of this bigger puzzle of say neurodegeneration or evolution, or biology, development, whatever. And I don’t think it’s as intentional as it could be. I think that there could be grand projects or grand plans. Not so much like the war on cancer. That always kind of felt like it was more of a PR stunt to raise lots of money and awareness. These bigger projects where you’re saying here are the things we need to understand to be able to actually move the needle on this and here’s how we’re going to fund this in a very intentional way over, not three years, but like, 20-30 plus years. 

Ben: So you’re expecting failure and you’re building all of those things in and as a society, we just don’t talk and think like that. Half of society struggles to accept climate change is real. So it’s definitely an uphill battle, but like

Grant: Well, the NIH funding is a roller coaster.

Ben: Yeah

Grant: It’s hard to make a 20 year plan when you have no idea what will be happening with the overall budget. I do think that is a pretty controversial take, right? Certainly projects like ENCODE and the Human Brain Project and things like that have gotten a lot of criticism from scientists saying the money would be better spent on R01 or, internationally, R01-like grants. But it’s interesting, the kind of long view and squaring that with our system of funding is a challenge.

Ben: Yeah, definitely. I think the biggest challenge really is the human side of things and figuring out how to design these systems or articulate these plans in a way that works, given the sort of vagaries in personal human interest. I’ve worked in multiple consortia and with lots of different scientists in my time and it’s pretty amazing the variety of ways in which things can go wrong when you’re talking about collaborative exercises. I can’t remember who I was reading on Twitter or somewhere about, but there’s a scientist who was talking about how “I can’t trust anyone else’s data but my own. Cause at least with my own data I know exactly how it was collected. I know it was done right.” But I think at some point we have to, cause we just can’t get far enough having individual investigators.

Ben: The amount of people who are suffering so badly because of some of these diseases and the fact that we just work together. Like that just seems like it shouldn’t be the reason why we don’t move the needle. So I think that there’s some aspects here of the science of science that probably needs to be brought in. Like there was an interesting paper that came out–I think it was in nature last year–talking about how small teams could be more disruptive, that they can coalesce a new idea and move it forward very quickly. So they’re like the explorers who are going out and discovering some completely new, you know, asteroid or something.

Ben: But then it takes the whole community to vet that thing and move everyone forward. So in terms of how we work together as scientists, I think you need some hybrid model where you’ve got small teams that are taking big risks and then maybe finding some crazy new biology or whatever, but then you have to bring the whole community along.

Ben: The danger of some of the high profile publications is there’s such an incentive for people to be the one who discovers that asteroid. There was a paper that came out recently on somatic recombinants in APP, [Amyloid Precursor Protein] and they thought that some of them were more pathological and that they were getting reintroduced into the genome and all this crazy stuff. And that was a paper that came out of Nature a couple of years ago. And there was paper that came out recently basically saying how that was probably just an artifact. That’s like an example of where the community is doing its work, but it’s on such a slow, long timeframe. 

Ben: I don’t think it’s a problem that we make mistakes as a scientific community. Like that’s kind of the point, right? You’re on the boundary of human knowledge. It’s an inherently risky enterprise. Your ideas are probably going to be wrong more than they are right. But that doesn’t mean we shouldn’t have good mechanisms for vetting that. And, but also for encouraging that exploration in a productive way.

Grant: Absolutely. I mean, in my experience, it can be more difficult to get a rebuttal published. You can be in review for much longer and the standards in some cases can be even higher than for the original paper. And I think part of the reason for that is there’s not enough tolerance for people being wrong.

Grant: And I don’t mean things like fraud. I mean, that’s a totally different matter, but when people get a paper retracted or something, it can be seen as the kiss of death for the first author and a stain on the senior author and so on. And as long as it’s an honest mistake.

Grant: The consequences can be so severe that people will defend bad work that’s wrong long after they should, just engage their critics and recognize, “Oh yeah, this is, this is wrong.” And retract the paper with a relevant statement and move on. 

Grant: And to a lesser extent that I think that happens very frequently. There are a lot of papers out there where essentially the core conclusions of the paper are wrong. And everyone in that subfield knows that, but if you aren’t in that field–you’re entering from an adjacent field and things like this–unless you really talk with people or have a postdoc spend a year or two trying to replicate the results you don’t know, and we don’t currently have a good mechanism for communicating that because again, in many cases, people fight the retraction so it doesn’t happen.

Ben: That’s partially due to the incentives, right? It’s like your stock options or something, man. Like once you have a couple of those Nature papers, you could just keep exercising your scientific credit options for a long, long period of time. 

Ben: I think it’s a human behavioral thing. Like there’s a network effect: the rich get richer, that sort of thing. You’ve established yourself as a leader in the field, so it’s going to be so much easier for you to get that R01 or whatever other federal funding opportunity. When people are wrong, they’re going to fight tooth and nail because it has a very direct effect on their ability to continue to professionally be a scientist in the current model.

Grant: And the other thing I’ve observed–I’d love to hear your thoughts on this–is sometimes the criticism is wrong and the results are solid, the methods are solid, but in many cases, other bystanders rush to conclusions. They see a criticism or a rebuttal of a paper and without really reading it and judging it for themselves and assessing it on the merits, they take a shortcut that “this is crap.” Sometimes that’s right. I think sometimes it’s not. Sometimes these rebuttals are–let’s see, podcasts appropriate language– incorrect. 

