The Bioinformatics CRO Podcast
Episode 50 with Alfredo Andere
Transcript of Episode 50: Alfredo Andere
Grace: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley. I’ll be your host for today’s show, and today I’m joined by Adam Siepel. Adam is chair of the Simon Center for Quantitative Biology and Professor of Biology at Cold Spring Harbor Laboratory. Welcome, Adam.
Adam: [00:00:15] Thank you. It’s great to be here.
Grace: [00:00:17] Yeah, it’s great to have you. So can you tell us a little bit about the research that you’re doing?
Adam: [00:00:21] Yeah. So we do work in a variety of areas in genomics. My laboratory is completely a dry lab. We do only computational work, but we collaborate closely with a number of experimentalists and really try to stay as close as possible to data generation and biological questions. But we do have strong backgrounds in probabilistic modeling, algorithms, machine learning and related areas, and we try to bring those skills to bear on our work. We are interested broadly in questions involving evolutionary genomics, in particular evolution of gene expression. We are interested in demographic reconstruction of human populations involving both humans themselves and human interactions with archaic hominins such as Neanderthals and Denisovans.
[00:01:11] And we’re also interested in natural selection making inferences about the strength of natural selection, which parts of the genome are affected by natural selection, and natural selection on different time scales: ancient natural selection affecting primates and more recent natural selection affecting human populations, for example. And then, I should say also, we’re interested in applications not only in human population genetics, but also in cancer and agriculture and other areas. We’ve also done some recent work on COVID modeling, for example.
Grace: [00:01:43] Yeah. I saw a really interesting paper from your lab on COVID, looking at the influence of daylight savings time on immune response.
Adam: [00:01:53] That’s right. Yeah, it sounds like sort of a crazy connection. But one of my colleagues here, a senior scientist, Rob Martienssen, HHMI investigator, had a hypothesis that there could be an interaction between daylight savings time and seasonal patterns of COVID infection relating to the fact that at certain times of the year, people are most likely to be interacting with other people during their daily commute, exactly at the time of a nadir in immunity, which occurs around sunrise. And he had the observation that daylight savings time changing the clocks prolongs the period of time when the daily commute coincides with sunrise. And so we did some modeling to show that indeed, there seems to be a signal in the public data indicating that there is an effect and that infections could be reduced by eliminating daylight savings time. So that’s one of many reasons to eliminate daylight savings time.
Grace: [00:02:53] Yeah. I’m all for that research as long as I don’t have to wake up an hour earlier than normal.
Adam: [00:02:58] That’s right.
Grace: [00:02:59] Yeah. So can you tell me a little bit about how you get those data sets to predict how human genomes have evolved over time?
Adam: [00:03:09] Well, all of our research is based on data that has been collected by other groups. Much of it collected by Svante Pääbo group at the Max Planck Institute in Leipzig, Germany. And they have over many years now develop techniques for extracting DNA from fossilized bones. The techniques are quite sophisticated because if you’re not careful, it’s very easy to contaminate the ancient hominin DNA with modern human DNA. And so they’ve developed clean rooms and special DNA extraction techniques and special purification techniques. And then post-processing bioinformatics techniques to ensure that the DNA they’re sequencing really represents the ancient remains and not the modern humans who are handling the fossils. But we can’t claim responsibility or credit for any of those works. We’re consumers of the data that they have produced and made publicly available.
Grace: [00:04:10] Right. Of course, I didn’t know if it was some sort of reverse modeling like taking current human DNA to predict what the DNA looks like previously.
Adam: [00:04:19] Well, there’s some of that because what you get is kind of a noisy readout of the the DNA for the ancient remains. And then we have higher quality representations of modern human DNA. And then we try to model the processes that could have given rise to both of those samples. And that does involve sophisticated statistical methods for reconstructing ancestral DNA, as well as explaining the observed samples.
Grace: [00:04:48] Yeah. And so can you tell us a little bit about what you found in that research?
