Transcript of Episode 8: Peter Joyce

Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard, and joining me today is Peter Joyce. Peter, can you introduce yourself please? 

Peter: Hi, Grant. Good to be on. I’m Pete Joyce, CEO of Grey Wolf and co-founder of the company back in 2017. Good to be on the podcast. 

Grant: Great. We’re glad to have you. So, can you tell us about Grey Wolf? What are you doing? 

Peter: So Grey Wolf is a company developing small molecule inhibitors of two enzymes called ERAP1 and ERAP2, and by inhibiting these enzymes, we’re aiming to increase tumor visibility to the immune system so that we can promote the attack and destruction of cancers and act as a novel immuno-oncology therapy. So the company closed series A funding in April of 2018 and excitingly in the last few months just nominated our first molecule that’s now making its way through formal, nonclinical development against ERAP1. And so we aim to be in the clinic in the next couple of years. 

Grant: And if successful, where would this fit in the menagerie of oncology drugs?

Peter: Yeah, it’s a good question because immuno-oncology is certainly a very exciting and hot space, and the anti-PD1’s and anti-CTLA4’s have been game-changing in the area. We see an awful lot of very interesting approaches following up after the anti-PD1 and anti-CTLA4, going after various other checkpoints, for example, or driving activation of the immune system in different ways as well as potential vaccine therapies.

Peter: And whilst, no doubt, some of those will be successful, there isn’t really any therapy looking specifically at the tumor visibility problem. What’s apparent from these checkpoint trials that have been running with anti-PD1 and anti-CTLA4 is there’s a good proportion of patients that aren’t responding to them. Apparently the tumor is not as visible to the immune system, and that’s where we think ERAP1 and ERAP2 sit, is the ability to modulate how the tumor is perceived so that you can promote their recognition to the immune system. And so it dovetails quite nicely with something like a checkpoint, and really gets at that visibility issue. 

Grant: And, how large is that patient population, approximately? If it works amazingly?

Peter: Yeah. So the promise of any ERAP1 inhibitor or the ERAP2 inhibitor in theory is relatively broad. Even in cancer types such as metastatic melanoma or lung cancer where these checkpoint inhibitors have worked particularly well, there’s still a lot of patients that unfortunately aren’t seeing that prolonged benefit in excess of 50% in those diseases where we are seeing really high, high benefit. And then as you move down the visibility spectrum into cancer types that maybe are seeing more like a 20% durable response rate, as you can imagine, there’s a lot of patients there that unfortunately don’t see that survival out beyond one to two years that 10-20% of patients are seeing. So it’s very large. It’s quite hard to calculate, but that’s why there’s so much interest and excitement in this space, because whilst there’s a lot of exciting work that’s happened, there’s a lot still to be done.

Grant: Great. Can you tell us a little bit more about Grey Wolf as a company? How the company works, why you decided to organize it in that way, and how it was affected by all this madness around COVID? 

Peter: Yeah, happy to. So maybe just to introduce the company itself, it’s a company that I started with Tom McCarthy in Oxford, in the UK. We started it in late 2017. I was previously at Vertex Pharmaceuticals as a drug discovery scientist there, and the company itself is, I guess, a virtual company. We don’t have any labs of our own. We operate with a number of different specialists, academic and contract research organizations, and really we coordinate and direct the research. Because you know, drug discovery and development is so multifaceted, and requires an awful lot of expertise.

Peter: That’s a hard thing for a very small company to have all in-house. And we run the company as if it were a project team inside a larger organization, really making sure that all of these different parties, of which The Bioinformatics CRO is one, are all working together with the with the clear aim of trying to deliver these novel therapeutics into the clinic and testing these hypotheses out clinically. So that’s how the company operates on a day-to-day basis. In the context of COVID, we’ve been remarkably lucky, I think. Because of that virtual nature, we do have our own offices because face-to-face contact continues to be very important. We do make a lot of effort to go and see all of the various different groups we work with in terms of trying to build up that project atmosphere.

Peter: So COVID hit us in the way that obviously we couldn’t meet face to face anymore, but we were all very well set up virtually to be able to continue to operate. And we’re very lucky in the sense that it hasn’t really affected any of our timelines this year, apart from things like Zoom fatigue and the usual things I’m sure we’re all suffering from, but we can count ourselves lucky in that. We’ve not had to lay any staff off. The major objectives of the companies overall continue to be met throughout the course of the year, and we’re excited as we move into 2021 in terms of the next phase of the company. 

Grant: Can you talk a little bit about the genesis of Grey Wolf? When you were at Vertex, what led you to jump ship and do your own thing?

Peter: Yeah, it’s a good question. I was pondering this earlier today. I initially came into Vertex as a bench scientist working on actually amyotrophic lateral sclerosis, so motor neuron disease, then moved into more of the oncology space working in immuno-oncology as well as DNA damage repair. Then laterally I was working in rare diseases, which is the often genetically validated diseases, which is the major focus of Vertex now. And through that process, I think I was exposed to a number of other podcasts and avenues that talked about this idea of setting up your own company. I always thought that was something I would like to try in terms of the ability to mold that company’s direction and drive it in the way in which you think it potentially could work.

Peter: Also the idea that a small biotech has that potential to be very nimble, operate very quickly, have very quick and efficient decision-making, and so that had always been percolating over the last couple of years at Vertex. Then Vertex exited oncology in 2017, and there were some interesting ideas in the literature. One of which was ERAP1 that I thought was particularly interesting and worthy of following up, because it was altogether quite different. So the two things really came together, and obviously never set up a company before and it didn’t know how to do it really. I spent my evenings and weekends trying to figure out how you set up a small biotech company. It was during that journey- I think it was in 2018. I was lucky enough to have been introduced to Tom McCarthy who’s the exec chair of Grey Wolf and been very successful in the biotech space, taking a small molecule all the way through to proof of concept in neuropathic pain and then partnering that with Novartis.

Peter: And he was looking for his next challenge in the space, and we decided to join forces. He actually made an initial seed investment in the company, and that’s what allowed me to step out of Vertex, get the company going. Then we essentially became a double act, raising series A or raising seed investment from signatures in addition to the money Tom invested. Then raising series A in April of 2018 and really leveraging all of the excellent contacts and skill sets that Tom has in setting up companies to get the company going, and we’d been working together ever since. 

Grant: Fantastic. What have been your biggest surprises on this journey?

Peter: Probably the biggest surprise is how almost addictive it is running a biotech. As scientists, as we all know, waiting for that next data point is always really exciting. That’s always something that feeds us and is why we do what we do. And then you mix that with the premise from which you decided to go off and set up a company and an ability to finance and get investment into that company. The two come together. Those two worlds come together, and as a result, it’s very hard to switch your brain off because you love what you’re doing. You’re really wanting to push to the next stage of development and investigate whether the idea is a good idea. And if it is, to really try and capitalize on it and push it into patients and ultimately test the hypothesis out.

Peter: So I would say yeah, on a day-to-day basis, that’s great because there’s nothing like loving what you do. The other element is learning. Learning more about my leadership style, learning how to leverage deep expertise across the Grey Wolf team in the various different facets of drug discovery, drug development, and operations to enable us to get the outcomes we want. And that’s certainly a growth within me as I set up the company with Tom and continues to be an active area of development for me, something I think you can always get better at and learn from. So probably those are the two key things.

Grant: That’s great. So Pete, tell us where the name for Grey Wolf came from.

Peter: Yeah, happy to. So as I do enjoy a bit of fly fishing, which I do with my father-in-law. I’ve had many fishless days, unfortunately, but there is one fly that seems to be lucky. It’s called a grey wolf fly. It’s a mayfly mimic, and I thought it was setting up the first biotech company, you know, it’s a bit of a lucky charm, so decided and it was also a bit of a different name. I decided to call Grey Wolf Grey Wolf therapeutics as a result after that fly, although the spelling is different for those who do Google it afterwards.

Grant: That is really interesting. I would not have guessed that. So let’s go back to the very beginning. Where did you grow up? Were you interested in science as a kid? If so, what got you interested in it?

Peter: Yeah. So, I don’t think, during what we call primary school in the UK, I was particularly interested or not interested in science. My grandfather was a keen bird watcher. I do remember being fascinated by birds at a young age and just interested and a bit of a twitcher back in the day, as you say. I think probably my interest in science probably really stemmed in secondary school, and I spent a portion of my time in high school in the US in Atlanta. I had a chemistry teacher, Tony Locke, who was probably really the person who got me very motivated initially. 

Peter: I was probably best at science out of all of the disciplines, and he put it across in a way that I found it quite fascinating and interesting, and ultimately encouraged me to go down the biochemistry route, which is why I then jumped off and did my degree back in the UK. And I think from there, obviously I stepped into the PhD. I fell into doing a PhD in the sense I didn’t really know what else to do, if I’m honest with myself. Then as I started working through that PhD, and as everyone waited till the third before you start generating the specific results or data. It’s two years of no results. That’s when the bug probably started to hit in a way, in the sense of the idea that you’re finding out something entirely novel here that people haven’t really discovered before, and you get to frame the questions that you want to ask. Probably that’s how it started, and then moving into my degree.

Grant: And, on a bit of a side note, what brought your family to Atlanta? 

Peter: So my father worked in the cement business. He’s a chemical engineer by background, always had moved around quite a lot because managing different cement plants and ultimately being responsible for more and more plants, there was an opportunity for him in the States. We actually almost moved to the Philippines before that, but that fell through for a couple of reasons. But then we went, moved to Atlanta and I moved at the age of 15 and came back at the age of 19. 

Grant: Nice. I guess Atlanta is our nearest big city, along with New Orleans. 

Peter: Yes. I guess you’re not too far, are you, actually from Atlanta?

Grant: It’s all relative by American standards. 

Peter: Yeah, exactly. By our standards, this is a long way.

Grant: So what did you do after your PhD? 

Peter: I actually took a bit of a detour. I worked at Wyeth pharmaceuticals in the UK doing something very different, so not research at all. I worked in the regulatory affairs group, working on post marketing. Sorry, post-approval marketing regulatory affairs. I think, probably if I’m honest at the end of the PhD, there’s always ups and downs during that process, and I wanted to explore what else there was potentially out there. I wasn’t really sure of what the next step would be. Yeah, I took that job on, and I think I knew very quickly within the first four months that this was not what I wanted to do. It was really useful and interesting learning about that side of a pharma business and actually probably useful as we move in closer to the development phase of Grey Wolf.

Peter: But yeah, I quickly realized I wanted to get back into research and science, and so from there I went and worked at the MRC in Oxfordshire at the animal unit. I was working on- that’s when I stepped into motor neuron disease, working in a highly collaborative group between the MRC and UCL at the Institute of Neurology and in London. Working at developing novel in vitro and in vivo models of ALS disease was the aim of the project, and so did that for three and a half years. 

Grant: What kind of models?

Peter: Cellular systems, but predominantly it was mouse model systems, and it was using a mutagenesis screen. So the place where I work was a place with Harwell and they used chemical mutagen ENU to essentially drive a whole number of point mutations throughout the mouse genome. Then the job was really to go in and find genes that were interesting in the context of ALS that we knew cause familial forms of the disease. And I try and identify intriguing mutations that have been created through this mutagenesis.

Peter: We found some interesting ones. One gene, we found some in the first gene, so two genes that we know cause the familial forms of the disease. And then the job was to rederive those animals and then phenotype and characterize them, as well as start to understand what are the potential mechanisms by which ALS could be caused in those model systems. The whole premise here was there’s a number of mouse model systems that rely on transgenic overexpression. The fear in this field is always that they could potentially lead to artifactual effects because you’re just driving such high levels of expression. Whereas this way was looking at the endogenous expression of these genes to see. Can you look into a more physiologically relevant environment to see if the pathology is the same as what we see in these other transgenic systems? 

Grant: What about after Harwell? 

Peter: So after Harwell, there was a job advertised at Vertex. I knew I wanted to get back into pharmaceutical research. I really enjoyed the postdoc at MRC, but I knew I wanted to get into that applied research in the pharmaceutical space, and lucky enough that there was a job advertised at Vertex. So Vertex is a Boston based company. They have research sites in Boston and at the time they had one in Canada. They also have a center in San Diego, and they have one in Oxfordshire. And so there was a research site there that was advertising for an ALS scientist to come in and help with the ALS program they were doing. So I applied, got the job, and was working on developing complex in vitro systems initially to try and help with some of the drug discovery programs, as well as driving new target ideas for the ALS programs.

Peter: ALS is a tough area for drug discovery in all honesty, and I think you can see that by the challenging trials and things that are going on. There was a slight refocus, and that’s when I moved into oncology immuno-oncology and DNA damage repair. That site in the UK was really strong in that space, both in IO but also in DNA damage repair, having put the first ATR inhibitor into clinical trials, which continues to do well now. So then, we started learning an awful lot about immuno-oncology, which I found fascinating and worked and led some programs in that space, before then transitioning into rare disease because of the transition of partnering those programs to Merck. Then, I was responsible for helping build the early programs in the rare disease space at the Oxfordshire site and leading some discovery projects there. 

Grant: So looking back across all your experiences and education, what experiences do you think prepared you the most for what you’re doing now and why?

Peter: Good question. I think all scientists go through that period where nothing is really working or you’re faced constantly with negative data. Certainly the first couple of years in my PhD were certainly like that. So I think that level of resilience is really important because you go through a lot of ups and downs in setting up and running a biotech. That is certainly from a personal perspective, it stands you in good stead. I think that also came through- I did a lot of sport growing up, and the same getting used to winning, but more importantly, getting used to losing and being a good loser. No one likes to lose, but I think you’ve got to do it. You gotta get accustomed to it and pick yourself up and get on with it. So, yeah, I think that from a personality perspective is really important. I think that the other key thing that was- it’s a bit of a corny sporting analogy, but it’s also very, very true in drug discovery.