Grant: And I think right now everything is very stilted. So, there is good conversation at conferences in person, but that’s not recorded that doesn’t get disseminated. There’s sometimes very polarized conversation on Twitter that doesn’t really get us towards the truth. How do you think we could set things up and take advantage of the internet and everything to get us closer to that in a way that is better recorded and more easily disseminated across both that sub-field but also the broader community.

Ben: Yeah, that’s a great question. I know that journals will often let the authors post their own rebuttal to the rebuttal. I was trying to think of a really good example of that. I think it was–oh, what’s his face?–David Reich at Harvard. If you read his rebuttal to the criticism, it was like a masterclass in how you defend yourself. But at some level, it almost feels like it’s a little bit more like science is becoming some sort of legal enterprise where you’re trying to make a case and it becomes less and less about a holistic synthesis of all of the evidence and more about debating your opponent and winning points on them in some way.

Ben: I think to answer your question, if there are ways in which it’s easier to share primary data, share all of the methods that are used and have almost like an audit type process. Where someone who doesn’t have any skin in the game, who’s an objective outside observer as much as possible, can go in and do an assessment. That would be one way.

Ben: The technological side of that is you have to be able to share data and methods. But I think until we get to that point, you’re always going to have this back and forth, these grudges that come up between various research groups. I think that’s all a lot of noise. 

Ben: Like you said, Twitter. I really like it– science Twitter–for seeing new science hot off the presses. Like that’s Twitter at its best, but for actual meaningful dialogue about these things, it’s just too easy for it to devolve into everywhere else in the internet. And then at that point, you’re just like, “okay, this is a waste of my time. I’m not getting a lot out of this.”

Grant: I mean, I’ve seen a lot of people essentially go quiet in the last few years or just leave their accounts together. I don’t know what your impression is of that, but my sense is maybe four or five years ago, there was a bit more of the back and forth. And now it’s gotten so polarized that you do see certainly some combative figures that are always jumping in and fighting with each other. But a lot of people just kind of lurk. And that’s mostly what I do. I just look for interesting papers.

Ben: It’s just too easy to say the wrong thing. I was just reading this article in the New Yorker. I’m going to be totally typecast now to your listeners: this guy loves the New Yorker. But it was in the one recently where they’re talking about the COVID-19 crisis and people getting shamed on social media. And how we still don’t quite understand the effect of social media. Public shaming has been how society enforces certain behaviors, but we’ve now created a technology that puts it on steroids. And what’s the effect of that? And just sort of fascinating.

Ben: And I think it can stifle open and frank conversation because people don’t want to login and get all this hurtful feedback from hundreds of thousands of people. That’s just a bummer man. 

Grant: I mean, it seems like the challenge is the monkey mind, and maybe tech can’t save us. Maybe it kind of amplifies it. And although–the thing is–some of the same people who are just total jerks on Twitter, are perfectly nice seemingly reasonable people in person. I think there is a psychological element to being face to face with someone versus typing on your phone.

Ben: Yeah. The anonymization piece of it. I think you could talk about that in the context of peer review too, if we’re just hitting all the related topics. I think the anonymization, there are good reasons for it in peer review. There’s also probably some pretty good reasons against it.

Grant: Do you sign your reviews?

Ben: I haven’t been. I might start now, especially when I’m going to a startup. I might start signing them because I’ll be in industry. Because your incentives are less linked to the whole academic system, there’s less chances for things being held against you later.

Grant: Right? It’s crazy. Some of these grudges you see they date from 25 years back. But it’s such a small world that it does have a substantial, negative impact.

Ben: It’s a very small world. Like the number of people in Study Section is not that many. And it’s basically like the last person standing, who gets to the point where they get invited to the study section. 

Grant:  Especially where a single person can essentially sync an application. I think that’s kind of a problem maybe and how the aggregate scores are competed.

Ben: Yeah. You know, I think most people are acting in good faith in Study Section. And in most reviews I’ve received as an author, there’s obviously exceptions where people are just kind of nasty and that’s just unnecessary. We should all as a community, make a strong stand like, “don’t be nasty in any of your reviews.” I don’t know why that’s a cultural thing in science where people can be just straight up mean, just give your thoughts and give it to them straight. But there’s no reason to tear people down.

Grant: Well, some people are just mean. For some people, the anonymous factor plays a role, but there’s some scientists out there writing under their own name that are very openly mean well beyond just making their scientific point. I mean, it’s kind of funny because you know, I’m pretty sure, like most of us, they were probably bullied as kids and things. Somehow some people become the bully.

Ben: Yeah they probably internalize it and they probably aren’t even consciously aware of what they’re doing is the sad part. It’s just how they’re reacting to that situation, given their personal history. Right. 

Grant: Yeah. Do you have anything else you’d like to add?

Ben: Yeah. I mean, a question I have for you, maybe I turn the Peter Thiel question back on you. I’m just curious what your take is on that. Like, what’s an opinion that you hold that other people would find controversial? 

Grant: That’s a good question because it’s not actually something I’ve thought about. Even though I asked you right? 

Grant: I think the chances of an existential calamity to modern society are higher than most people think. I mean, there’s a lot of fragility. We are extraordinarily dependent on the internet for so many things. And in many ways, if a lot of the backbone infrastructure of our civilization were suddenly severely disrupted–you know, if you’ve got a very strong solar storm or something like this–I think it would be difficult for us to reorganize quickly.