Adam: [00:04:53] Sure. It’s now fairly well known and well accepted based on findings that were developed over the last decade that there has been a genetic interaction between modern humans and Neanderthals. In particular human populations outside of Africa, including Europeans and East Asians show a signal of Neanderthal DNA at something like three percent of their genomes that traces back to Neanderthals. And the best explanation we have for that signal is that there was some sort of interbreeding between modern humans and Neanderthals, probably outside of Africa, after humans had migrated from Africa, something like seventy or eighty thousand years ago. And that signal persisted as these out-of-Africa populations spread across the globe.
[00:05:55] We came in already knowing these findings and already familiar with these findings. And we tried to develop a model that would jointly explain a number of ancient samples and a number of modern samples from around the world. And our goal was to see whether we could both explain this known pattern of Neanderthal-human interaction, but also possibly detect other signals of interest. And what we found interestingly, and this was published in 2016 in a paper in Nature that I jointly co-led with Sergi Castellano, who was then at the Max Planck Institute in Leipzig and has now moved to London. What we found was that there was a signal, surprisingly in the opposite direction of modern human DNA in Neanderthals. And this was something that hadn’t been reported previously. And the signal was quite subtle. And it was quite difficult to convince the community that it even existed.
[00:06:49] But we were able to convince reviewers of our paper, and it has since been supported by a variety of other analyses. And interestingly, this signal is not specific to out-of-Africa populations. It’s shared by Africans as well, and it appears to be much older. We’ve since in more recent work, dated it to somewhere around two hundred fifty thousand years ago. And so that suggests that there was an earlier integration event that left a signature in the opposite direction from modern humans to Neanderthals, and it affected all human populations. So it probably occurred in the ancestor to all modern humans. Furthermore, that’s interesting because it must have predated the migration of humans out of Africa. So it seems like there was a group of early modern humans that migrated out of Africa interacted with Neanderthals, leaving this signature in Neanderthal DNA that we’ve detected.
[00:07:52] And then that group of modern humans either just went extinct or ended up being absorbed back into human populations in Africa before a second migration out of Africa seventy or eighty thousand years ago. So anyway, it suggests not only another interaction between modern humans and Neanderthals, but one that’s much earlier, and it paints a picture of multiple migrations of modern humans out of Africa. And only the more recent cases led to the current populations that we know of today outside of Africa.
Grace: [00:08:28] Yeah, that’s so fascinating. I feel like bioinformatics is already such an interdisciplinary subject. I mean, taking together biology and computer science, and then you take it a whole step further and add anthropology in there. Do you work closely with people in anthropology or studying human history?
Adam: [00:08:48] Yeah, I have not worked directly with anthropologists myself. Although we did have an anthropologist collaborator on the paper in Nature, although we worked more closely with Sergi than with me. But there is a lot of interest across the field at this intersection between genetics and anthropology and Svante Pääbo and David Reich and others have been quite proactive about interacting across fields. I attended a meeting here at Cold Spring Harbor a few years ago that was organized by co-organized by David Reich. That was a group of geneticists and a group of anthropologists together discussing these issues, and it was fascinating. But it’s not something that’s really in the center of my own research.
Grace: [00:09:29] You seems to have a very broad reach with your research. It’s great. Yeah. So you started out doing computational modeling and phylogenetic modeling in HIV. Can you tell us a little bit about that work?
Adam: [00:09:41] Yeah. So this was my first job actually straight out of college. I hadn’t gone to graduate school yet. Through a friend of mine who had been an undergraduate with at Cornell, found out about this opportunity to work at Los Alamos, doing HIV sequence analysis. And it was interesting to me for a number of reasons. I had done an undergraduate degree in agricultural and biological engineering. And so I had been interested for a long time in sort of the intersection between mathematical modeling and questions in biology. But I had never dealt with DNA sequence data before, and I had never dealt with phylogenetics or evolutionary reconstruction. And when I was exposed to those fields, I just found them fascinating. I mean, they resonated with me in a whole variety of different ways. I’ve always been interested in reconstructing the past.