Peter: And I learned quite early on at Vertex was you can never know everything. It’s so highly specialized it’s like being on a basketball team and thinking that you can’t be a point guard, a guard, and a forward. It’s just physically, that’s not possible. So you learn what you can do, but you learn enough about the areas that you can’t do. You know what the questions are you need to ask or where you need to rely on other people. And I think there’s someone stepping out to set up a biotech, particularly in the discovery phase, I was aware of what I didn’t know, what the skill sets I needed to bring in, and that continues now, as we start to move through the development phase. There are people who know more about development than I do. It’s about harnessing everyone’s capability and skills to achieve the outcome, so those are probably two key aspects of spring to mind. 

Grant: And as you build a virtual biotech, how have you balanced what you bring in-house and what you do externally?

Peter: So as we are purely virtual, the emphasis for me initially was trying to build that on the R&D side, was trying to build that project team as would exist in a pharma organization and what we had in Vertex. And so that means you need expertise across each of the scientific disciplines- biology, chemistry, DMPK, safety, toxicology, formulation, CMC at the appropriate points. In the virtual space, you don’t need full time employees across all of those things. You need to have expert people that can fill the various different roles as and when you need them to. A lot of the people we work with work on a consultancy basis, and then we have some more full-time employees or consultants working with us. Then the other aspects, which is really where Kirsty, who’s our chief operating officer, and actually is married to Tom, but has got a deep experience in running tech companies actually over the last 20 years. We needed to be able to operate the company.

Peter: One of the key aspects of having a small biotech was the ability to be nimble, was the ability to decision-make quickly. We make good decisions and if the decisions weren’t good, we can change the decision quickly, and that relies on having an operations area that operates incredibly efficiently. Because you’ve got to go through legal paperwork, you’ve got to go through the financing aspects, all of the investment agreements and everything. They all need to be done efficiently, but to the right level of detail. And so really we lean heavily on Kirsty, and she’s built up that aspect of the organization as we’ve gone along. The two work in parallel, and that allows us to operate efficiently as we go forward.

Grant: Do you think you’ll ever stop? Post Grey Wolf, what do you think you’ll do?

Peter: We’ll probably do it again. It’s the aim. I always really enjoyed time in Vertex coming up with novel or different ways of approaching drug discovery problems or coming up with therapeutic hypotheses that you think worthy of testing and really spending a fair amount of time on that target identification and what is a good target. Stewart Hughes who runs Pathios Therapeutics as a company. I’m also involved with them, we used to work together a lot on that aspect because getting that bit right is so important to everything else that follows.

Peter: And so, yeah, I’ve always got an eye out to the literature and academic groups that we work with and talk to about what could be an interesting possibility of a target idea. Not for prosecuting now, because I think we’ve all got our plates full, but in the future. Because ultimately, it’s not a chore getting out of bed, doing what we do. We’re ultimately trying to make a difference, but at the same time we get to do something that’s very, very interesting. So no, probably I don’t think I’ll probably ever stop.

Grant: Good answer. So, what would your advice be to current industry bench scientists who might be considering doing what you’re doing now? What kinds of skills should they build? What kinds of co-founders should they look for? 

Peter: The number one thing is to be a bit of a sponge and always continue to be a bit of a sponge. You can always do things better. You can always learn. The adage of growth mindset, and whilst it gets overused now, is really, really important. There’s always areas you can be interested in. There’s always things you can find out more about. And it’s not always with a view to you becoming an expert in everything, it’s with a view to understanding how everything fits together. So I would say, yeah, have that level of intrigue and interest. Be curious and absorb as much as you can on the specifics of setting up a company. At least in this iteration of Grey Wolf, one piece of advice I got quite early on from a family member actually was to identify someone who’d been through the drill, been successful because there will be tough times. There will be difficulties. There will be things that don’t go right, and we will know that as scientists, but even on the company and business level, there’s always going to be challenges you’re going to face. So having someone alongside you that has been through all of that before is a great source of help and benefit.

Peter: Tom and Kirsty both have lived that, so that’s a real source of help. A problem shared is a problem halved and all that. So, those are two definitely key things I think you should think about. And then to be honest, the third I would say is the “why”. Make sure you’ve got the “why” right, because there’s times where things are difficult or challenging. You’ve got to really understand why you’re doing what you’re doing. Otherwise, you’ll find it very hard to motivate yourself and get yourself out of any issues and things that you’re doing. You know, I think in our space, the “why” is quite easy. If you’re trying to develop a novel therapy that ultimately is going to help patients, but you can lose track of that at times when things are maybe a bit more challenging or you’re on your 15th Zoom call of the day. But yeah, I think that’s another important thing.

Grant: I guess on that note- you don’t necessarily need to go into specifics if you can’t- but what’s been one of the hardest things that’s happened with Grey Wolf since you started. 

Peter: Probably the hardest thing was, even though I was very, very excited about the next step, the hardest thing was finally saying and actually having those initial conversations with Vertex because it was a company I hugely admired. I had a lot of colleagues there that I really enjoyed working with, and I believe the science was really interesting. So it wasn’t like I was leaving a job that I didn’t enjoy or potentially wouldn’t have a good future in. I had two kids at the time. I have three kids now. At the time, my wife wasn’t working because we just had our second child.

Peter: And so the hardest thing was making the jump and the step, and some people thought I was probably a bit crazy, a bit mad. But it was always something I wanted to do and give it a go. So that was probably the hardest thing from a personal perspective. There’ve been hard things along the way. I think we’ve been very lucky at Grey Wolf. Particularly the ERAP1 project so far has gone quicker than any other project that I’ve been involved in, and the science, for the most part, continues to prove out the idea founded in the literature. So there’s been ups and downs. There always will be, but I think we’ve been quite lucky in the way that’s operated.

Grant: Great. What do you think is some common advice or conventional wisdom that’s wrong or at least that has been wrong in your career? 

Peter: I think probably one thing, and it’s prompted by a book I read recently actually called Range, was in the sector of science, you can get caught up with being a hyperspecialist with really knowing everything there is to know about one thing. And I think that gets drilled into you during your education, really. You get more and more specialized as you go to the point. Though if you start to follow an academic career, you get really stuck down into a niche. And I think the beauty of drug discovery development space is you have to have to lift out of that.

Peter: Although even within that, you’ve got this idea, this notion of a specialist always. You’ve always got this notion of a specialist, and I think in the experience I’ve had today, you no doubt need the specialists. You need them for the particular areas and the particular depth of experience on a particular problem, but you absolutely need the people that range. So people who are able to see the larger picture, can take something from over here and apply it to something all the way over here, which you wouldn’t connect if you’re stuck down in a specialism. And so I think conventional wisdom certainly pushes specialism, but I think we should be also mindful that there’s an equal, if not bigger place for people who can see the bigger picture.

Peter: And I would suggest that that needs to come down further into education in the early phases of education, because you see it with the top academic scientists. Those are the ones who actually are seeing the larger picture and can connect these amazing collaborative networks together and do and ask some pretty fundamental questions because they’ve not gotten down, drilled into this tiny little niche.

Grant: So how do you think we accomplish that? Project based learning? 

Peter: Yeah. Project based learning’s one idea. Broader education for longer, problem solving as opposed to just learning lots of different facts, learning the skills from which to do that. Education’s going that way in a lot of respects. Good question. It is interesting. They talk about the Roger Federer example or the Tiger Woods example, and the Tiger Woods example- being a two year old that picks up a golf club. He was fantastic and was amazing at it, but all he did was play golf. 

Peter: That’s all he did. Roger Federer was pretty good at about four or five sports, apparently up until his teenage years and continues to be a pretty impressive athlete and only really focusing on tennis later on. He exposed himself- or his family exposed him- to multiple different facets for as long as they possibly could until he just chose to specialize.That really does apply to our sector. Those people who are really successful and develop game changing therapies are the ones that can see the bigger picture. 

Grant: So I guess that goes back to being a sponge. What do you think are some of the most effective ways to be a sponge? Some reading on the side on the weekends or trying to get very different work experiences? 

Peter: I think the first thing is figuring out the best way in which you learn. You know, some people learn by reading. Some people learn by talking to people, because that will be the fastest route to which you can be a sponge. You know, obviously we all read the scientists. We have to read publications and everything. I’d probably learn faster by actually having conversations with people and asking questions. I think I was lucky at the time in Vertex because I was able to attend meetings covering a broad range of topics and things. Being a little bit pushy in a nice way to expose yourself to the different facets of whatever sector you are in to broaden your horizons. I also think you can get a bit workaholic. I think giving yourself some downtime, letting your brain reboot, whether that be exercise or whatever it may be, I think is important.

Peter: I think we all talk about it. Certainly there was a period during the first lockdown over here in the UK where I wasn’t getting an awful lot of headspace at all. And then it becomes quite hard to have that “sponge mindset” of trying to absorb and learn lots of things because you’re not giving your head, your brain, the time to download all of the things it’s picked up.

Peter: And you mentioned Pathios earlier. I was just wondering outside of Grey Wolf, what other kinds of things are you involved in and why? 

Peter: Grey Wolf takes up almost all of my time. I do some advisory work with Pathios. I think it’s a really cool idea and interesting idea of targeting macrophage conditioning and modulating the way in which they can go from being immunosuppressive to a more immunostimulatory or pathogenic macrophage. So I’m involved somewhat in that. Outside of that from a work perspective, I’m just really focusing on making sure Grey Wolf delivers, is my primary aim. I think in the future, I’d like to take on some non-exec type roles or, as we were talking about, thinking about what is that next company down the track, but at the moment, yeah, very much focused on the job in hand.

Grant: Makes sense. It’s all consuming. I mentioned this in my email. I don’t know if you’ve had a chance to think about an area in which you think most people are wrong. Or was that the range answer?

Peter: I mean that was what I was thinking with respect to that. There’s this focus on that type of learning. I only think about that with my children now, not just pushing them down one particular avenue, like being a chess champion. It’s exposing them to multiple different things so that they can start to pick and choose what they enjoy and what they’re good at. Often the two tend to coincide. But I did have an episode interestingly during lockdown. I think one of your other questions in the email is what hobbies you have and enjoy. Most sports. I used to play a lot of table tennis growing up because my dad built a table for my old brother in the garage.

Peter: And so I spend most of my weekends doing that. About a year ago we’ve got a garage- or garage, as you’d say in the US- at home and we bought a table tennis table for it. During lockdown, I got quite excited because Will, who’s my oldest, him and I would play probably most evenings, actually. It was a nice way of having a bit of downtime, and he was getting pretty good. He was actually having some very, very good long rallies, but I realized whilst I was really enjoying it, he was trying to not enjoy it so much because he started to get a little bit competitive. I think he got a bit repetitious for him, actually, to be honest, as much as anything else. A part of me is a bit disappointed because you know I love table tennis, could probably play at 10 hours a day every day, but I had to acknowledge that it’s not something that he loves as much as I do quite yet.

Grant: I guess it goes back to letting kids try lots of different things and finding what they like. Hopefully it’s not just some social media. 

Peter: Yeah. Or video games, it’s  constantly video games in our household.

Grant: Cool. Well, hey, thank you so much for joining us today, Peter. It was really great. 

Peter: Thank you. Really enjoyed it. Good to speak to you.

Transcript of Episode 7: Adam Marblestone

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Adam Marblestone. Adam, can you introduce yourself please? 

Adam: Sure. Hi Grant, I’m a Schmidt Futures Innovation fellow working on trying to roadmap and galvanize funding for new medium scale moonshot science projects. And previously I’ve done a bunch of research in a few different fields, many of them connected to neuroscience in some way: a bit on neuroscience-inspired AI, a bit on brain computer interfaces, and earlier some work on molecular biology tools and imaging tools for the brain. 

Grant: Thanks. I’d really like to get to some of your older work, but first let’s start out with FRO’s. What are they?

Adam: The basic observation behind FRO is that there’s a class of problem that is not a great fit either for academia or for startups. The reason why academia struggles with this type of problem is if it requires a certain level of concerted organization and scale of an effort beyond a, let’s say a handful of people tightly working together.

So an example of this is recently a deep DeepMind’s recent work on protein folding, where you have 18 co-first authors on one paper. And that would be very unusual in academia because in the end, everybody has to have their own thesis or their own postdoc or so on. So you do have people in academia who very much want to collaborate and can be very good at it, but from an incentive perspective, it’s hard to get say 20 or 30 people working in a tight-knit engineering organization for let’s say five or seven years to build something, a really complicated integrated system. On the other hand in startups, it is very possible to do that, but by the time you’d be five or seven years, and you really need a very clear path to revenue and product market fit and often for a fundamental, if you want basic science platform, that wouldn’t be a good fit. So FRO’s are simply an attempt to get both the government and philanthropists to fund dedicated nonprofit organizations to tackle this class.

Grant: How do you see this as different from what came before?

Adam: Well, I think there are definitely hints of this, and in some ways we’re just drawing attention to the need for more of this. I would say projects like Janelia Farm Research Campus doing a fly connectome, or the Allen Mouse Brain Atlas, some projects at institutes like the Broad and Sanger Institute.

There are certainly concerted projects, and there’s also examples that are much larger like the Genome Project was maybe $3 billion or so. The way we envisioned focus research organizations is 30 to 50 times smaller than that, roughly. So tens to hundreds of millions, rather than billions. Enough that you would meaningfully change the incentives and organizational structure and path of a field, but not necessarily have to put billions into a single project. So I think that really the observation is just that there’s more need for this type of model than is being satisfied by the existing mechanisms, and that it is possible to systematically go and try to identify what those are and push on them.