Grant: I mean even this COVID stuff. This is like an IFR 0.5% respiratory virus. Throughout the 19th century, we had infectious disease epidemics that were far more deadly on a regular basis, every several years. We’d have something like this.

Grant: And of course we’ve tamed that through modern medicine and with vaccination, good clean water, and things like this. But something like this that no one would’ve really batted an eye at in the 19th century has done a lot of damage around the world. Not enough to end civilization as we know it or anything like this. But I do think it reflects a greater level of fragility because a lot of the ways we used to do things, we don’t don’t have anymore. So a lot of even workplaces now increasingly are getting rid of landlines. It’s just so many things that were backup systems, we’ve gotten rid of for the sake of efficiency that we can no longer fall back on. 

Grant: And I don’t know specifically what that shot could be. It could be any number of relatively low probability things, but if you take a lot of low-ish probability things and integrate over time, the chances of something happening are more than negligible. 

Ben: I was just gonna say, I totally agree with that. So I don’t fall into the camp that doesn’t, but

Grant: So maybe it’s not as controversial as I thought. 

Ben: I don’t know if I’m a typical person. But I think that a lot of that has to do with incentives. Like you said: efficiency. Markets are always looking for unrealized short term efficiencies, but these big scale risks, these black-swan events. The local risk model where your tails are very thin and you’re like, “Oh yeah, no, that’s like a 15 Sigma event. That’s not going to happen until the heat death of the universe.” Well, no, the distributions for those sort of events don’t follow that for a while. 

Ben: I think a lot of the incentives are linked to short term thinking. Coming back to what I was saying earlier, if you think more long term, then you start to think “Oh yeah, no, we’ve got to design our systems to be less fragile. We have to build in redundancy” And that there’s that concept of antifragility, where you actually have things that, in the presence of perturbations, become stronger. Those sorts of conversations, it’s rare to hear them. It’s not like what we’re taught. It’s not like this crazy political season that’s what you’re hearing on the debates.

Grant: Right. Well, and that’s another thing, maybe my other answer to that would be–although I think this has become a lot less controversial in the last few years–is just that the modern democratic-neoliberal order is much more fragile than most people recognize and we take it for granted in a lot of Western countries, in English-speaking countries, and things. We assume it will be like this indefinitely. But there are already cracks, right?

Ben: Yeah. Not just in the US either. It’s like everywhere.

Grant: Right. And the relative freedom and prosperity and things that we’ve enjoyed for a number of generations here, in the long view of history, is very short. Hopefully we can keep that going for as long as possible. But I think it’s far from guaranteed. You know, we could see things break apart in our lifetimes. I don’t know. Hopefully not.

Ben: Gosh, I hope not. And that has become a lot less controversial in the last few years, but yeah. I think climate change, that’s the real X factor. I mean, even the defense department was putting out a report on how climate change is going to cause all this geopolitical instability.

Grant: I mean, I think climate change is a part of it. I think it’s a lot bigger than climate change though. Climate change certainly contributes to and accelerates a lot of the habitat loss and things that were already occurring and have been for a very long time, but at the end of the day. Actually in our last last episode Chris was here, we actually talked a bit about ecological disaster. 

Grant: I think something like that is more than just a possibility, depending on how you define it. If you talk about mass extinction events, that’s a certitude. It’s already happening in a lot of the insects and things like this, on which ultimately the charismatic megafauna depends, are already on their way out.

Grant: You know, it’s kind of a nervous laughter kind of situation. But yeah. People are pretty adaptable. It’s not–I don’t think–going to be the end of, certainly not the end of life on earth. And I don’t think the end of humanity on earth or anything like that, but it certainly will make things different. And there will probably be a lot of people wishing that their ancestors had made different decisions.

Ben: Yeah, I totally agree with that. It’s all kind of unnerving. I’d really like times to be a little less interesting for a bit. They just seem to be getting more interesting.

Grant: Yeah. Boring isn’t bad. Yeah. 

Grant: So what are you doing in between Sage and the startup? I know you’re not hiking the continental divide or something, but obviously your options are limited at this time.

Ben: I know. I’ve had a week off and I’m in Bend, Oregon right now taking a little bit of a break. Though it wasn’t much of a break cause I was working the last two and a half days on finalizing the editorial changes on my last paper from when I was at Sage. So I feel like I was kind of trolling myself. Like “I’m going to have this week off to relax.” And then I’m like, “Oh no, I need to get this edit. Cause it’s going to be a pain to do that once the job starts.” 

Grant: Oh yeah you’re going to be busy.

Ben: But that’s done now. Thankfully I got those in yesterday. So I don’t know. You know, I’m an aspiring ultra runner. So, I do a lot of running. I’ve got a big race coming up in February next year. Hopefully it’ll happen. Obviously who knows with COVID. It’s the Black Canyon 100 K down in Arizona. So I’m just trying to put all the work in so that hopefully that’ll go well.

Grant: Well if the official race doesn’t happen, you can always go to Arizona and run by yourself. 

Ben: Go run for like 11 hours.

Grant: Make yourself a shirt, right?

Ben: That’s right: 11 hours just in the desert.

Grant: Thanks for joining us today, Ben. I appreciate it. 

Ben: Thank you, Grant really appreciate being here today. It was a lot of fun chatting with you.

Grant: Awesome.