[00:10:28] I’m interested in random processes. I’m interested in computer algorithms. I’m interested in evolution. And so all of these interests sort of came together in this fascinating area of using phylogenetics. And then that work also had an epidemiological component. We were building phylogenetic trees to describe HIV sequences, but then we were making use of them to understand the spread of HIV across the globe because we were seeing different strains emerge in different regions of the world. And then we began to see interactions between these strains and the production of recombinant strains of HIV. So my first scientific paper was actually on an algorithm that I developed, a very simple algorithm, to detect recombinant strains of HIV, which at the time was a kind of a new idea and something that was of great interest in the field.
Adam: [00:11:23] My first experience was very exciting publishing a scientific paper. I think I was 23 years old and was able to publish a paper that established researchers in the HIV field were excited about, and I got to present at meetings and so on. And after that I was hooked. I was hooked on science, I was hooked on computational biology and I was hooked on evolutionary genomics.
Grace: [00:11:45] Do you still keep up with emerging HIV research and things like that?
Adam: [00:11:49] I haven’t participated in HIV research since that time. I moved on to other questions. Although I have to say I got interested again recently in this question of recombination and viruses with the emergence of COVID-19. And reread some of those old papers and including my own old work on detecting recombination in viruses because there was some discussion about the role that recombination might have played in the emergence of SARS‑CoV‑2 in human populations. But that’s my only experience in that area in the last 25 years or so.
Grace: [00:12:30] Yeah. So I guess speaking of the pandemic, from an evolutionary standpoint, do you think we need to worry about new variants and things like that?
Adam: [00:12:41] The basic evolutionary fact is the probability of emergence of a new variant should be proportional to the number of viral replication events, which is going to be proportional to the overall number of cases. And so we need to get the number of cases down. And the best way to do that is through vaccinations. It’s been extremely discouraging to me to have these effective vaccines, more effective than anyone could have hoped, and see people reluctant to use them. So I think we just have to keep hammering on the vaccination efforts. They need to be available across the entire world, not just in rich, first-world countries. We need to push really hard on getting access to them, convincing and incentivizing people to use them.
[00:13:36] Ultimately, I think new variants will emerge. We will develop over time an increasing sort of baseline resistance for most people in the world who will eventually be exposed. And I think the pandemic will eventually reduce itself to a baseline level. But I think the virus will be endemic and we’ll have to adjust to it being part of life. I’m optimistic that with increased baseline resistance, increased vaccination, increased ability to provide new vaccines quickly and efficiently, that we won’t be brought back to our knees by emerging variants. But it’s difficult to say for sure what could happen as new variants emerge.
Grace: [00:14:29] I always like to hear the different perspectives of people in different fields of science on the pandemic. I think the evolutionary take on it is very interesting. You also did a study in bats, on the evolution of bat immunity and things like that.
Adam: [00:14:46] That’s right. Yeah, we’ve gotten very interested in comparative genomics of bats, in part because of their connection with SARS-CoV-2. But for other reasons as well. In fact, our initial work on bats has been funded by our cancer center here at Cold Spring Harbor. Because bats are remarkably resistant to cancer and we’ve been trying through DNA sequencing and comparative analysis to shed light on the genetic underpinnings of both bat specific immune responses, bat specific cancer resistance and longevity of bats. Bats are extremely long-lived mammals for their body size. If you plot body size versus lifespan in mammals, you see a general proportionality. But bats are an outlier. They live much longer than other mammals of similar size, such as mice. Bats can live 35-40 years or more.
[00:15:42] We’ve been doing DNA sequencing and analysis. We have an initial preprint out on our findings. We’ve found some interesting things in both immunity and cancer, in particular a massive contraction of the IFN1 locus. And a strong enrichment among apparently positively selected genes for tumor suppressors and DNA repair genes. And we’re in the process of working closely with experimentalists to begin to test the actual molecular basis of some of these differences between bats and other mammals. And we’re also in the process of applying for grants from the NIH on this topic.