Grant: And what fields and projects do you think would be the best candidates for an FRO?

Adam: Well, that’s part of what we’re trying to figure out now. We’re going around to the scientific community pretty broadly and asking people, “what would you do if you were not only not limited by funding, but also not limited by structural affordances?” Imagine that you could take your research program that you want to do and spend four or five years doing that as a FRO with the optimal resources, and then spin off several companies, and then incorporate that back into universities or government labs. So we’re going around pretty widely. I think there are a few different categories of FRO that do emerge from that. One is where you have to build a prototype system that just involves a lot of complicated working parts, but it’s directed toward some kind of basic science problem.

The classic example I always use of this is next gen connectomic brain mapping. So normally with brain mapping, at the finest resolution where you could actually see connections between individual neurons, you are dealing with the electron microscope, which is the highest resolution microscope that there is.

You need that because the way you’re labeling the neurons is you’re just saying any given pixel is either black or white, basically a gray scale. Is this the membrane of the cell, or is this not the membrane of the cell? And then you have to image that very, very finely and then try to reconstruct the shapes and connections of all the neurons from that very high resolution, but at some sense, limited data, which is just membrane or non-members. Now with optical microscopes, you can get much more information out of any given pixel.

You can, first of all, have several colors, but more importantly, you can image the same spot many times because the light is not destructive and you could flow on different chemicals and reagents. So you can get a huge amount of information out of any one spot, but the resolution of optical microscopes is slightly lower.

You can overcome that with new chemistries, like expansion microscopy, which moves the molecules apart from each other, so that they’re more easy to resolve and with fancy microscopes and instrumentation that can boost that a bit. If you wanted to make a brain mapping technology or generally a biological tissue mapping technology based on this, where you can see lots of molecules and adapt this, to be able to actually see neural connections, you just have to put a lot of pieces together.

You have to put multiple types of chemistry, multiple types of biology, multiple types of engineering and instrumentation, and it’s just complex, building out that prototype system. Once you have that, scaling it is also challenging, but it’s a slightly different problem. So that’s an example where you’re building a prototype, and you also see things like that in completely different fields.

You can imagine making the first drill that can drill down into very high temperature rock for fine geothermal energy sources or something like that. It’s a completely different field. Similarly needs to integrate high temperature electronics from NASA with drills from the oil industry and geochemists or things like that.

So that’s a prototyping FRO.I think another category is something that’s more like a foundry where in general, there is some ability to do something already, to produce some kind of artifact already, but it’s not reliable enough and systematic enough that end users have access to it. So this is maybe closer to things that companies would do, but in some cases, the resource that you want to produce should be some kind of public good, and/or it’s deeply pre-commercial in the sense of the application space is not yet known.

So we see this in several areas of nanotechnology or nanofabrication where, for example, with the next generation of 3D integrated chips. Conventional chips, you basically have layers of silicon metal insulator on a flat surface, but what you might want to do in the future is have things like layers of carbon nanotubes, and very deep three-dimensional interconnects between these different layers, so very densely integrated in 3D with lower energy switches. And that’s something that requires you to go beyond the kind of foundry environment that Intel or TSMC or these other companies where you can order chips from already have. So you need to build a foundry for that. At the same time, it’s not clear exactly what the application is yet, because you would have to figure out what kind of software compilers and all sorts of other things you would make for that.

So you actually need a foundry just to be able to discover applications, let alone actually sell something. So that’s an example of FRO for a foundry, and we see that with a bunch of different areas of nanofabrication. Then I think a final category of, although this is not an exhaustive list, would be something like an observatory, which is a little bit more similar to the Human Genome Project, where you just need to collect a data set in a very unified way. Maybe you have existing- 

Grant: Maybe like the Allen Institute. 

Adam: So yes, I think the Allen Institute, many existing institutes, I think do something that’s more like this. They take a relatively mature set of technologies and they just integrate the data. They just create a more unified way of collecting and accessing that data. I see a lot of need for that in some fields of disease biology and in-vivo biology where a lot of the existing institutes that do the data collection at this scale actually are focusing mostly on the normal state.

One example of this would be human brain tissue, doing really deep proteomics, maybe even single cell level proteomics of human brain tissue samples. Another example would be in the field of aging or geroscience. There are a bunch of putative interventions that have emerged, including one that was recently on the cover of Nature, claiming to have ways of basically reversing aspects of the aging process.

And this is pretty amazing, but often different papers and labs in this field measured completely different things when they apply those interventions. So, one lab will have a relatively small number of mice and they’ll measure a few properties, but maybe not the lifespan or maybe not methylation clocks in the blood that are supposed to be predictive of the rate of aging. Another lab will measure the meth methylation clock but not measure cardiac function or something like that. And so how do you have a really unified basis on which to compare and combine these anti-aging type interventions? That would be an example of the kind of observatory I think you could build where you would really systematically look at the phenotypic effects of aging and interventions against aging. 

Grant: Very cool. So, if you could direct a $100 million to a specific FRO today on this podcast, you have to choose in the next minute, what would the topic be? 

Adam: Yeah, it’s hard to choose one. That’s part of why I think we want to create a- 

Grant: Wait, which of your children do you love the most?

Adam: You can’t answer it. I mean, I think that there are different ones that are appealing for different reasons. I think some are appealing because they so clearly fit the FRO category. They’re so clearly not doable in some other way. Others are perhaps more appealing because of the near-term impact that they would have, or the kind of clarity of the path to impact. I think we want to really add to the vocabulary of government and philanthropy that this is a more repeatable model to be used and complimentary to lots of other ones, including the ARPA or DARPA model, which is a bit more discovery oriented and yet also aims for a certain degree of scale or concertedness of efforts.

Personally, you know I have some long term interest in the brain mapping when I mentioned it, but that’s just one example. Yeah. 

Grant: So what would be the ideal outcome of your current position? What would constitute success? 

Adam: Well, I think if we could get philanthropists excited about essentially wanting to own topics this way. So if you have someone who’s really interested in antibiotics, then perhaps in addition to supporting a diverse set of research about antibiotics or investing in companies that have antibiotics near to the markets. The go-to thing would be to say, well, let’s figure out what the FRO would be for accelerating the field of antibiotics discovery.

And then it’s a big ticket item compared to too many things, but for some of the larger scale philanthropists out there, tens of millions of dollars is not a completely unreasonable amount to spend. You might spend that much on having a building or something. How about instead, just nail antibiotics discovery platforms or something like that. That would be on the philanthropic side. On the government side, while there are multiple potential governments, but on the US government side, what we wrote in this white paper that we’re circulating in the policy community through this entity called the Day One Project- day one referring to the first day of a new presidential administration.

The idea there is there’s an entity called the Office of Science and Technology Policy, which is part of the White House, and it helps to coordinate the priorities that the president and the White House have with what the individual agencies like the National Institutes of Health or the Department of Energy are doing.

I think perhaps an ideal outcome on the government side would be the Office of Science and Technology Policy (OSTP) coordinated initiatives where multiple different agencies each have their own. If you want interpretation of how to push more research into these kinds of concerted moonshot projects, you could therefore have FRO’s in multiple domains that way.

Grant: So going way, way back, I want to go to your PhD, but I’m just curious to go back even further. Tell us about your childhood, where you grew up. Were you already interested in science or did that come later? 

Adam: Yeah. So I grew up in Western Massachusetts, and as a young kid I wasn’t necessarily the most scientific. I had some of my elementary school colleagues were much more scientifically advanced than I was.

I was doing a bunch of sports, karate, and gymnastics and horse riding, and eventually got really into gymnastics for a while. But my dad had a telescope. He was an amateur. He was an economist but an amateur astronomer. I got exposed to astronomy that way, so I knew that was cool. I had supportive parents that would buy books and stuff for me.

So I’d sometimes end up in the Barnes and Noble. I don’t know what you have local to you, but when I was a kid, the Barnes and Noble bookstore was where you would go. I sort of use it as a library cause you could basically read stuff that was on the shelf, so I would go to the science section of Barnes and Noble, which presumably was a consequence of my dad helping me get excited about both stars, galaxies, and robots or so on. So that seemed like the natural place to go, and eventually in doing that, I stumbled across books that got me interested enough. One was about nanotechnology and introduced me to the idea that you could have general purpose technologies that would very broadly impact multiple key areas of human life.

Grant: Do you remember the title of the book?

Adam: Oh, that was Drexler’s book called Engines of Creation. 

Grant: That was one of my formative books too, actually.

Adam: Wow. Yeah. It’s funny how it doesn’t necessarily have to be a successful academic field for something to be absolutely transformational for generations of people.

So that was one, there was another popular science book at the time that I was a kid called The Elegant Universe by Brian Green, which was about string theory and cosmology. So between those two, I knew that there was a lot of cool things you could do with biotech and a component to getting at that through physics, and so that set my path. So eventually gymnastics was taking too much time away from my reading physics books, and I eventually got to the Feynman books and stuff. Once gymnastics was in the way of the physics books, then it was clear what the choice was.

Grant: Great, and maybe we can jump forward several years. Tell us about what you did during your PhD. 

Adam: Well, PhD was a pretty crazy ride because I had a high freedom graduate program called the biophysics program at Harvard, which was really a kind of wild card where you could basically work in any department or lab in the broader biomedical sphere at both Harvard and MIT. It’s kind of an underappreciated program in how much freedom it gives you.

And then I ended up with a very high freedom advisor, George Church, who is first of all, very creative and open-minded, but second of all has, I don’t know, fifty to a hundred people in the lab at any given time. I had a lot of freedom there, and I also ended up with a high freedom fellowship. So I really did have this free phrase. Enough rope to hang yourself, and I definitely had that many times over. As a result, basically as a PhD student, I was trying to pursue somewhat perhaps unrealistic moonshot projects, very much of the nature of what we’re now trying to do with FRO’s, and it was only along the way that we managed to publish a few kind of basic papers to actually do the PhD.

But I started out interested mostly in biomolecular self-assembly and trying to make the so-called DNA origami structures, which self-assemble in a programmable way, I wanted to make those much bigger so that we could integrate those onto silicon chips and make a kind of bio chip that would have nanometer to centimeter levels of control all the way in the single platform.

With that one, I think we made a little bit of progress. We did make some unusually large DNA origami, but I also realized somewhere in the middle of that I didn’t have a very clear path of what the applications would really be of that. So midway through, I sort of switched to neuroscience and had also a very unambitious goal of trying to record simultaneously all neurons in the mouse brain.

Which in the Church lab, which was mostly focused on DNA based technologies, the natural way to think about doing that was to try to record neural activity into DNA molecules inside your cell, so we did a lot of ideation and kind of team building around that. A little bit of preliminary experiments, where we were exploring whether you can get these DNA polymerases which copy DNA to encode information about the ion concentrations in their environment, which would be reflective of neural activity, into patterns of errors in copying DNA. And then we sort of branched out from there, both to collaborating with a number of other labs on applications of DNA barcoding and DNA encoding to structural brain mapping, and on trying to understand more from a physics perspective a number of different ways you could record neural activity and super large scale. Including but not limited to these kinds of molecular methods, so that was a pretty weird PhD. 

But it definitely was the breeding ground for thinking about these kinds of focus research organizations and what would it actually take to properly execute on some of those ideas that as a grad student, I was nowhere close to being able to do on my own or with the few people I was collaborating with.

Grant: And somehow in the middle of all that you co-founded BioBright. 

Adam: Yeah. Well,  I met another person who was interning in the Church lab at the time named Charles Fracchia, and one way to put it, maybe unflatteringly to both of us, is that neither of us were that great at wet lab experimentation. Charles was maybe a little better than I was. He had had an undergrad in biology and mine was in theoretical physics, but we were interested in whether there was a way to make it easier to keep track of what was going on in your wet lab experiments, and there was a popular and exciting notion just emerging around that same time of cloud labs.

What if you were to basically have a web interface to an external lab that would do the experiments for you with companies like Emerald and Transcriptic popping up, and our feeling was that that was part of the story. But the other part of the story would be, how do you augment the scientist in situ in their own lab and make it easier for them to take notes about what’s happening and to compare what happened in the experiment to what the protocol should have been, or even simply to gather and centralize data from all the different experiments and equipment in the lab into a central way of looking at what was happening.

So eventually Charles did some more work on this in the MIT media lab as a master’s student, and then went off and became CEO of his company. I was a co-founder and helped it get some grant funding and recruit some initial people, but Charles has really been leading the charge on that.

And that has just evolved a lot by interactions with customers and contracts and understanding what different commercial entities need in terms of tracking and analysis of their experiments. But that’s also been a really interesting thing to see happen. Yeah.

Grant: And so after you finished your PhD, you went to work with Ed Boyden at MIT. Can you tell us about what you did there? 

Adam: I developed a lot of ideas around these issues of large-scale brain mapping in the PhD, as I mentioned. The idea there was that teaming up with Ed and others, we could increase the scale beyond individual experiments that I was doing. And so how can we launch larger initiatives and projects?

It’s just seeing the line from where I was towards doing focus research organizations. This is where we started to think about how to move in that direction. So a lot of the work I was doing with Ed, I wasn’t doing experiments in the lab, but I was doing a lot of grant writing and a lot of coordinating of teams and developing strategies and collaborations to try to particularly develop some of these next generation structural brain mapping methods.

The main thing that was exciting at the time and continues to be really exciting to me is in 2014 or so between the Church lab and Tony Zador’s lab at Cold Spring Harbor which had been leading the development of these so-called DNA barcodes for identifying individual neurons in the brain. With Ed having some input into that as well, we were thinking about how do you combine all these pieces together into an ultimate brain mapping technology for the structural and molecular end of brain mapping, not for the living state.