Transcript of Episode 2: Chris Ponting

Grant: So welcome to The Bioinformatics CRO podcast. I’m Grant Belgard, and joining us today is Chris Ponting. Chris, would you like to introduce yourself? 

Chris: Hi Grant. Yes, my name is Chris Ponting. I’m from the University of Edinburgh. I’m the chair of medical bioinformatics here. 

Grant: Lovely. And Chris was also my PhD supervisor many years ago. And then my supervisor for a second time, a couple of years after that. 

Chris: Well, you’ve gone on to greater things than I have, so well done. 

Grant: Oh, I don’t know about that. So, yeah, I just wanted to talk about some of the things that you’ve gotten into in recent years, especially your move to ME and CSF. I’d love to hear about that and what your thoughts are on the comparisons that are being drawn to post-COVID syndrome. 

Chris: That’s really interesting and a story that really goes back many years. So I was at university with a guy called Simon McGrath and some years later he got ME, myalgic encephalomyelitis. And it destroyed his life or at least his expectations and hope for the future. I felt for many years that I could do nothing about this as a scientist. And then recently I was just thinking that perhaps the techniques of population genetics might be interesting and useful. So I dipped my toe in it, and eventually after some years, some discussions, and bringing together many people from across the United Kingdom, we were awarded just this year over 3 million pounds to start a genetic study of myalgic encephalomyelitis, ME. 

Chris: In the middle of that, of course, COVID comes along. It was apparent to many beforehand and certainly more now that there are some interesting overlaps between post-COVID syndrome or long COVID or whatever you want to call it.

Chris: And after all, many people with ME report a viral infection before they come down with their syndrome. And this is exactly what’s happening now with long COVID. Now I don’t wish to say that long COVID is ME, it isn’t, but some people may resolve from long COVID into ME, which in this country as an adult, you need to have symptoms for over four months.

Chris: But watch this space! Basically, I know some people are doing some, or the beginnings of some genetic studies of long COVID. And it’s going to be fascinating to compare the genetic signals for ME, with the genetic signals for long COVID. Are they different? Are they the same or do they overlap, but not completely, et cetera.

Grant: Have there been any genetic studies of ME that have been broken out by the kind of proximal cause. So if it were a viral infection, have there been genetic studies broken out by the subtype of virus and so on?

Chris: So you would have thought that there would be plenty of genetic studies and well-powered ones involving many people. And you would think so because in this country, in the United Kingdom, around a quarter of a million people suffer from ME. But there are not. There aren’t any well-powered genetic studies anywhere in the world. So we know, nothing about the genetics of ME. We know that there is evidence that it is inherited, which gives us some support for our case that we should do this genetic study. But we know almost nothing about what will eventually be seen, obviously, as being a whole set of different conditions, which have different contributions made by the environment and from different parts of our chromosomes. It will be one of these complex disorders and teasing everything apart will take time. But given that there’s no effective treatment for people with ME, we really have to start with the genetics because we know almost nothing as researchers in ME.

Grant: And why do you think it’s been so neglected? 

Chris: It’s been neglected essentially because of this lack of information. The ignorance that we, as researchers, have towards it. We know almost nothing. It’s multisystem, which is difficult.

Chris: If it was affecting only one system, then we would know what to do, but, for some people it manifests mostly in muscles, others in the brain, others it may be perceived as an immune response problem, for many people it’s all three or more. So that’s a problem, but also there’s been a problem with its diagnosis. It’s a diagnosis by exclusion, meaning that it’s not particularly easy to diagnose. And that means that often people are not diagnosed. Indeed in the United Kingdom, the time taken on average to be diagnosed is about eight years. It’s neglected because we know very little. So “Let’s start finding out” is my view. 

Grant: And are there any countries that have been ahead of others on looking into this or has it been very well neglected across the board?

Chris: So it’s neglected a disease across the world. There are many countries where it’s not recognized at all by health professionals. And there are many health professionals across the world who think of people with it, with ME, as malingerers or who make up their disorder for whatever reason. It is the most devastating of diseases. In terms of quality of life, it’s so much worse than almost everything, including many cancers and multiple sclerosis, et cetera. Why anyone anywhere would make up such a disorder. I just don’t know.  It’s almost Victorian in our approach to this disease in the sense that when we look back on this, maybe in 10 to 50 years time, we will understand what it was. And think “why on earth did we overlook it and disbelieve all of these people for so many years.”

Grant: So I guess this feeds into another question, which is what do you think we need to do differently in biomedical science? And there are probably many answers to this, but. 

Chris: So one thing is we need to listen. If we’re studying a disease, then the person with the disease is often the expert on it. They have lived the experience of the disease. If they say that it fluctuates in different symptoms, that’s what it is. So listening and acting upon the experiences of people. And it’s not just their lived experience, but also they’ve become experts in the literature. They often, in my experience coming in from the outside has been, many people with ME, despite the fact that they are fatigued and they have post exertional malaise, and often can’t do things even things like brushing their teeth, but they do get involved and read the literature and are experts in it. And so I’ve often gone to my friend or others of people with ME and asked their advice as to what we should do scientifically.

Chris: And we should also pay attention to the fact that there are many scientists who have ME, many people who, in multiple different professions who have long COVID. I see nowadays that such experience can be thought of as being negative, that somehow subjectifying their experience, because it is they who are suffering, actually is not valuable. We need to be dispassionate in our observation of disease. And actually, I don’t think that’s right. 