Grace: [00:16:22] That’s so cool. Because I guess I wanted to ask a little bit about the importance of evolutionary biology in the study of cancer, because I wasn’t necessarily sure how those two topics connected. So that’s a really interesting take on comparative genomics and looking at how that immune system has influenced their susceptibility to different cancers. And bats have a reduced immune response. They don’t have a very active immune system, is that correct?
Adam: [00:16:51] Yeah. They seem to be able to tolerate viral infections without having a very powerful immune response. And it’s interesting because when you look at what makes humans sick when they become infected by SARS-CoV-2 or other viruses, it’s often an overly powerful immune response that makes them very sick. And so in some cases, it seems that viruses are killing us, not because our immune response is inadequate, but because it’s too powerful. And one hope is that we can learn something from bats in the way that they’re able to keep from getting sick from these viruses and yet not have an overly powerful immune response that ends up harming them more than the virus itself. Yeah. So that’s one of our interests in this area.
[00:17:44] Of course, it’s also interesting to just understand the dynamics of zoonotic transmission and the way in which bats are harboring viruses and then transmitting them to people. The fact that bats seem to be able to tolerate such high viral loads does seem to be essential to their role as a reservoir for viruses that get transmitted to humans. And so understanding their viral tolerance is also important and interesting in that regard.
Grace: [00:18:16] Yeah. So I mean, evolutionary biology is kind of a pretty popular science topic. So what do you think are some misconceptions that people in the general public have about evolutionary biology?
Adam: [00:18:30] One misconception is understanding the diversity of selective forces that have influenced humans. People tend to think in conventional terms about the strongest humans being the ones that propagate, you know, the ones that are least likely to be killed by predators and that sort of thing. And undoubtedly avoiding predators was a source of selective pressure on humans. But there are many others that I think tend to be underappreciated. One of them is infectious disease. I mean, humans have been enormously shaped by infectious disease. And one of the strongest selection pressures on us is the resistance to infectious disease. The pandemic is helping make this issue more clear. But I think in general, we tend to have forgotten a lot about infectious diseases because they play much less of a role overall in modern life than they have in the past.
[00:19:28] Another really important selective pressure is sexual selection. The choices people make about who they mate with for various reasons. And then there are very strong selective pressures that influence reproduction in a way that humans have no choice over. So, for example, sperm competition individual sperm cells competing with one another to fertilize an egg. So there are many, many levels at which selection acts. And I think when people just think about a caveman dodging a mountain lion or a bear, they’re only getting at a very small sliver of the diversity of selective forces that have influenced human evolution.
Grace: [00:20:14] Yeah, that’s true. There are some really interesting selection events. So after you worked in Los Alamos, where did you head after that?
Adam: [00:20:22] Well, I was working in Los Alamos in the mid 90s. And I had an engineering background and I had a lot of interest in developing computer software. And at the time, I felt that my interests lay more in the software development area than in the scientific research area. And it coincided with a time where there was a lot of opportunity for software development in bioinformatics. A lot of companies were creating bioinformatics groups. A lot of people were developing and either selling or making publicly available bioinformatics software. And so I took a job at a group in Santa Fe, New Mexico, called NCGR, National Center for Genome Resources that was doing a lot of software development. I went there and I worked for about 5 years as a software developer and learned a lot about software development and then kind of came to the conclusion that I wanted to get closer to the science.
[00:21:20] And after many years of putting off going back to graduate school, I decided I really needed to bite the bullet and get my PhD. I was sort of a reluctant academic, I have to say. At the time, I was of the mindset that I could teach myself anything I needed to know. But I finally decided that there was value in getting my PhD and diving back into scientific research. So I left software development, became a full time PhD student and went to Santa Cruz, California, to join David Haussler Laboratory. And from that point on, I have plunged myself into the world of comparative genomics, population genetics, evolutionary modeling and so on.
Grace: [00:22:01] Yeah. And despite that reluctance to going into academic science, you stuck with it after your PhD because you went and became a professor at Cornell and now Cold Spring Harbor. Can you talk a little bit about that decision? How did you change your mind?