It seemed really promising, but there was this one missing piece, which is that you have to really fancy microscopy in order to make this work. You’d have to slice the tissue really thin, and it was just challenging. And the church lab had just come out with this idea of fluorescent in-situ sequencing which was kind of working, but it was a bit hard to get it working in actual intact slices of tissue, as opposed to just cultured cells. The tissue processing and the microscopy were just hard. But Ed at the time took me into his office and said, “You know, we have this cool thing that we’ve been working on, which is a new way of doing microscopy that solves all the problems that you’ve identified.”

And I was like, wow, that’s pretty crazy, and this was this expansion microscopy thing where you physically swell the brain tissue with the hydro gel to move the molecules apart from each other in an isotropic uniform fashion, 

Grant: Like everyone in the neuroscience world did a journal club on that.

Adam: So what we were trying to launch, maybe we were a little ahead of ourselves, we were trying to launch big initiatives to move this into really developing a strategy and workflow for doing a very integrated form of brain mapping. In practice, it took several years just to be able to get any kind of grants at all about this, for example because it just seems so crazy to people. Even though they had done a really great job and validating this, so it took some time just to gain basic acceptance by the community, and what we ended up accomplishing and Ed’s lab and collaborators with me kind of helping coordinate a bit of it.

What they ended up accomplishing is really demonstrating a bunch of different ways in which this can be used, including so-called double expansion or iterative expansion, where you can expand twice and get twentyfold expansion instead of fourfold expansion. This integration of the chemistry of the in-situ sequencing, or physique, so-called multiplexing methods with the expansion, which is now in a pre-printed bioRxiv and going to be published pretty soon, expansion microscopy where you’re staining the lipids much in the way you would stain with the electron microscope to make it more relevant for connectomic brain mapping in the more traditional sense.

We also just pushed forward a bunch of other ideas, including a still-theoretical approach to how you do single molecule protein sequencing, a bunch of things that sort of related to microscopy and molecular multiplexing, and it was a really amazing time with lots of people. Just seeing a lot of possibilities emerge.

Grant: And after that you went to work with Brian Johnson at Kernel, yeah? 

Adam: Right. Seeing the difficulty at the time of having anything quite close to a focus research organization for this brain mapping, I decided that maybe my best bet would be to use that same strategic roadmapping approach that I’ve applied to these different areas and try to push it in the context where we really did have a very scalable team that was commercial driven, but also thinking long-term because Brian Johnson had been making a big commitment of his own funds in his own time to run Kernel.

And at the same time Elon Musk was starting Neurolink. I had some discussions with those people as well, and there was kind of this burgeoning brain interface mini industrial boom around late 2016. All of a sudden people are like, we should start gigantic Brayton computer interface companies.

Whereas they hadn’t been saying that before weirdly. So Kernel was a great experience, and we basically went through and tried to figure out all the possible things that Kernel could do, ranging from deeply invasive medical devices to super next gen physics of how you would do noninvasive things, and then sort of met in the middle with what they’re doing now, which is a set of relatively practical but still quite new approaches to non-invasive brain activity, mapping headsets basically. They recently released a headset that does what’s called functional near-infrared spectroscopy, fNIRS, which is an optical way of measuring brain activity.

They’ve basically released a much faster, cheaper, better, more portable fNIRS headset and are starting to give that to a bunch of collaborators to discover what can you actually do with that. Can you decode speech? Can you decode mental imagery? Can you use it to help train AI? Can you use it to detect if a patient with a coma is conscious? A bunch of different things.

If you have more accessible imaging technology that you could potentially do and yeah, it’s super exciting to see that progress. 

Grant: And then you went to DeepMind. I don’t know how much you can tell us about that. 

Adam: Yeah, yeah, DeepMind was cool. I was on the neuroscience team. That was a really great experience, basically at the point where Kernel identified its product direction.

My crazy scientific roadmapping I think was a little bit less relevant. I chose to let them push on the commercial execution of that and the engineering of that, which is not really my strength, but it’s definitely the strength of some other people they have there and scratch my AI itch. Because in all of this time, thinking about how we could map brain circuits or brain activity, I had of course been reading a huge amount about the neuroscience literature and what do people think these things actually do? What’s the actual functional interpretation of brain circuits? So that’s what I tried to learn about in the time that I spent at DeepMind, which was amazing. I was on the neuroscience team there, and we explored a bunch of ideas, a lot of them having to do with how memory works. They’re on the AI side. It’s not directly possible to connect it to circuits yet. You know, so there’s nothing we can say, well, okay, we mapped this thing over here with in-situ sequencing or electromicroscopy, and then here’s the AI algorithm. Although we I spent a lot of time learning about exactly how close we are or are not to that in different systems.

And there are some systems where it’s much closer, like the way that the songbird does reinforcement learning for learning how to sing its song. That is something where we have both an algorithm understanding and a circuit understanding. Some of the higher level questions about how to get memory to work have a flexible way of accessing working memory and short-term memory in much the way we think about thinking is sort of “I remembered this and I combined this idea with that idea in some compositional way.” It seems to rely on having memory buffers basically, and we were thinking a lot about how that works in a way that’s inspired by what we know about neuroscience, but it’s still a little bit loose. But it was really cool.

I got to learn about how AI researchers think and write a bunch of AI code to try to test out these types of models of how memory might work. It’s a completely different perspective than conventional circuit neuroscience, but I think that I remain super optimistic actually, that these things are going to converge. But of course it might take some time. Yeah. 

Grant: So what areas of science and technology are you most excited about and do you expect might have the highest likelihood of affecting a major transformation to people’s lives and in society?

Adam: Lots of them, lots of them. Yeah. I’m excited about a number. I would say that with my initial push on nanotechnology as a teenager, right.

As I was mentioning, I still want to see a path to make that work. I see that still as having sort of fallen behind biotech. Lots of things we want to do with the material world we can do either with chemistry or with biology right now. And so general purpose atomically precise fabrication I think is one of these things that is really great, but it has a limited foothold in what the research community is doing right now to actually bootstrap that.

So in terms of things that seem on the cusp of really exciting developments and that I’m particularly interested in, looking at with focus research organizations,I would point to a few. One is aging and age-related diseases, and I think the aging field is really starting to take off. You can always debate this.

Have we actually understood anything fundamental about how aging happens and until you see results in humans, extending a mouse lifespan could mean something completely different. You know, mice die for very different reasons than humans do. Mice apparently mostly die from getting cancer. There’s not as much let’s say heart disease or various other things or Alzheimer’s or things like that, but I’m really interested in the idea that we can, including with animal models, identify common roots fundamental drivers of age-related diseases. 

I’m really interested also in neuropsychiatric diseases where many pharma companies have basically killed their neuro R&D divisions, but I’m optimistic that some of the brain mapping and proteomics and related types of technologies we’ve been thinking about now for a years, can circle back and tell you more mechanistic insights with what’s going on with brain diseases.

I’m weirdly really interested in geothermal energy these days, as a possibly underappreciated source of clean energy that requires its own moonshot to figure out how to drill deep in the earth. 

I’m interested a bit in applications of AI to social technologies and discourse. Can you make recommender systems that would be more genuinely helpful to people rather than just recommending what you will be most likely to click on? Can you recommend what will be most helpful to you as judged a month later or a year later? Or can you use that to sort of do fact checking or improve people’s reasoning ability with all these new AI based natural language and prediction technologies? So, I mean, I’m just excited about so many things and that’s, that’s why I’m trying to focus on organizational enablement right now. It partly means I don’t have to choose one. 

Grant: So since you’ve been investigating these areas, can you maybe give kind of the take home points of your understanding of where the aging field is today? What we know, what we don’t know, what maybe we think we know that we may not actually know. 

Adam: Hmm. I can try, but that’s a hard one, but I can try.

There was maybe a basis many decades ago for things to be kind of exciting just in the realization that different species age at very different rates, you have things like naked mole rats that seem somewhat similar to other rodents, and yet live much longer.

Things started to get exciting when people like Cynthia Kenyon were doing genetic screens in C elegans where they could pass for low lifespan and they could identify genes in a somewhat random way that would have a dramatic effect on the sea elegance lifespan, empirically. 

And then that started to hone in on metabolic regulation of how much we are doing something like repair versus how much we are just consuming as much as we can and growing as fast as we can. So sort of growth versus repair lead to the discovery of rapamycin and Metformin and stuff sort of emerge on that axis.

That was one stage. And then I think there’s been a more recent stage that in some significant part comes out of both this revolution of induced pluripotent STEM cells and the Yamanaka Nobel prize for work that I think was done around 2006 era whereby you can by dumping a few transcription factors on differentiated and aged cells, you can “reprogram” them back to a polypotent state, but also one in which a number of aspects of their physiology seem to revert back to a younger configuration. For cells in a dish, this was already exciting. 

One of the things that they measured in that Yamanaka paper was DNA methylation. And they said, look we know that as cells differentiate their methylation patterns change, and this procedure has actually reversed that methylation change. Perhaps, partly as a result of that, or just as a result of the growth of DNA array technologies and sequencing technologies, others have started to develop these ideas of epigenetic clocks that measure aspects of the rate of aging, that seemed to be somehow related to this differentiation versus reprogramming spectrum.

In that same era, there was also the discovery that if you want non-cell autonomous or sort of circulating factors that operate in the blood–this is not clear how exactly it relates to this metabolic access of the first Kenyon kind of discoveries–but in some as yet not very well understood way seems to restore the kind of regenerative proliferative capacity of cells through circulating factors. For example, famously if you take the blood from young mice and put it into old mice.

But what is really going on there? And just in the past year or so, there’ve been a number of advances along that, including one from UC Berkeley, where they simply rather than put blood of young mice, they simply dilute the blood of old mice. And they just put back one protein called albumin and saline. And that seems to have some of the same effects, although this is just a sort of preliminary study.  

And another paper where they have an as yet undisclosed fraction of a young blood proteome. They put that into old rats and they measure a bunch of physiology. They measure methylation clocks and things like that, and they see a bunch of seemingly kind of coordinated effects from these circulating factors. 

And meanwhile, all of these lines have proliferated and developed, and there’ve been a number of different compounds discovered that have some kind of effect in enhancing the re regeneration or blocking cellular killing senescent cells, understanding the interactions between how things like senescent cells drive systemic inflammation and how inflammation can in turn cause a bunch of other aspects of aging related problems like atherosclerotic heart disease and things like that. 

So I think there’s a lot of progress in understanding the physiology of different levers. It’s still not a unified picture where you can completely say, these are the core drivers versus these are the effects.

And there’s a so-called set of hallmarks of aging and, at the same time, there was this other paper about the pillars of aging, each of which I think identified eight or nine different underlying features. But none of those are completely understood. Is that really the right list of seven? Could you instead parcellate that in a different way that would reflect cause and effect more closely?

That’s still not really known, but I think it’s a great time to push towards these concerted FRO-style moonshots to really measure everything about what’s happening in those processes. You can never really measure everything, but to have a really systematic kind of genome project style attack on, on understanding how each one of these levers affects all of the others for more data-driven hallmarks of aging.

Grant: It’s really exciting. I love the optimism here. I’m a big fan of that. I was wondering if before we wrap up, if we could change tack a bit, and as you think about these various scientific and technological moonshots, what do you think is the greatest, realistic dystopian threat that could come out of this? And what can we do to mitigate it?

Adam: Yeah. I spent some time. I’m not really an expert on this, but I spent some time trying to ascertain what people are thinking about more versus less in these areas. I think sometimes there’s a lot of emphasis on risks from artificial intelligence. And in particular, sometimes people say that if you do neuro-inspired AI, that would actually be even more risky because if you sort of try to copy the brain for AI, it will be harder to prove theorems about that and basically prove bounds of sort of controllability or correctness because you never derived it from math in the first place. You’re just taking heuristic hints and then seeing what happens. 

I’m not sure I agree with that. I think that potentially, the brain may have some understandable, relatively unified set of learning principles that it uses. And that if we actually understand that better, we may be able to design AI safety or alignment mechanisms that are actually just more reflective of how the algorithm actually works. It doesn’t have to be totally inscrutable.

I also think that there’s potentially some software engineering ways that you can make really capable AI systems that are not agents in the sense of optimizing some single objective function that you’re worried about getting out of control, but rather just a very tightly sort of supervised sets of processes that are more like Microsoft word than they are a person. I think there’s a lot to be understood there, broadening out the research in AI safety. 

I generally feel that the obvious one is pandemics both natural and engineered. And I think we need to have ubiquitous DNA sequencing everywhere. Apparently it’s actually possible to see so-called viral chatter. So if, if a virus is starting to make its way to the point where it could start spreading in the human population, before that happens, you would start to see in patient samples, just a little bit of an uptake of this virus. So even if you aren’t going and sequencing the rivers or something like that, if you’re just sequencing people that come in for primary care visits or physicals or whatever, if you’re just sequencing the population, it should be able to track the emergence of viral pathogens. 

But I don’t think we have anything like that in place, let alone, globally to be able to anticipate. But we should also have something like ready-made vaccine candidates for each of the 20 or so major categories of viruses, so that you really know whether an mRNA or a peptide or what is going to work and maybe you have to customize it a little bit. 

So I think that we are still very under-prepared for biological risks and hazards. And then I think we’re very sociologically at risk of mass scale misunderstanding and vindictiveness. As we see playing out on the internet and I hope that particularly AI technologies can be used to actually sort of mitigate some of that rather than amplify it. But right now it might be on balance just by optimizing for attention or engagement. It might be on balance actually increasing the infighting of humans that need to be focused on moving forward as a species. 