Grant: That’s really interesting. So if I were to push you a little bit out on a limb and ask you to speculate, what do you think we might learn about the disease, if you had to give your best guess about ME CSF and it’s overlap with long COVID and so on? You don’t have to answer this.

Chris: I don’t mind answering questions, which are all speculative. I think as scientists, this is what we do. We do speculate, but in the knowledge that many things that we speculate about, we have no evidence for.  We put forward hypotheses, which is a part of speculation, and often we try and work out whether those are false. So one answer to your question is that I would not be at all surprised if it were to turn out that there was a mitochondria component. So, as you know, the mitochondria is the energy of the cell, so the battery of the cell. And if something were to go wrong with that, it would affect many different systems. It would affect the immune system. It would affect the nervous system. It would affect muscle, et cetera. And, and that would make complete sense to me if that was going wrong. 

Chris: But I actually think it will be many things. There have been people with ME, who’ve been diagnosed with ME, who have surgical operations on their neck and have had many fewer symptoms since then–not a large number, a smaller number. And I need to say that it is a procedure that is fraught with risks, so anyone thinking about it should go to their GP, their practitioner. But it is highly likely that there will be many different contributions made. And this is not easily understood because the medical model is that if you have a set of symptoms, it will be one thing.  But from genetics, we know that many different things can break and manifest in the same way in a person.  And there are many different ways in which the clinician can be hoodwinked by symptoms into thinking that symptoms are one thing and they’re many. So I am willing to speculate on the basis of evidence and thus far, we have none. So from our genetics, we will have some, I hope. And from there we will draw up the hypotheses and hope that many of the experts in those different fields will take on the challenge of seeing whether those are false or indeed real. 

Grant: And can you talk a bit more about your genetic study, and, you know, the design and when you might expect the results?

Chris: I think the results will come in about two and a half years from now.  What we will have done by then is to look at the DNA differences in 20,000 people with ME in the United Kingdom: differences with respect to the general population. We’re lucky in this country because we have something called the UK Biobank where half a million people have had their DNA changes read out. And so all of that’s been done. We don’t need to look at those, who we would call controls. We only need to look at the 20,000 cases of ME. Now we had no idea how long it would take us to get to 20,000 and we haven’t yet formally launched, but we have a registration page on the Decode ME webpage, and 20,000 people with ME in the United Kingdom have registered for this study, which is outstanding.

Chris: And the work that’s been put in by charities and digital marketing companies, et cetera, to get us to that point, even before we launch is fabulous, which gives me confidence that we really will be able to go quite fast in the early stages. What we’ll find, I don’t know. Maybe we find nothing–possibly–but we should look.

Grant: That’s outstanding. What are your thoughts on how communication from scientists has been handled with COVID-19? With, you know, many preprints going out, there have been a number of preprints that have influenced the discussion among the general public that wouldn’t pass muster on review and so on.  On the other hand, certainly science has moved much, much more rapidly with this than I think anything we’ve ever seen before in a positive way as well. What are your thoughts on that and on how politicians have kind of adopted, in many cases, scientists to agree with whatever they want to push? In the state of Florida, for example, our governor recently had a series of conference calls with some scientists who had somewhat fringe views on the pandemic and what should be done about it. But what are your thoughts on all that? 

Chris: Well, I think it’s really interesting because previously I think the public thought of scientists has been quite a homogenous bunch with the same views. Um, and that’s not true at all. People think of things in very different ways. And those kinds of controversies and conflicts in ideas really have not been exposed to the general population before. But now that they are because we’re seeing people on the same day writing letters to the same place to say, I believe in X and others saying, I believe in not X and that is causing confusion, but it actually reflects the reality.

Chris: I think the more it is understood that there are differing points of view and the more that you air them, the more people can weigh in and give their views, the better. And that is happening. Although you implied that peer review, which takes preprints into the publication domain, isn’t perfect. And just because it gets published, doesn’t mean that it is correct. And in fact, it’s the goal of scientists to demonstrate that what was known previously was either imperfect or incomplete. And also I don’t think the general public understood. 

Chris: Now your question also makes me say that the politicians who are considering the scientist views really are not qualified to understand the science, which is a shame. Why is it that scientists don’t become politicians? If we had a cohort of scientists who were members of different legislatures.

Grant: I guess Germany does, right? 

Chris: It does. They’ve had a very coherent response to COVID, but our country in the United Kingdom–and I think in the United States, it’s the same–there are almost no scientists. And I think that’s a huge deficit because the greater expertise, and the breadth of expertise that there is in parliament here, I think the better laws will be made and the better decisions would be taken. At the moment, we have the situation where people who have taken arts degrees–which are great–are making essentially scientific decisions. And I’m not sure that that has led to good outcomes here in the UK. 

Grant: So if you’d be willing to speculate again, the UK seems to be undergoing a second wave now, right? How do you expect that to play out? I mean, obviously there are a lot of decisions every day that you have to make about how you and your own family will respond to this.

Chris: I think it’s incredibly hard for every single person to judge what they should and what they shouldn’t do, and it’s leading to a huge amount of anxiety. We went to a restaurant as a family last night for the first time in a huge while. But of course we were worried about that. Should we have done that? Are we infectious? Are others infectious? Should we wear a mask? We did. Unfortunately, I think this is going to be the situation for others. 