Adam: [00:22:16] I actually had not planned to go into academia. I wasn’t sure what I was going to do. But it was an exciting time, the early 2000s for academic computational biology. There were a lot of opportunities emerging, a lot of new departments, new research centers. And in my third year as a PhD student, I had been working with Rasmus Nielsen, who’s now at UC Berkeley, on a book chapter project. He was editing a book and I was writing a chapter with my advisor. And he sent me a job ad at Cornell and I read this job ad and it just sounded like it was written for me. I mean, they were looking for someone who had exactly the sort of expertise I had. And, you know, I had been an undergraduate at Cornell, so I had a lot of affection for the place.
[00:23:07] Coincidentally, I also was considering moving closer to family. My family’s from upstate New York and Cornell is in upstate New York, and I had two small children and we were getting tired of putting them on planes every time we wanted to see family. So I said, what the heck? I’ll apply to this job. I applied and I got the job. So I said, well, you know, I never really planned to be an academic, but this sounds like fun. It sounds like a great opportunity. I love what I’m doing. This is an opportunity to keep doing what I’m doing. And I took the job and never looked back. I’ve really enjoyed academic work since then and have been able to make it work, been able to keep the lab funded and keep publishing papers and keep recruiting students.
[00:23:49] And I think I’ll just keep doing that as long as I can. But it was a different time. I mean, I mentor a lot of my own graduate students and postdocs in their job searches. And I think the job market is much more competitive now than it was then. There was a lot of opportunity in computational biology in the early 2000s, and I benefited from being in the right place at the right time. Sometimes I see the job searches we carry out now, and I wonder if I would have even gotten an interview for some of these jobs.
Grace: [00:24:18] Yeah. Academic science is a very competitive space these days. But there is such a strong need for bioinformaticians and computational biologists. So I mean, there’s a lot of job security in that, but maybe academia is a lot harder.
Adam: [00:24:34] Yeah. I think there are more industry opportunities now than there were at that time. And, you know, the combination of the competitive academic job market and the opportunities in industry means that a lot of young trainees are going into industry, which I think is great. I have a number of recent postdocs from my lab who’ve taken industry jobs and are very happy in them. But, you know, the pendulum tends to swing from one side to another on these things. And I wouldn’t be surprised if in a few years the supply and demand dynamics have changed and things open up in academia again.
Grace: [00:25:08] Certainly. And how have you seen bioinformatics and computational biology as a field evolve in the last few years?
Adam: [00:25:18] One change is just, as I mentioned, a swing toward more activity in research and industry. Another change that I’ve seen in my time in computational biology is just a general shift toward embracing the biology side of the field. They need to ask good biological questions, they need to engage with the data and people not being satisfied with just taking whatever the latest algorithmic or machine learning advances and applying it to a biological data set. I think when I started in the field in the early 2000s, there was a lot of that. There were a lot of people doing computational biology who weren’t that interested in biology and didn’t know that much biology. They were just taking off-the-shelf computational methods and applying them to biological questions in a not very imaginative way.
[00:26:10] And I think over time, people have really realized that in order to do computational biology well, you have to engage with the biology. It’s not enough to just have a computational hammer and look for nails. You have to really think imaginatively about biological questions and how computational methods can be used to address them. And about the interaction between computational methods and experimental methods. About how experimental methods can lead to hypotheses that can be tested computationally and vice versa. Computational methods can generate hypotheses that can be tested experimentally. That feedback between computation and experiment, I think is extremely important and has become more pervasive in the field.
[00:26:54] I think the field is also just bigger and more competitive. Early on, there were really just a handful of people who had this joint background in computer science and biology. And if you were one of those people, then you could sort of write your own job description. It was relatively easy to find a job in the field. Now there are many, many people who have those backgrounds. There are people emerging from PhD programs in bioinformatics and computational biology. There’s a lot more awareness of these questions in biostatistics departments or biophysics departments. It’s just a much more established and competitive academic field.
Grace: [00:27:39] Do you think you would have chosen the same path if you had graduated in bioinformatics today?