Grant: Yeah, I think that’s still overall, relatively optimistic answered a dystopian question. So unfortunately we’re out of time. I wish we had more. 

Adam: Thanks a lot for this very wide and thought provoking discussion Grant.

Grant: Yeah. I really appreciate you coming on. Thanks Adam.

Transcript of Episode 6: Tony Altar

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

Tony: Hi Grant. Yeah, it’s great to be here on the podcast. My name is Tony Altar. I have a PhD degree in neuroscience. I’ve spent the major part of my career working on neuropharmacology, neuroscience and helping to develop drugs for people with psychiatric conditions. More recently, I’ve been involved in the neurological side of brain disorders. And as a result, I’ve moved my career more from the pharmacological approach to gene therapy and genetic approaches, because I think neurological disorders are more caused by genetic problems and problems at the early expression of genes, as opposed to drug mechanisms for psychiatric disorders. So I’ve really moved my career quite a bit, just in the last 10 years.

Grant: Great. Let’s talk about your career. Maybe start from the beginning because I think it’s a pretty interesting path, but maybe if we can go way back to child Tony, what kinds of things were you into? How did you end up in science? What possibilities did you entertain?

Tony: I love that question, Grant, because I think for all of us, understanding the origins of our own careers, our own interests are really informative and important. I think those are some of the enduring parts of our personality that help us through the tough times, as well as the good times in our career. So for me, that’s true as well. My interest in science started with my own father who was a fairly successful theoretical physicist, and extended to- I guess I would call him my second father- a man named Art Yuwiler, who ran a neurobiochemistry laboratory. Which, at the age of 17 when I started working there, I don’t think I could even pronounce neurobiochemistry, nor did I really understand much of what these guys in the lab were saying, but it was a fascinating experience. Between my upbringing from my mother and father’s side and love of science and then the ability to actually conduct science at such an early age, that helps set a path for me.

Grant: Where did you grow up?

Tony: I grew up in West Los Angeles. enjoyed the area near UCLA, near the ocean. It was a great place to be. We moved there in the late fifties and lived there. I did my schooling at UCLA, UC Santa Barbara, UC Irvine. So a good dose of California through my entire education.

Grant: What pulled you away from California in the end?

Tony: Well, having luckily married my wife Kristen, I needed to start paying the bills. I also did want to work in the pharmacology industry, especially helping discover and develop drugs for the CNS. So I moved to the East coast to come work at a company called Ciba-Geigy. Now it’s known as Novartis, so that was a great opportunity after my postdoc at UC Irvine to really put into play the creative process that I always dreamed about doing. In fact, it was back in Dr. Yuwiler’s laboratory where I had a small desk, and right next to my desk, there was a little placard from Upjohn Pharmaceuticals and it said, “The best way to create new therapies for brain disorders is to understand the basis of those brain disorders.” And that always stayed with me. Understanding the cause of disease, if you know that you can actually have a real opportunity to come up with a new approach and come up with therapies. And I believe that today more than ever.

Grant: What got you into the brain?

Tony: I got interested in the brain- actually another figure in my life, a good friend of mine Doc Renneker, who is now a very famous surfing guy, Mark Renneker and I used to skateboard every weekend at UCLA. That’s where we got pretty good at skateboarding in the very earliest days of the sport. Mark’s father was a psychiatrist, and we used to chat about the brain and his patients to some extent. I was always fascinated about the whole possibility that you could help people with psychiatric problems by talking with them and by giving them drugs. There was also around that time in the middle of the late sixties, as you can imagine with the psychedelic revolution that came along.

Tony: We got really interested in this whole idea that a single molecule completely changes someone’s personality. It could completely change their insights into the world. To me, that hasn’t gone away as an interest. It’s coming full circle now. So that was really interesting to me because I realized that a single molecule like an LSD or psilocybin can have such profound effects when given as a pill, then our own neurochemistry when subtly changed could explain things like depression, like schizophrenia. So that was kind of an epiphany that I added up at a pretty early age, and working in the neuro biochemistry lab reinforced that thinking as well. So I think that was part of it, the cultural milieu of Los Angeles at that time and the whole country, really. Being able to address it from a scientific perspective and then use it, those were all great recipes for my career.

Grant: So you went into biotech, and one thing I’ve always found interesting about your career, Tony, is you spent almost your entire career postdoc in industry, but you’ve published like a successful academic. How have you done that? How have you managed to keep that up? 

Tony: It’s an interesting question because the general idea is if someone’s in academia, they have to publish or perish, and if they’re in a drug company, they don’t want to tell anybody anything about what they’re doing. I was somehow able to do both. I’m really not sure the answer to that, because I did a lot of publishing at the university of California. I think I published three or four papers before I even got my PhD, but the bulk of my publications was at places like Genentech, Regeneron, out of Ciba-Geigy. Actually, every company I’ve ever worked at. I don’t know, maybe they were a little more permissive for us, but at most of those companies, there was also a high priority placed on excellent science. Publications showing the world what was being done, that was a great recruiting tool.

Tony: I think the companies I worked in always put a high premium on that kind of academic excellence as well as developing compounds. So maybe part of it, to answer your question, was being able to be at places that fostered that kind of environment. The resources at those companies to this very day were always very good. Yeah. We had lots of opportunities to do the work and didn’t have to worry about writing grants. We could spend our time writing papers and stuff. That’s another reason I was always attracted to the pharmaceutical industry, because you really did have that emphasis on research and production rather than scrounging for money and trying to placate reviewers and play the academic game. I was pretty turned off about by the time I was done with my postdoc.

Grant: So how can one as a founder and/or manager create an environment that’s conducive to that?

Tony: That’s a very important question, and I’m faced with that now as I start my own new company with a co-founder. A company that’s focused on gene therapy that we’re really very excited about. This is an important question. You want to attract the best and the brightest, which was the mantra at Genentech and other companies I’ve been at. It was really important to hire the best people, but you have to retain them, too. We have to like our boss. That’s the number one factor for why people leave. They don’t get along with their boss. They need to have a scientific career that’s successful and really productive. That’s another reason that you retain people, because they love the environment they’re in and make great results and are rewarded with publications. You have to see an end product in a company.

Tony: Most people that work in companies are there for some of those reasons I mentioned. They have resources, the pay is good, the environment can be outstanding with other colleagues, but we’re also there because we want to make a difference for human health. We want to really create something like a pill that makes a schizophrenic patient better, or a gene therapy product that helps with a neurological decision, or a pharmacogenomic product that gets patients on the right medications for their ADHD. I’ve been allowed to succeed in all of those spheres. So the best way to retain people has a lot to do with how you recruit them because the people coming into a company see the people who were there. A big part of the decision is to the boss but also the colleagues. Who am I going to work with in this new environment?

Tony: If they’re excited, they’re doing high quality work, and they’re hitting all of those criteria I just mentioned, those interviews are going to go great. I remember an interview I had at Regeneron when I decided to go there and work with Len Schleifer, George Yancopoulos, and the team. The best part of that whole interview process was we went out to dinner one night and there were ten people who came to dinner. We had this fantastic discussion around a big table about all the science that was going on, things I’d been doing at Genentech at the time. I think after that dinner, I pretty much decided I’m coming here. So you recruit with the people who are there, you retain by allowing the new people who’ve just come in to also succeed, and you have a high ethical standard.

Tony: You run a company where there’s no smoke and mirrors, where there’s real results, where the science is first rate, and you can publish in the best journals. As long as you can manage scientists to achieve that way, you’re a good scientific manager. And the rest will just fall.

My expression for managing scientists is you manage by letting them do good science, and then the other problems, which will always arise- competition, maybe some jealousies that come along, frictions that develop, uncertainties. These always will come up in any lab environment, but as long as the results are these high level achievements, everybody will benefit. One thing to really pay attention to here is that- and in retrospect, I’ve become very aware of this- when you look back at the companies you’ve been working at, or universities too, it’s the team you worked with and the treatments you got as a team that you talk about.

Tony: I really don’t talk much about my own individual accomplishments. I talk about what we did at Otsuka and discovering and developing Abilify as a huge team. Eventually, it was a hundred people along with another company, Bristol Myers Squibb, and more than a hundred to do the clinical work. We as a team developed this best in class drug for schizophrenia, depression, and bipolar disorder. No one person does that, but it’s the team that you’re working with. It really makes the difference. I do believe in that. The best management and the best way to retain people is you encourage that team feeling. You have the team succeed.

Grant: I don’t know if you’re allowed to talk about that, but can you talk about the development of Abilify and what the thinking was around that? How it went down?

Tony: Sure. Oh, I’m allowed to talk because it’s mostly been published. And in fact, Otsuka is another example of an outstanding company. They’re a Japanese pharmaceutical company. It really started back in the 1980s when I was working along with some other people on trying to come up with a partial dopamine D2 agonist. D2, meaning a dopamine D2-type of receptor. The theory was actually promoted by Dr. Arvid Carlsson. Arvid won the Nobel prize in 2000 because he discovered dopamine. Not a shabby thing. Leading to theories about schizophrenia and Parkinson’s disease, and Arvid proposed very early that perhaps instead of blocking a dopamine receptor to treat schizophrenia, which was already known, you could actually put in a partial agonist, which didn’t block the receptor, but it quieted the receptors activity to an intermediate level.

Tony: Whereas a receptor blocker will stop all activity. That’ll treat schizophrenia, but it also creates lots of side effects. So Dr. Carlsson proposed that a partial dopamine D2 receptor agonist would lower excessive tone, but bring lower tone up to a sort of a middle level. I worked on that in the early 1980s, and at Ciba-Geigy, we brought some drugs to the clinic. Unfortunately, those drugs have some liver metabolism problems and the company was really quite conservative and didn’t pursue it any further than that. So when I got the opportunity to interview at Otsuka to head up their global neuroscience department, I looked at this early clinical data and some of the ideas about the mechanism, I realized this was very similar to the work I’d already done.

Tony: But now we were 15 years later. I was able to take that job and manage the team in Japan and in the US. We further profiled the compound. One of the important things about Abilify is, turns out, it’s not only a partial dopamine receptor agonist. We discovered that it’s a partial serotonin agonist as well. We realized it could not only treat schizophrenia, but depression and bipolar disorder because of what Arvid Carlsson, who now was one of our consultants, called a serotonin-dopamine system stabilizer. I thought that was a brilliant concept. That went on to be a marketing tool to describe how Abilify works. There are ten times more people with depression than schizophrenia and two to three times more people with bipolar than schizophrenia. The result was, Abilify was the largest grossing drug worldwide in 2014, and I think to this day has treated millions and millions of patients. I’ve been blessed that I was able to work with Dr. Carlsson on this project and with this fantastic team, both at Otsuka and Bristol Myers Squibb to get this really important drug made. All this is publicly known. There’ve been many imitators since.

Grant: Yeah, that’s a huge accomplishment.

Tony: Well, there’s an important term here for that accomplishment and it’s persistence. Persistence is probably the most important single attribute that we as scientists have to embody, because we’re not going to succeed the first time. If we persist, however, it will eventually succeed. Sometimes we have to come full circle. So I had to go from my work at Ciba-Geigy to coming back to a new company to finally get that project finished. That’s a really important thing, is to persist.

Grant: So what, what most excites you in the field of neuropsychiatry? What do you think will have the biggest impact for patients in the next 20 years?

Tony: I think for psychiatry, it’s maybe a little harder to say than for neurology, where I was hoping you were going with the question. In psychiatry, the reason I’m a little more hesitant is because a lot of our psychiatric knowledge has come from how drugs work and which drugs work. By luck, we happened to come across an antidepressant and that we elaborate on those kinds of drugs without really understanding why people are depressed in the first place. The targets in psychiatry are not as clear. There have been some significant breakthroughs with a class of drugs called muscarinic agonists, which for schizophrenia have been proven now to be very effective.

Tony: That may only be the second receptor target that has never been validated for treating schizophrenia. Luckily in the early 2005, my team at a company called Psychiatric Genomics discovered that very approach. It’s been finally capitalized by a company called Karuna. They made a drug that is actually a muscarinic agonist that’s coupled with another drug, and that has proven to be very effective against schizophrenia.

Tony: That may be only the second target after dopamine receptors to treat schizophrenia. I’ve luckily been involved in both the partial dopamine agonist innovation and now maybe this muscarinic agonist, if indeed further trials continue to show that it’s so effective. I think those are the most critical approaches, perhaps one of the most important breakthroughs in psychiatry today, because I don’t think it’s only going to be useful in schizophrenia. I think it may also be useful for Alzheimer’s disease because a very similar story about muscarinic receptor sub activation has been made for Alzheimer’s disease as well. So it could well be that this very same drug, which was made originally at Eli Lilly, could be useful in treating Alzheimer’s disease. Of course, that would be a huge breakthrough.

Grant: What if you were to answer the same question for neurology? Would it be the same answer with the muscarinic receptor?

Tony: Well, maybe for Alzheimer’s it actually could. I mean, Alzheimer’s patients do have a deficiency of muscarinic receptors, signaling, and metabolism of brain neurons as a result. So that could be, but I don’t think that’s going to be the final answer because I think that kind of approach treats some of the symptoms of the disease but not the cause. Therein lies the real difference between neurology and psychiatry. So for neurology, I think a lot of these causes are at the epigenetic level. We know that’s true for many disorders. We know that for Alzheimer’s, we know it about ALS, we know it for Parkinson’s disease, we know it from Huntington’s disease, and the list goes on and on. For all of these diseases, there’s always a subgroup or often a subgroup of patients for whom the genetic mutation that they inherited is the cause of the disorder.