Chris: Someone asked me yesterday, “how long is this going to last?” I mean, how do I know? I’m not an expert. I’m not a virologist or an epidemiologist, but I answered. I said, “I think we have another two years of this. I don’t think we are going to be saved early by a vaccine or several. We don’t know whether a vaccine like that is going to have great efficacy across the whole population.” So the best thing to do is to try not to infect people. And people are finding this hard because they lose their freedoms. They are used to traveling the world. They are used to doing X, Y, and Z and hate the idea that they’re being told that they can’t do it anymore. But a better idea is to protect other people. And I’m an optimist that feeling of protection others will eventually prevail. 

Grant: I do think there’s been a bit of a move in that direction even here. I would say a few months ago was probably the height of people screaming at each other because they didn’t want to wear a mask or whatever. But I think over time, people are realizing that the virus is real. You know, certainly if you go out when sick and so on you’re kind of in A-hole.

Chris: We needed to be tested because one of the household had symptoms. And so we went to a drive-in testing facility locally. And that was a sobering experience: watching the line of cars, watching the expense that the state has put into testing so many people, getting your results back within 15 hours was amazing.

Grant: That’s fantastic. That’s unheard of here. 

Chris: It’s not half an hour as it is in airports in Germany, et cetera, I mean, in Italy, but that was a sobering experience. I think, for us all. And it brought it home to us how widespread this is. That yes, maybe one in a thousand people are infected at any one point, but that is a huge number. 

Grant: What are your thoughts on–I don’t know if you’ve looked into this. You don’t have to answer if you haven’t. But, what are your thoughts on the prevalence of long COVID, and the longer term consequences? I mean, it seems, there are a lot of contradictory numbers about, you know, exactly how different studies are conducting surveys and differences in definition and so on.

Chris: This is a great example of where science doesn’t know. It doesn’t know how to define “recovered” for people with COVID. And if we don’t know how to define people who are recovered, then we don’t know how many people are ill still after, however many months. So we have to set down our structures, our frameworks, our concepts, and that’s beginning to happen. And upon those, are new studies that are going to be determining the proportion of people who are unwell still. Now there will be people who’ve been in hospital and intensive care who will still be ill. Um, and they are often going to have other conditions and they will be very poorly for a long period of time. And then there are people who are going to be unwell essentially for the rest of their lives, just as people then me off and. 

Chris: Your question is really how, what is the proportion of people who have been infected will be affected with ME like symptoms for the rest of their lives? It could be small. It could be of the order of 1%, which is the mortality rate as well. 1% of everyone is a large number. So if it’s 10%, it’s 10 times that large number. And we would have in that scenario a whole stratum of our society who would be unable to work, unable to look after themselves in many cases. A quarter of people with EM are bedbound or housebound. And if that’s the case with some people with long COVID eventually, then that would be the case. 

Chris: It’s a horrifying thought that that might be the case. But people with ME look upon that as something that might be an opportunity for ME to be understood, but more importantly, with a huge amount of empathy and a fellow feeling that they’ve always been left alone by society. And through no fault of anyone, they might be joined by a large number of others, which will mean that society will have to pay attention, which they haven’t for so long. But I haven’t answered your question. Your question was, what’s going to be the health burden in the future? And the margins of error of an estimate are too large to know at the moment. I don’t know.

Grant: Yes. Speaking of COVID, have you attended any virtual conferences? 

Chris: I was a co-organizer of a conference over the last two days. I think most people are Zoomed-out, being on Zoom or whatever calls, throughout the day. People, including myself, by the end of the day were just exhausted.

Chris: That conference actually, however, had some energy to it. We made use of breakout rooms and that was sort of randomly done. We allowed, you know, people to interact in ways which were less formal, and we paced it I think quite well. At least the feedback said so, but conferences are going to change quite clearly. 

Chris: I go back to the ecological case that conferences actually have not been very good for our environment for many years and I’ve contributed to that. So we will continue to communicate in that way virtually at conferences. And I think that’s a good thing. We will miss one another. We will miss, you know, looking one another in the eye and having a beer or whatever, but that’s a small price to pay really for everything else that’s going on at this moment. and what’s already happened over the last few decades.

Grant: Why do you think zoom is so fatiguing? And everyone has the same experience. You know, you can be at a conference all day and it’s fine, but if you’re on back-to-back zoom calls for three or four hours, you’re exhausted.

Chris: I think in a conference you can zone out. You can kind of do what aquatic mammals do and switch your brain off and sleep. Well not sleep, you know we don’t sleep in conferences, but you can at least zone out for short periods of time and then come back in and focus. If you’ve got a bank of 20 people staring at you, you shouldn’t really do that.

Chris: And so you don’t and you fix on what’s going on, and I think that’s very tiring. So at the beginning of lockdown, when we have back-to-back Zooms throughout the day: eight or whatever per day. I had to change that. I had to move to being more spread out. And as winter comes here, we’re going to have to organize ourselves so that we can go out in the middle of the day and actually see some sunlight and get some exercise. So we’re going to have to plan our days differently. 

Grant: So I guess on a completely different note do you want to talk about your novel or will that be more of a surprise?

Chris: I’ve forgotten that I told you about a novel. I basically finished a novel. I’ve not sent it to anyone. The first version was read by my wife and she was rather scathing, probably quite correctly. So there is now the second version, which is probably a bit more thoughtful and explanatory. And it’s a dystopian novel. It does derive from a genetic story, but it takes a whole bunch of quite broad subjects from the ongoing ecological disaster that we’re in. And it has a theme that I thought was not particularly topical at the time, which was viruses. But now that it’s done, it’s complete, viruses of course now de rigueur. And so people might think that, I took inspiration from the COVID pandemic, but actually this wasn’t the case. 

Chris: The idea is that it is the human race on the earth that is the virus that is infecting our biosphere and so many species are being driven to the wall by our infection on the planet. It’s not a particularly nice idea. It’s not a particularly new idea. I go on to say that there’s more to it than that. It is that the men of the human race that are culpable. So there is a character–giving some of the plot away cause it’ll never be published obviously. 

Grant: You could always self-publish. You can get anything out these days. 

Chris: I could self publish. Yes. There’s a character, a woman who basically realizes that one way of putting a handbrake on the ecological disaster is this essentially to try and hobble males of the human race. So she introduces a virus to do so, which targets the men only and in so doing it does two things: it hobbles the males, and reduces the ecological disaster. But unfortunately viruses will do what viruses do well, which is evolve. And so, the virus then begins to jump to other animals and to women.

Chris: And causes a devastating effect on the whole of the human race, which has only countered–I’m telling you the whole story now–it’s only countered by the introduction into the germline of a mitochondria-like entity, which targets the factors that have led the males to be hobbled. And basically immunizes some people but these are only women to the effects of this virus. And so we have basically a matriarchy, whereas previously we had a patriarchy. 

Grant: That sounds really interesting. I would love to read it. 

Chris: I’ll give you a copy. I’m going through it one last time at the moment. 

Grant: That’s great. So maybe that feeds into one question I like to ask, which is: what would you do if you weren’t a scientist? Would you be a novelist or something else? 

Chris: Absolutely. I’ve already started thinking about my next one–if I’m going to write a next one. I’ve actually enjoyed writing a lot because it is freeing. As a scientist, I have to write facts, the facts as I understand them. As a novelist, you can write anything, and that freedom to venture anywhere in your mind is wonderful. You’re not constrained by the evidence that’s out there. So I’ve really enjoyed doing that, but it also takes quite a lot of rigor to do and to plot out all of the different characters and the themes, et cetera. So this is what I’m doing now for what might become a second, which is on the theme of clonality.

Chris: The idea is that–I haven’t written a word of this–that a male clones himself, thinking that he will gain immortality. But actually all that he does is introduce another male into the world. Who’s much younger than him. And he finds it to be quite a challenge to his sense of superiority.

Grant: Interesting. And I’m guessing it’s not in the same world as the first novel. It’s a totally separate world.

Chris: Yes, both of which are empty of COVID-19. 

Grant: And what’s your process like for that? I mean, do you dream up all the characters and the plot and so on before you start writing or is it very iterative between the writing process and kind of picturing the world?

Chris: So those are the two processes I’ve adopted. The first was organic: organic growth that just percolates up in your brain and that leads to all sorts of conflict in your mind and inconsistencies in the plotline. So I had to go through the plot again and again and again, to try and make it consistent. That took a long, long time. So what I’m now doing is trying to ensure that the plot is cogent, well-thought-out, the characters are three dimensional, and their interactions with one another are well-described even before the first word is written. So let’s see, the first way of working led to a full draft. The second way of working hasn’t yet generated a single word. So let’s wait to see. 

Grant: That’s great. So what do you think is the most interesting thing happening in biomedical research today? You don’t have to pick one, you can pick a few.

Chris: I think the most interesting thing that’s happening in research is how it’s becoming much more immediate in its effects on the population because of COVID-19. There are researchers now who have generated findings last week, who put them up as preprints this week will know that through the media they will become known by millions of people tomorrow. That immediacy of effect is changing the way that researchers are considered by the population, by governments. But it’s also changing the way that scientists themselves are thinking how they can do that science and what effect and impact they have on others. 

Chris: A lot of science is done without thinking about “what is the impact? What is the benefit, the immediate benefit?” These are blue sky science, and that’s a good thing. So I think hopefully once COVID blows itself out, there might be a long lasting impact on the idea that scientists should engage more with the population, work out what are the issues that vex them most, and also work on things that are more impactful. So that to me is interesting, more sociological than anything. 

Chris: What was interesting before that, of course, was the advent of CRISPR: this idea that as scientists of model organisms from flies to mice, to human cells, we can go in and edit and cut and paste and change DNA. And in that intervention ask, “what does this letter do out of our 3 billion? What does that letter do?” And really, we hadn’t had that ability before. And that really was game changing almost within months everything changed in genetics. 

Grant: And where do you think that’s headed? What do you think will be the most impactful thing that comes out of CRISPR and related technologies?

Chris: I think. All of those technologies are now giving insights into the world of molecules that we had never had before. What do I mean? I mean that we’ve been able to observe. We’ve been looking at cells and molecules and watching them go by and prodding them and seeing what their response was. But never really have we been able to intervene directly in a very targeted manner and have alongside the edited cells, the other cells, the wild type cells, and then compare them one to another and actually see what is going on.

Chris: That may not seem to be a big deal, but absolutely it is. And it’s taking biology, I think, one step closer to other types of interventional science, for example in physics and chemistry, where previously biology was much more observational. 

Grant: Do you think we’ll still be eating meat as widely as we do today in a generation?

Chris: No, we won’t be eating meat, if we’re still around a generation from now. I don’t believe so. There will be a proportion of our population–as ever–that will take up a large slice of our resources of the world, but most people I don’t think will be eating meat in a generation. At least my vegan daughter would hope that we don’t see as much meat then as now.