Tony: You don’t see that in psychiatry. That there’s very few of any chains that have been linked to psychiatric conditions, let alone with the penetrance that you see for the neurological mutations. We have a huge opportunity now in neurology to know that at least for a subset of patients- sometimes it’s all of them like Huntington’s disease- where there’s a genetic mutation that is the cause. We know the targets, which is quite different to what you have in psychiatry where we hardly have a clue about what the target is. So that’s where I see it as a huge advantage. Because it’s a genetic problem, the nucleus and the gene expression of nuclear DNA is often the cause of why those patients go on to develop these neurological conditions.

Tony: I think for neurological conditions, gene therapy is a much more viable option, and we’ve already seen that proven in just the last few years with the approval of Spinraza for spinal muscular atrophy and Luxturna for a form of blindness, both of which are due to inherited mutations in genes important for those systems that are diseased.

Grant: Cool. Shifting gears a bit, can you tell us about your skateboarding?

Tony: Ah, skateboarding. Sure.

Grant: When did you get into it?

Tony: Well, I was very lucky. I got into it very early on, and I mentioned I got all of my training in Los Angeles. I went through the Los Angeles Unified School District System at places like Paul Revere Junior High School and Palisades High. I was involved from the very earliest days. Some of my buddies like John Fries, there was a first national skateboard champion. And the year after John, I was able to compete in the same tournament. I did pretty well. Part of that is because we lived in West LA where, I think as far as we can tell, the sport originated. If it didn’t certainly originate there, we kind of did a lot of the improvements. So for me, skateboarding is- today it’s a way for me to keep my brain in shape. I don’t do it just for the fun of it. It’s a great aerobic sport. I know it requires a lot of balance, coordination, persistence. It’s tiring to do it right.

Tony: It’s a full workout. I partly do it for that. There’s a lot of similarities with science and what we did in the early days. We innovated, we had to make our own skateboards where we would cut roller skates in half and then put them on either end of the board. We needed to figure out new materials to make the boards from. At the same time, it was fun. Got chased away by dogs and angry adults. I was eventually able to compete at the highest level. There’s a lot of similarities with scientific achievement. It was only later that my buddies and I, looking back, realized that we were on the forefront of a brand new sport. Totally in awe with what people are doing nowadays on a skateboard.

Tony: But we were part of that early, very early phase. I think it was exciting at the time. I see science in a similar way. I still like to do science because it helps keep my brain in shape too, to think a lot. I have to innovate, I have to plan new stuff, work in an area that no one else has been working on, and carve out new territory. There’s kind of a similar process going on there.

Grant: Tell us about the polyurethane wheels.

Tony: Because my best friend at the time, Mark Renneker, and I did a lot of skateboarding and because Hobie, which is a manufacturer of skateboards and sponsored a lot of skateboard tournaments in Santa Monica, I was able to get into the tournament scene. Luckily in 1966, I was on a skateboard team. At that time, we were skateboarding on a rubbery cork wheel. It was okay, but if you have a little pebble, the skateboard would just stop dead, and you’d go flying. So, it made you very cautious. One day our skateboard team, we were sitting around a table and our captain of the team. He came in with his paper bag, and he poured out this bunch of wheels on the table. They were all these white polyurethane wheels which we’d never seen before.

Tony: In fact, most of us had been skateboarding on metal wheels just a few years before. And we looked at these wheels. We realized immediately what this might mean, and we put them on our boards and sure enough, the boards were much easier to navigate. You could go over little rocks and not get stopped. You had a lot more contact with the surface. And luckily I think I was able to continue to compete at various tournaments, including the national tournament on those new wheels. That really helped. It was a nice example of where an innovative tool really helped you jump ahead.

Tony: Funny to think about these polyurethane wheels. It turns out they weren’t the absolute first polyurethane wheels never made for a skateboard. I mean, that bag came from one batch that had just been made. No other skateboard wheels had ever been made. When I talked to the experts who really follow skateboard lore, if I asked them when they thought the first polyurethane wheels came out, they’d say mid 1970s. We were almost ten years earlier with these wheels. I still have them, of course. Nice collector piece.

Grant: Find all the people who’ve written on the history of skateboarding and set them straight. 

Tony: Yeah, there’s this one guy. We had a big rally at the Smithsonian museum about five years ago. Tony Hawk showed up, Rodney Mullen, and all the great guys came. We already knew some of them when one gentleman who was The Professor, they call him because he professes to have all this knowledge about skateboarding, and he’s a good skateboarder himself, but he was wearing a white lab coat. He was a skateboard expert, and he was the guy that bought the first polyurethane wheels, out in the mid-1970s. Actually, the Smithsonian wanted the board that I have with those wheels on it, but I wouldn’t give it to them.

Grant: Nice. So how did you come to live a stone’s throw from NIH?

Tony: Oh, when Otsuka hired me to head up global neuroscience, the Otsuka lab was here in Rockville, Maryland, so we moved out at that time. 

Grant: What changes have you seen in the biotech scene on the 270 corridor?

Tony: I think the biotech scene along the 270 corridor is really starting to come on to its own. It’s always been a great promise, and there’s always been biotech companies. As I mentioned, I headed up one of them called Psychiatric Genomics in Gaithersburg. It never really became a Mecca like Boston, and it still isn’t quite to that level, but in the last five years, I think things have really been picking up. I know having found new laboratory space ourselves in just the last few months, lab space is still a premium and very large laboratory facilities are now being created and moved into very quickly. Companies like Novavax, Regenexx Bio, MedImmune, and AstraZeneca that took over the MedImmune site.

Tony: There’s a lot of activity that’s building now from some very successful companies. I think it’s starting to really pick up. Having NIH here, having the FDA here, having a lot of other pharma and other organizations that are associated with our industry certainly doesn’t hurt. There’s a lot of really talented people in this area. I’m just surprised it’s taken as long as it has. It’s a great area to live in, and housing’s affordable here. A lot of talent, a lot of universities. I think it’s just going to continue to build out.

Grant: if you were a young biomedical scientist just leaving academia, what geographic area would you head towards? Obviously there are many biotech ecosystems around the world. Which do you think might have the most promise if you’re looking out over the next generation or two?

Tony: Geography is important, but I define geography more than by the laboratory geography. I’d rather be in a great lab that’s in a good institution almost anywhere in the country, compared to being in, let’s say a San Francisco area, in not so good a lab or not, let’s say a more competitive and tight situation. I think the local environment is more important if the lab is fostering excellent research, and people are productive, much like I described for companies themselves. I think that’s the most important thing for a person to choose. The other thing though, besides geography is the opportunity that you find in that first job after a postdoc.

Tony: So, are there innovative techniques and new methods that are coming out of that group that you’re working with? Are they giving you projects that allow you to see your field of science in a way that no one else has been able to look at before? I’m a big proponent on new methods. I think new methods are almost always the key. Look at the CRISPR Nobel Prize that was just awarded. It’s a new method that was realized first for basic biology, but then exploited for its potential therapeutic use. That creates a field day for you. If you come into a lab with new techniques, new ways of seeing science, new ways of exploiting science, everything you do is going to be innovative and important. I would encourage people to think about doing stuff that’s on the cutting edge, as opposed to coming into a lab to put the dot the I’s and cross the T’s on the principal investigator’s work. You don’t really want to be there because that’s going to be stuff where many other people have already been.

Tony: When you go into the marketplace, let’s say your next job, you want to be able to be in a position to say, “I’ve carved out this whole new area. I see that you’re expanding in that area. I’d love to work with your team.” I’ve seen that in the gene therapy field. For example, about five years ago, there was a whole big breakthrough in gene therapy delivery, ways of getting gene therapy products into the brain or into specific tissues. That was a huge area, and it’s continuing to build, it’s continuing to show promise. I noticed that all the young investigators that were working in that field were getting jobs and moving to new places, new companies. All these guys are getting great offers to move, and that’s because the field was seeing the growth of this area. I wanted people with those skills to help those other concerns move to where they needed to be. That’s why the innovative stuff where you can see a real application in the commercial world, I think is the best single geography to consider, not so much where that lab happens to be.

Grant: What do you think will be the big changes coming to biotech as an industry in the coming years?

Tony: I think one of the big changes is in the field of bioinformatics. I’ve actually been a bit skeptical about big data and bioinformatics for a while. Partly I think that’s because bioinformaticians sometimes don’t have a good handle on the biology that they’re trying to uncover through their methods. At the same time, I can give biologists a little bit of grief too, because I think we as biologists need to do a better job knowing about statistics and bioinformatic databases and what they can and can’t provide. Getting quality data has been a real limitation. Just RNA-seq data, for example, can take a whole variety of forms. There are many different ways to measure RNAs, and how you measure it has a huge impact on the interpretations you make. I see a lot of people just blindly measuring the RNA, and they don’t really know what they’re actually measuring. Is it nuclear? Is it cytoplasmic?

Tony: Is it both? Is it cell-type specific? The questions go on and on, and these are important questions. But to answer your question about where I think things are going on in biotechnology, I think bioinformatics will come to play a bigger and bigger role as quality data is provided, as tools are created to analyze that, as people understand better on both sides of the equation what we’re looking at and how we’re interpreting it. I think there’ll be quite a revolution in how we can target disease therapies through what we learned from the bioinformatic analysis.

Grant: That’s good news for us.

Tony: Well, partly working with you, Grant. You’ve helped us actually understand some important properties along the way. I think it’s just going to get better as these methods continue to unravel what’s going on and in gene expression and gene processing for these diseases that are clearly genetic in nature. I think that’s the second level of great excitement for the biotechnology field is using these targets and these mechanisms that we’re learning about to come up with therapies. We were a bit lucky. Well, maybe I shouldn’t say lucky. We worked very hard to find this muscarinic receptor target for schizophrenia.

Tony: That was basically a four year odyssey, but it worked. The reason I say we were lucky is the cells that we used in our in-vitro assay happened to have muscarinic receptors. We knew that was always going to be a limitation. Do the cells even have the receptor mechanisms that you’re going to be evaluating? Often, people don’t even ask that question. We did a whole profiling, so we knew what those cell lines express, so we knew what candidates could come up because they were there, and the other receptor candidates we’d never learned about cause they weren’t expressed. But those are the kinds of questions that we have to ask.

Tony: I think the good thing for bioinformatics experts to keep in mind is to learn the biology about what you’re being asked to analyze. You have to be aware of the experimental design and really know about the material that you’re analyzing, whether it’s RNA-seq data or cell-based data. What are the constraints? What don’t you never learn about because the cells don’t express those players, or you’re looking at the wrong cell type, so you can never learn about the mechanism of disease because the disease is due to another cell. You have to learn about the biology, and it’s not just one sided. The biologists also have to learn about what they’re asking the bioinformatics person to analyze, what the limitations or possibilities are from that data.

Grant: So, you’ve worked remotely a pretty good chunk of your recent career, at least. Can you comment on what you’ve learned from that? And do you have any advice in this time of COVID where everyone’s working remotely, most everyone’s managing remotely. How do you pull it off?

Tony: Well, I’m actually about not to pull it off. I’m about to be back in the lab. I’m really looking forward to that. Learning molecular biology skills and applying what I’ve been learning over the last few years of genetics. I’m not a big fan of remote work. Certainly, it can be done. I’m a bigger fan of remote meetings. I don’t think people need to get on a plane and travel across the country for a one-hour meeting. I’ve even heard of a person once. He flew all the way from here to Asia for dinner. When dinner was over, he was put on a plane and he came back. I mean, this is crazy, and it’s not very helpful to our environment as well.

Tony: I think we have a real responsibility to preserve and improve our environment. I think remote meetings are fantastic. I’m seeing that as a real advantage, but the workplace is different. I think people really should be, when possible, in a similar physical proximity to one another. Especially in a laboratory environment where you have to be there to do the actual work. Hopefully when our COVID-19 vaccines come along and people start wearing masks and behaving properly, we can abruptly put an end to this pandemic. People will look at the best of all these worlds. We’ll have remote meetings. We’ll be back in the workplace. Our commutes may be five days a week, maybe there’ll be three and four. We mix it up a bit and just work to our own better advantage for all of these things.

Grant: Do you care to prognosticate about what will happen with COVID? When will things start to get back to normal?

Tony: I can’t. I’m not an expert, but one thing is really clear. If we all behave ourselves in a concerted way and show discipline about wearing masks, about keeping our social distance, about not having these large gatherings, and the vaccine becomes available, which it looks like it will. We do all of those things. As of January 20th, I would give it then another four months before we see a real improvement. And by the end of the year, we’ll be back almost to normal, but that’s the most optimistic scenario. The harsh reality is there are a lot of people who still will refuse to wear masks and won’t want to get vaccinated, and that won’t stop the process, but it will slow it down quite a bit.

Grant: What surprised you the most about this pandemic and the response?

Tony: I guess what surprised me the most about this pandemic is how so many people will refuse to wear masks. We ask our soldiers who go to war, to wear 50 pounds of gear and to go into battle wearing that kind of gear every day and literally risking their life to fight an enemy. Here we have COVID-19 as an enemy, and I’m shocked when I see people who aren’t willing to wear a one ounce mask and actually defeat an enemy that’s killed more people than these wars. I really don’t know what’s up about that, except that maybe it has become a political statement for people who just don’t like to be told what to do. We live in a society where we have to drive the speed limit. We have to follow certain laws, and this to me seems like a great example of just another minor adjustment that people should be making.

Tony: And most do. In many areas, the compliance is 90% and above, but it isn’t everywhere. If we all pull together and make these small adjustments, our economy can recover much more quickly in the long run. That surprises me that I even have to say things like this or parenting what other people say. That’s the big surprise, which seemed like this should just happen as a matter of common sense.

Grant: It’s a bit depressing.