Grant: And do you think CRISPR and related technologies might play a role there?

Chris: I have to be careful here: I have colleagues who work in this area. Is CRISPR going to have a long lasting effect on livestock? It is going to have an effect and absolutely is being used currently to improve–as some would call it–on livestock traits. Is it going to last for more than a generation? I don’t think so. But whatever prediction I make, of course, doesn’t matter because I’m just one person. And once another generation has turned, I will have shuffled off my own mortal coil. So I can say whatever I want now. 

Grant: And have you seen what Meatable and similar companies are working on now? It’s a little bit of genetic engineering, but…

Chris: Yeah, I don’t know much about this. 

Grant: Moving to lab-grown meats to get away from animal-derived. 

Chris: My, my question would be what are the resources that are required to generate those lab grown meats? It may be that there are issues there that would need to be, considered quite carefully. The best thing is simply not to eat meat or drink milk. I’m not a vegan, right. I’m just saying that’s the best thing for our world going forward. If everyone were to make that same decision, the whole world would be such a better place. 

Grant: Certainly. In, I guess, probably even still the very early stages of a very great extinction event. Yeah. And practically speaking, do you think that there’s much chance of avoiding that or are we so far along at this point?  

Chris: As a human race, we’re not avoiding ecological disasters at all. We’re observing them and not learning from what has happened over the last few decades. It’s a train crash that we’re observing year on year. And I’m constantly reminded by people in my family who say “Yes, COVID is a huge thing, but the biggest thing, the greater thing than that, is the ongoing ecological disaster.” And I played my part in that. I think everyone in my generation needs to stand up and retell exactly what we’ve done and then ask ourselves what we need to do in the future.

Grant: This is depressing, but true, right? Um, so what do you think is your most controversial opinion in science? 

Chris: So we have evidence that we’ve published that 90% of the human genome does not alter who we are and what we do. And that if there were to be any changes in that 90%, it wouldn’t affect us at all.

Chris: Many people I’ve talked to who are scientists, or who are not scientists, are absolutely outraged by this idea. The scientist cannot believe that the molecules that we have in all of cells, or parts of molecules would not generally do anything. But that’s what the data says. I have to look at the data and come to a conclusion. We’ve looked at the data in a particular way that no one else has. And absolutely. It makes sense.  It comes to that conclusion, and others, using different approaches, come up with something similar.

Chris: So interestingly. We as humans would like to be sort of perfect objects, where you know, the genetic codes in our chromosomes are, in some way, perfect machines. It’s not true at all. In fact, you know, that there is an evolutionary argument, which is well-established, that as a population we’re pretty poor at getting rid of bad mutations. So we carry them in our population and pass them down through the generations often, more often than other species. So we’re certainly not the epitome of all animals now in that respect. So I think that’s a really interesting view of the human genome. And not one that, as I said, every single scientist and every single member of the general public would agree with. 

Grant: That’s interesting. You’ve certainly ended up in a very different place than where you grew up as a child, moving to England and then Scotland and so on from Uganda. Where do you think you’ll retire? 

Chris: That’s a great question. Where I’m allowed to retire by my family. Where would I like to retire? I spent two years on the West coast of Canada. And the countryside there, the variety of countryside from rainforest to arid desert in just a hundred miles or so, is outstanding and outstanding beauty. Great people: the Canadians. And I quite like that. I won’t be allowed of course. We’ll have many more family ties inside the United Kingdom. But I can always think of that as a dream and wonder whether that’s going to happen or not. 

Chris: Um, England? Unfortunately I’m less fond of England than I was. So I’m not going to retire there. Politically I think it’s not a place that I recognize as the one that I grew up in. 

Grant: Do you expect that by the time you retire, Scotland will even still be part of the UK? 

Chris: Well, this is a big question as to what’s going to happen with Scotland. The current opinion polls are that if there were to be a referendum now, Scotland would go independent. The separation of Scotland from the rest of the United Kingdom would take a very long time, just as the separation of the United Kingdom from the European Union, which I regret hugely, will take far longer than anyone has ever thought. And that will happen with Scotland. 

Chris: We share a border, which is, you know, you can not see the border, you just pass through it. It’s just like a state line in the US and that would just have to change, particularly if Scotland were to become again part of the European Union, which most people in Scotland would wish. 

Grant: Just logistically it seems like it would be difficult for them not to. It’s such a small, well, it would be such a small country. 

Chris: It is a small country. It’s, you know, 5 million or so, but that’s sort of similar size to other members of the European Union. And such countries have benefited hugely from being part of that Union. And I think sometimes we focus too much on large versus small equating them to important to not. And Scotland’s always been at the edges of the United Kingdom and made much of that. I think it might do so again. 

Grant: Great. Is there anything else you want to say before we wrap up? 

Chris: Not really Grant. It’s a pleasure talking to you. And I wish that I could ask you as many questions as you’ve asked me today. So we should catch up separately. 

Grant: Yeah. Definitely. 

Chris: And I wish you well, you and your family well, obviously. I don’t think so. I’ve covered quite a lot of ground and probably some ground that I’m happy to stay in, but I’ve not covered before politics, et cetera. 

Grant: Thank you for joining us, everyone. Hope it was a nice conversation.

Chris: I’ve enjoyed it a lot Grant.