Tony: Yeah, it is, but I think we’ll get through this, and the vaccines, I think will make a huge difference. 

Grant: As a final question, maybe a bit of a humdinger. What do you think most scientists today have wrong?

Tony: Well, let me rephrase the question a little bit. What do I think scientists nowadays, especially the younger group of scientists coming into industry really need to pay particular attention to? We all need to pay attention to the limitations of the data that we simply download from the internet. You know, I remember the first scientist who said, “Well, I’m going to Google that term and look up the answer.” I thought, what are you crazy? Aren’t you going to go to the library stacks and pull out the journal articles or read the articles and figure it all out. Both methods are clearly limited, but nowadays people do go to Google and they type in pretty much anything they want to learn about science. You certainly can learn a lot. There’s no question. It’s a fantastic tool, but the problem and what people get wrong is that’s kind of where it stops. They go, “Well, I read, I saw on the web that you can use this app. That’s what we’re going to do.” 

Tony: What they don’t do then, they don’t say, “Well, gee, is that assay really the right one for me? Or can I compare it with two others that I learned about? What I have to do empirically in the lab to answer for myself that what I’m reading and learning from the internet it’s true.” That’s what I see as a real problem. I hear this from other people who train students and see the new crops coming and going. There doesn’t seem to be as much willingness to run the empirical foundational studies that are needed to convince you that you’re on the right path. And so scientists often can move into the wrong direction, and they’re doomed to fail from the get go. You have to do the experiments in the lab to convince yourself that you’re on the right path.

Tony: Is your analytical tool sensitive enough? Does it give you the kind of information that you need? Is it analyzing the right materials to even answer the question? All of these kinds of things. I’m a big believer of positive controls and negative controls. You want to run your assay with outcomes you fully expect. We did an RNA-seq study recently where the database that was provided by Grant’s company. The first experiment I did when Grant provided the data to me was I said, “Okay, if this data is correctly provided from Grant’s team, I should be able to recreate the figure from which the data was deposited.” And sure enough, we exactly recreated the data that was in the publication for one gene.

Tony: And that gave me confidence that the other genes are going to give probably accurate results. So that’s the kind of stuff you need to do. You need to convince yourself in the lab empirically that you’re on the right track, and not just assume that because you found a method somewhere that that’s going to work. We all know most methods that you just snag from somewhere don’t work. You’ve got to tweak them, and then sadly enough, many of the results that we get in papers can’t be replicated. So the way to get it right is to have the right methods, and show that you can produce reproducible results that convinced herself that the methods are right. I mentioned persistence is important. Replication is my other favorite word in science. We have to be able to replicate what we’ve done and what other people have done with similar methods before we have any confidence that we’re on the right track.

Grant: Couldn’t agree more. Well, thank you so much for joining us today, Tony. It was great. 

Tony: Great pleasure to be with you, Grant, and I look forward to seeing great things out of your own organization and from all of us who really are committed to putting all of this that we’ve been talking about together, so that we can help people get over inherited diseases, acquired diseases, and even those that are spontaneous. There’s a lot to be learned. There’s really no end to what medical science is going to be able to achieve, and t’s just exciting to be part of this. 

Grant: What will science do next?

Transcript of Episode 5: Quin Wills

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

Quin: Hey, Grant, thanks for having me. I’m Quinn wills. I’m the co-founder and CSO of Ochre Bio. We’re a sort of deep-phenotyping, tissue genomics company, that’s trying to understand liver metabolism and how that maps to liver diseases. For example NASH, which is a type of liver disease that is becoming really big in Western society right now, and liver transplants, which is kind of cool. 

Grant: So what’s the origin story behind Ochre?

Quin: Oh, okay. Well, that goes back quite a bit. You know I started off life on the dark side, so I started off life as a medical doctor and geneticist before I moved into comp bio. And I was always interested in liver metabolism and metabolic stress in those days, more around alcohol metabolism and fetal alcohol syndrome. But when I moved down from South Africa to the UK, I think like a lot of us, I became very intrigued with big genetics and big genomics.

Quin: I could see what was coming on the horizon of the human genome project and wanted to be more directly involved with that. I didn’t like the idea of handing over data for other people to think about and engage with. I really just retrained. Did a few extra degrees in mathematics and comp bio, and then my PhD in systems genomics at Oxford, really focused on technology, but constantly thinking about liver and liver metabolism all the way through. 

Quin: I started my first biotech company before I finished my PhD using, in those days, gene expression arrays. Can you believe it? I’m sort of dating myself now. I was trying to compete with high content imaging to really understand drug toxicities of target effects for the pharma industry. Moved that out East and carried on my academic career, doing single cell genomics and spatial genomics as we do now, these kinds of things. 

Quin: And I pitched the idea to a large pharma company that was coming on to the Oxford campus that we’d like to use these technologies to improve target discovery in NASH or NAFLD rather. I prefer to call it NAFLD, non-alcoholic fatty liver disease. So in other words, too much fat in your liver, which one in four of us have these days with obesity. And they loved it.

Quin: And of course we got going into some big industry-type pipelines and great technologies at a functional genomics level. And I loved it. I genuinely loved it. I think the clinician in me was a little bit frustrated with the struggle to translate this problem. 

Quin: It’s a massive problem. We are realizing it’s so connected to cardiovascular disease, diabetes. Too much fat in the liver now is becoming the main reason for needing a liver transplant in many countries. It’s not alcohol, it’s not viral hepatitis anymore in many places in the world. And it’s also becoming the main reason for the shortage of donor livers. Because fatty livers don’t transplant very well. 

Quin: I just really wanted to solve this and I do really want to solve this still. And I figured that it was one of those sort of moments where we have to be honest with yourself and say, well, “Genomics is great. And all the computational work is great, but we really need more to take this to the next level.”

Quin: And really the idea that came together was when I started engaging the transplant surgeons in Oxford, and we chatted about this problem in the transplant space. And they mentioned to me that they’re about to publish on this incredible liver perfusion machine, effectively a machine that keeps donor livers alive outside the body so you can assess the liver, keep it warm, keep it nourished before you transplant it. And would I want to study these fatty livers, these discarded or donor fatty livers on the machine and do really interesting temporal analysis with gene expression. 

Quin: And I realized very quickly that this was an incredible alternative to a preclinical model. It was so much more satisfying going from sequencing lots of livers or studying liver physiology at a tissue level to then testing out your hypothesis in a living human liver on a machine and thinking about what’s going on rather than going to a high-fat diet mouse, for example.

Quin: But I think for me, there was more to this, and I decided that it deserves to be its own company. And the moral to this is that by really focusing on the transplant space rather than diabetes, which I’ve sort of been for many years, this really allowed us to not only study livers on machines, but it allowed us to find new targets by studying the right spectrum of disease, which is a big issue in the space.

Quin: Right now, people are focused on biopsies of very inflamed, fatty livers, whereas we’re sequencing. We’re building the largest genomic atlas of the human liver, using a thousand livers from one of the biobanks in Oxford, going from healthy level all the way through to the early stages of disease, which we love.

Quin: And I can tell you a little bit about exactly how we’re building up this atlas of what’s going on in the liver. And then finally–and this is more clinical twist rather than a comp bio twist–we wanted to get around the biomarkers problem. You and I both know this is a massive problem in our field and a massive problem in clinical trials.

Quin: And fatty liver disease, NAFLD, is like a lot of chronic diseases: silent for many, many years before it presents. And they are just no good biomarkers, so there are no ways to do good clinical trials, particularly for the early stages of disease. So everybody’s very focused at the moment on very late stage disease endpoints, crude endpoints, and molecules and experimental drugs that frankly do fall too little too late. And this is why we still don’t have a therapy on the market. Not to over simplify: there are many reasons. But it’s a difficulty in clinical trials that is frustrating the space right now.

Quin: Of course we love biomarkers, like any good computational biologists, and we were thinking about it, but we don’t want to bank on it. And so rather than trying to do these very difficult clinical trials in the fatty liver space, in terms of NASH and NAFLD, we rather want to think about this in the transplant space. So treat fatty donor livers with the same kind of therapy we would use for patients living with the disease and improve outcomes in the transplant space. And those trials are much simpler. I mean, they, like all clinical trials, are a big gamble, but compared to the biomarker perspective, have much clearer endpoints.

Quin: And then what we will do as part of that, is look for improved long-term outcomes in these livers, and we’re doing this using these new GalNAc siRNAs. So they are liver-specific siRNAs that can last, with effects for many, many months. And so we can treat these livers entirely ex vivo on these machines before they’re transplanted. And so it never really goes into the patient. It’s entirely a liver specific treatment, but we can see the effects play out over the first six months of the transplant life. And, that’s where we’re at. And that’s what we’re doing as a company. 

Grant: That’s amazing. So tell us a bit more about the lasing efforts.

Quin: Our first big target discovery project is this human liver atlas, which takes a thousand livers from the QUOD biobank in Oxford. So this is a collaboration with transplant surgeons and the QUOD scientist at Oxford, where we are genotyping these livers. We are bulk RNA sequencing these livers. We are spatially sequencing these livers. So in fact, all of these livers get sequenced up to 4 times. So there’s really a lot of data at an RNA sequencing point of view generated on these. 

Quin: We also do imaging AI. And that is really to standardize the histopath. So we have about seven images taken from each liver so we can understand what’s going on. The imaging AI also identifies the key regions that are interesting to us. For example, there’s this cell phenotype called ballooning degeneration that happens into the cells that we think is really important, and we’re trying to understand as something that connects too much fat and then inflammation and fibrosis cirrhosis in the liver.

Quin: And then we have all the bloods from the donors so we can understand cardio-metabolic parameters. And we have about 150 clinical variables from the donors and the recipients. Because most of these livers that we’re working on ultimately exist in somebody out there somewhere. And so we can really start asking questions both around disease progression and particularly the early stages of disease, which we’re quite keen on and then how that maps to transplant outcomes. So can we think of targets that are applicable to both?

Grant: So what would be your ideal outcome in say five years, if everything goes perfectly?

Quin: Yeah. I think like all biotechs, we have what we’re doing now, but there’s a grander vision and unlike a lot of computational biologists, I believe that this type of work is what we call deep phenotyping or cellular genomics is going to be such a big part of tying this together in our space. You know, GWAS and associating SNPs with clinical disease is a great first step, and pharma is beginning to embrace this in many ways. But in particular, I would argue for chronic diseases where there’s so many factors and so many steps in the progression, this has to come into play.

Quin: And so we’re using this to understand liver metabolism and particularly fat metabolism and how that plays out in the liver and also how that plays out systemically. But I think we consider ourselves a cardio-metabolic company, and we’re really looking at more fundamental health span drivers that we think we can be targeting in the liver to improve outcomes and really improve human health span.

Grant: Great. So you guys went through YC. Can you tell us a bit about your experience there?

Quin: Loved it. My co-founder and I, Jack O’Meara, had an unusual meeting. I left my job with this pharma company. I decided to go build my dream treehouse in Costa Rica and rethink how I want to do this and where I want to be based. Do I want to be European in terms of biotech or do I want to be on the US side. And of course, they are very different cultures. Even in the US, West coast biotech is very different from East coast biotech which is very different from UK/ Europe biotech. Very different funding, cultures, VC styles, trajectories in terms of how the companies grow.

Quin: And I figured I possibly want to be on the US side, even though I have such strong roots in Oxford and the transplant scene and the liver scene and the metabolism genomic scene. But then I was introduced to Jack. I called Jack. And at the time I called Jack, he was building huts in Tanzania.

Quin: We immediately realized we were kindred spirits and equally crazy. Jack is an individual who also comes up through biology, more tissue engineering and is really focused on clinical progression. So getting therapies into markets. He really was my natural counterpart and shared a lot of my philosophies. We both share a lot of philosophies on how to really innovate in biotech and what that really requires. So we started off in London. I came back, Jack was in London, but it was a strange one. We went out to California for a conference, met the YC folks on the day that particular cohort was beginning, chatted with them. Within two, three hours I said, “We like you guys, stick around.”

Quin: And we canceled our tickets back and never went back to London until COVID forced us to come back, take our money and get set up. We’re very much a Californian company, but with a sort of subsidiary in the UK and very, very international operational. Oxford lab is what handles a lot of the target validation and screening work after we’d done with the target discovery and before we put it into profused livers, which we do with transplant centers here in the UK, particularly Birmingham.

Grant: So, what is your philosophy of innovation?

Quin: Well, now you’re going to get me to step onto my podium. Yeah, I’ve learnt a lot. I’ve been humbled a lot over the last 15 years of doing comp bio. I’m seeing a lot of different cultures all over the world on how to do things. For me, there are three things that seemed to be common to a lot of very successful genomics companies.

Quin: So maybe we call ourselves a genomics company rather than a biotech company. One is speed. I mention this because this is something that I think a lot of academic computational biologists struggle with. It’s such a culture shock to them particularly if you come from big academic centers. There’s this whole culture of “Oh, it’s okay. Get it a hundred percent right six weeks from now, Eighty percent now is probably not fine.” Whereas in our world it’s very different. 

Quin: The centers in academia are very different. There’s this “publish or perish” sort of notion, whereas in our world it’s “deliver or die”. There’s just no substitute for speed. A biotech company with half as good an idea and twice the amount of money will probably get there faster, unless you are super quick. And there really just is no getting around that. Building that culture in a comp bio company is extremely tough. I am far from perfecting it, but we try our best every time and hopefully get there with the teams.

Quin: I think the other important thing is value based innovation. I can’t think of a better way to describe it. Again, in academia there’s very often this idea of blue sky, go be a brilliant computationist and come up with new algorithms. It doesn’t matter if they’re relevant or not. Just over-engineer, go wild. For us, innovation by definition has to be better, cheaper or faster than what somebody else is doing, because if it’s not, don’t waste your time. Get on, do it the way other people are doing and focus your intelligence and creative mind on other problems where we can be better, cheaper, faster, and it really must be valuable. As a biotech, you have so many priorities and you’re moving so fast that even if something does seem like it could be better, cheaper or faster, right now, it’s just not a priority for you. And being able to balance that, especially if you’re naturally a creative person as so many computational biologists are, it is a difficult balancing trick.

Quin: And I think companies that get that right tend to be more successful. And I think one final one, and this is where I do believe US companies are winning hands-down, is a general can do positive attitude with a positive risk-based culture. We in Europe struggle with us. We are still very risk averse. We still always find ten reasons for why something won’t work. And it amazes me how much of general positivity, which you see both on the East coast and the West coast, can really transform innovation within a company. And those are the three things. 

Grant: What do you think American biotechs can learn from European biotechs culturally?

Quin: That’s a toughie because I’m always raising it the other way around. Let me rephrase that question a certain way. There are certain attitudes in American biotech that won’t play out in European biotech simply because the funding structures are very different. A lot of what I’ve just discussed works extremely well when you’re also chucking a lot of money at the problem.

Quin: As I hinted at early on, there is no substitute for just chucking a lot of money at the problem. That is part of the high risk game that we play when we take venture capital, and we try to move quickly. A lot of biotech in Europe tends to follow a trajectory of incubating within a university center, which perhaps is not so different from other biotechs, but then taking on grant structures, which have a longer timelines. These are all great. There’s no right or wrong way. People will argue whether it’s right or wrong, and there are lots of opinions around those. I do believe fundamentally there’s no right or wrong way to do that, but that is a much longer trajectory, and so certain attitudes won’t play out to that.

Grant: Great. What about the other way around? What can European biotechs learn from American biotechs under the constraints of differences in availability funding?

Quin: Stop overthinking everything is the one simple way of explaining what I see when I look at a lot of European biotechs. Again, it’s really about focus on speed, focus on value-based innovation and focus on creating a can-do culture. Really those three things for me are so, so important. I’m South African, even though sort of the UK is my home now. Even after all these years after making the UK my home, it still amazes me at how difficult it is to get a high five kind of culture going. It just doesn’t always work. It just doesn’t work with the culture. It doesn’t work with people, but finding ways to implement a more positive can-do culture needs to happen in a lot of biotechs to emulate what’s happening in the US. But maybe reappropriated the way we do things on this side of the pond.

Grant: So changing tack here, if you weren’t a scientist or a physician, what would you be doing?

Quin: It would be building tree houses in Costa Rica nonstop.

Grant: Talk a bit more about that. I’m curious about this. What’s the story here?

Quin: It’s easy to make up stories to defend things you’ve said when you were young. I want you to be careful about causality, but I remember in my first year of school when your teacher asks you what you want to be and what do you want to do? I suppose I was a bit of a precocious kid. Probably still a precocious kid now, even though I’m in my forties. I said to my teacher, “I want to be a medical doctor that does research.” So that was my career choice then and there and that one played out.

Quin: Maybe I was creating a self-fulfilling prophecy. I always said I wanted to live in a wooden house. So growing up in South Africa, that had a very particular idea attached to it. But as I learned to love traveling all over the world as a scientist, as so many of us do, I just fell in love with Central America. I really fell in love with Costa Rica and the life out there. And so together with a really good friend of mine, who’s in many ways the Jane Goodall of Central America, put some money down for some jungle and started building my dream treehouse out there next to a beautiful river with gorgeous waterfalls. And I will keep doing this as long as the money is available and I can do it. Build my little community and friends and retire out there one day. 

Grant: When is the other South African internet enabled satellite service coming available? 

Quin: Yes. Soon, please. 

Grant: You can just work from your tree house, right?

Quin: Yes, absolutely. You joke, but right now I’m trying to solve this problem from a distance because one of the first things I had to do out there was set up some solar powered WiFi. Well, 4G to WiFi routers. The solar panels are still working and the repeaters still seem to be working, but the main router is sitting on a hill somewhere. It seems to be giving in. With my poor Spanish and others’ poor English, it’s difficult trying to fix this. So yes, please. Somebody needs to sort out internet to the jungle very soon.

Grant: Pretty cool. Tell us a bit more about when you were a kid. Why did you say you wanted to be a medical doctor who did research? Did you have a family background in this or read some book? Came to you in a dream?

Quin: Honestly, I still don’t know where that came from. Something must’ve sparked that. I come from a long line of engineers and tinkerers and inventors, so that there is the invention streak in my family. That’s for sure. But it was bizarre. I said I wanted to be a doctor. My brother said he wanted to be a lawyer. He did the next best thing and became a banker. 

Grant: I knew it. Where are they living? 

Quin: My brother lives out in the UK, too now. Enjoying the London highlife as a banker.

Grant: Fancy schmancy, huh?

Quin: I hope to make as much money as he does one day.

Grant: So tell us about the COVID situation. I think you two had maybe even a more interesting than average experience with the timing.

Quin: I did a quick trip out to Costa Rica. A few days into the trip I just realized that the world was shutting down with this epidemic which became pandemic and very quickly flew back to California to watch California shut down and everyone panic. I was there when everyone’s doing the panic purchases and toilet paper was disappearing and all the rest. We have to make a call. Do we hang around in the US and try to continue our fundraising out there? Or do we assume that the whole world is going to go virtual and we need to be right back in the UK so once the money is in, we can set up and continue in the UK. It was again one of those things where Jack and I just looked at each other and said, “Right, tonight we’re going home.” and we did and got there.

Quin: Thankfully, everybody adjusted to the new normal very quickly. Thankfully, biotech funding only improved. Not that I would wish for COVID, but I think it focused individuals on healthcare and investing in companies with longer term goals rather than immediate profits. And so that played out very well for us. And thankfully again, we’re able to get set up in an academic center so that the lab can continue on rota. Because a lot of this initial target discovery was in collaboration with QUOD and the transplant scientists as being processing tissues, that could continue. Not without its problems. It’s cost a lot more money to keep the show on the road. I think a lot of international couriers have made a lot of money out of us getting samples sent all over the world, But again, count our blessings and thankfully, so far, so good.

Grant: Great. So what advice would you have for biotech entrepreneurs? 

Quin: So the common question you get asked is, do you need to have a brilliant idea? You do need to recognize your unfair advantage as an individual. What is it that you’re not only particularly passionate about, but that you’re probably better than average with, and that you could really trade on?

Quin: I think in many ways that’s more important, but of course, a fantastic idea doesn’t hurt. Most important, and I’ve learned this painfully over the years and growing up in the space, is that your co-founder is everything. I was really adamant that I didn’t want to have another very academic scientific co-founder. It’s kind of like finding a life partner and getting married. Somebody who just naturally compliments you. You match each other, when you’re both down, you can fill in for each other, it’s you two against the world, that kind of thing. I found that in my co-founder, and I know that no matter what, we will keep assisting together and we’ll find a way to make this successful. And that is just so important to do. 

Grant: So other than serendipity, how can people maximize their chances of finding a good co-founder? How can you increase the likelihood of a serendipitous event?

Quin: That honestly, I think the rise of these accelerator programs internationally has been a fantastic way to do this. Yeah, I could have done things up through the Oxford ecosystem, but one of the reasons why I went for these accelerators is because of this. Something perhaps that we are better at, says he controversially, in the UK versus the US is how accelerator programs are done here. There’s a truly excellent accelerator program here in London that we started off with called Entrepreneur First. They have many philosophies on how one could be competitive as a UK company. 

Quin: And right at the top of that is getting the right co-founder. Versus a YC where it’s “come with an idea and your co-founder, and we’ll give you a good chunk of money just to move very quickly with it”. EF’s approach is “find the right partner”, and they give you money and time to play with the right individual and really test out if this fits together. Both are great models. Both worked for us, but really first and foremost, it began with Entrepreneur First, here in London. 

Grant: Cool. So long-term, looking out when you retire whenever that is, what would you like to be known for? What would you want to have done?

Quin: I think really fundamentally, I am driven by this concept of improving human health span as somebody who started off life with a condition. When I look at health care, it doesn’t matter which health care model you believe in on either side of the pond. When you look at how as a species we’ve doubled our lifespan and are progressing to tripling it, and what that means in terms of crippling healthcare in the later stages of life. I think one of the best ways to flatten that curve, no matter which side of the funding equation you are on in healthcare, is that we need to start moving away just from disease endpoints and to start focusing on fundamental health span endpoints. Some people would call this aging myths and anti-ageing myths.

Quin: I’m careful of using those terms because it does come with a lot of traditional baggage around that, but really focused on the fundamentals of health span and how we tackle that as add-ons to other therapies or even fundamentally just to improve your health span. And when you’re in the cardiometabolic space like this- diabetes, liver metabolism, NAFLD, it’s a really juicy problem to be thinking about. I hope one day I can, I can just make a little bit of a dent in that problem and improve health care for humanity.

Grant: And what do you think are the most sensitive levers there that we can pull?

Quin: That’s a very, very big question. I think perhaps I will answer this in an overly simplistic way. I love to listen to this debate, the healthcare debate. And for me, the obvious debate is always the side of the pond, the US verses universal healthcare. And how does that pay out? It’s interesting because if you look at big data and how it’s driving health care, a lot of trends are non-obvious. For example, a lot of preventative health, which overlaps a lot with how I think about health span, has been driven by private medicine. Insurance companies are rewarding you for your apps and staying healthy and taking so many steps, which is light years ahead of perhaps how we do it in the NHS here in the UK.

Quin: And I say that very carefully so that I don’t get myself into too much trouble. So there are a lot of non-obvious things happening in the space, but I think what is universal to all of us is that we spend an extortionate amount of money in the later years of life to stay healthy. You know, we’ve all now in the COVID era, become familiar with this concept of flatten the curve. Flattening the curve in healthcare I think needs to come from therapies that are fundamentally focused on health span and health span modifiers, whether that’s more preventative medicine or that’s attached to other therapies.

Quin: Even more simply put, one way I like to say this to people who think a lot about obesity, because of course, obesity is a very big area that I think about too, in the space. We really need to stop preaching to people and telling them to move more and eat less. It doesn’t work. It really doesn’t work. And if we could solve obesity as an issue, we would make massive strides towards flattening that curve in healthcare. 

Grant: Well, it’s impressive how strongly heritable obesity is in the context of environment. But in terms of comparing across different phenotypes, it’s very heritable.

Quin: It’s incredible. And it’s just incredible seeing what we know about obesity and body fat distributions and how that affects different diseases. Why are we still so moralistic about this in general health care? I can only hope that future generations will look back at and shake their heads at how silly we were at really just forcing this. Body shaming people and making them feel bad about how much they eat and the sedentary lifestyle. Of course, these things are important.

Quin: Of course, we should do our best to exercise as much as we can and not consume too many calories. but we need to start thinking more smartly about how we modify these phenotypes like obesity and fat distribution. There are the three distributions, and in many ways as a company we focus on one, there’s fats around on the outside of a body. And many of us in research know there’s fat around your internal organs, but now we know various organs also build up fat inside of the organs and inside the cells of the organs as you age, and the liver is one of them. We just need to think about how we treat this problem.

Grant: So on a completely unrelated topic, what surprised you the most about the COVID pandemic? 

Quin: I love non-obvious things playing out, and especially when they come out in your favor. And I think the thing that has amazed me is how well biotech, investment is happening right now. And again, like a lot of complex systems, you should be very careful of coming up with overly simplistic explanations. But it seems to me with COVID that it has really highlighted to people how fragile we are as a species in terms of healthcare. We can fly to the moon. And yet, a simple virus can wipe out an economy very quickly.

Quin: It seems to have really made venture capitalists more eager to focus on companies like biotech, where you’re not expecting to turn out a massive profit in three years’ time. And that’s fantastic and power to all of us, because that is where I believe the future is in good innovation, and venture capitalists need to be taking bigger risks.

Grant: Many people who maybe weren’t in the infectious disease space weren’t seriously anticipating something like this. What things do you think are underestimated? What things do you think in the next decade or so could really come out and change things in a way that most people aren’t at all prepared for. 

Quin: Quite honestly, I think it’s worth pointing again to infectious disease. You know, it’s really incredible. If you’re interested in aging medicine and aging drivers and nutrient sensing and how these things play out as we age, you look at some of the great, popular books that have been written recently. Particularly coming out of East coast, West coast, US, and I think almost all of these books, by various academic authors, except for one or two, perhaps have all said that. Of course the future of humanity is so dependent on understanding the fundamentals of aging metabolism, et cetera, et cetera, with a one exception. That there could be a massive viral or disease pandemic that can wipe us out. And there you go. This is what we live in right now. We are a very fragile species.

Grant: This is the little league version here, right? If it’s at the fatality rate of SARS or MERS or something, we’d be in a deep shit.

Quin: No, absolutely. Let’s hope we learn from this, but history dictates that humans need a few things to go. You need it to go wrong a few times before we learn. Fingers crossed, it is not the case this time round, for sure.

Grant: Great. Well, thank you so much for joining us today. I really, really appreciate it. And also, is there anything else you want to say before we go? Anything else about the company, a shameless plug, some hobby of yours, whatever. 

Quin: No shameless plugs, only to just thank my co-founder and thank my team. It’s such an honor and a privilege as a modern human being to be able to chase your passions, to be financed to chase your passions and have, even if it’s just a small chance of being able to make a real difference to human health. And I’m very lucky to have the people I have around me. So thank you. And, thank you for having me on, man.

Grant: Well, thanks for joining us. It was great.

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.