Adam: [00:27:46] I really don’t know. I mean, I think I was attracted to the field being so new. And maybe I would feel today that it was too established and I would look for something newer and more niche. But it’s hard for me to say. I also think it’s possible that if I were finishing my PhD now that I would end up in an industry job rather than in an academic job just because of the dynamics of the field at the moment. But it’s always hard to ask these counterfactual questions.
Grace: [00:28:19] True, true. So given the hyper competitive job market for positions and bioinformatics, can you maybe give advice to people who want to enter that field? Like what sorts of skills are most important today?
Adam: [00:28:37] Yeah. I guess, I think it’s true that a graduate from a bioinformatics program who’s interested in this field needs to be fluent in data science and machine learning, basic statistics. But I think that those things are necessary but not sufficient for success in the field. And I think what really will push a person over the edge is also really thinking like a scientist, not just like an engineer. So developing a good taste in problems, developing a nose for questions that can be effectively addressed using computational methods, developing a fluency in the biological technologies and biological questions of interest, the ability to interact closely with experimentalists. I think these are the things that push a person over the edge from being just a data scientist to being a computational biologist who can lead the way in the scientific side of the field.
Grace: [00:29:41] It’s very good advice. So tell me a little bit about you. Like, who is Adam the non-scientists? What do you do outside of research?
Adam: [00:29:49] Well, I have two kids. My daughter just started at the University of Rochester. So I’m adjusting to going from two kids at home to one kid. I live on Long Island in Huntington, New York and live in an old Victorian house and spend a lot of my time fixing that up. And I like to do a lot of cycling and some hiking and spend as much time as I can outdoors. That’s probably a pretty good summary.
Grace: [00:30:16] Yeah. I actually saw in your Twitter that you were planning on heading to Iceland. Did you make it out there?
Adam: Yeah, we did.
Grace: Nice. Yeah, I was just there a couple of weeks ago.
Adam: [00:30:27] Ok. Yeah, we really enjoyed it. We had a fantastic trip. It’s a beautiful place and it felt like the right sort of first trip out of the country after COVID. Relatively safe and controlled.
Grace: [00:30:39] Yeah, that’s excellent. Yeah. Actually, Iceland is a really, probably a very interesting area to study because it’s so isolated and they have a huge dataset. Haven’t they sequenced everybody in Iceland?
Adam: [00:30:52] Yeah. The studies by deCODE have been extremely influential in a variety of different ways, both for association studies and also for studies of rates and patterns of human mutation, which they’re able to trace in great detail, taking advantage of their genealogical databases and pedigrees. So, yeah, it’s been very important in human population genetics. It’s also interesting to look at Iceland from an ecological perspective. I think the largest land mammal was the Arctic Fox in Iceland when Scandinavians arrived and began bringing agricultural animals. So there’s a very short history of large land mammals there. And then there have been interesting events like the introduction of the Icelandic horses and then subsequent genetic isolation, of those horses. And it’s interesting to see the way they have been shaped by the Icelandic landscape and climate, as well as by human selection. But yeah, it’s a fascinating place for questions in evolutionary biology. Certainly.
Grace: [00:31:59] Certainly. And yeah, Iceland horses are really interesting. They had such strict laws that if an Icelandic horse was taken out of Iceland that it couldn’t be brought back into the country. It was just really interesting. And with humans, they have an app. It’s like a dating app where you can check and see if it’s okay to date somebody based on your familial relationship to them.
Adam: [00:32:24] Ah, to see whether you might be related, yeah.
Grace: [00:32:26] Yeah. You put their name in and I think the generally accepted okay line is like fourth cousin or something like that.
Adam: [00:32:33] I see. Well, amazing.
Grace: [00:32:35] Yeah, it’s a really interesting country. Yeah. So as we wrap up the episode, do you have any other any final thoughts on the future of bioinformatics?
Adam: [00:32:46] Well, I guess the future of bioinformatics, I think it’s an open question whether bioinformatics will remain a distinct field. I think that to some degree, the tools of bioinformatics are being absorbed by broader biological sciences. They’re just becoming part of the toolkit of doing biology. And I think in the future, biologists will need to be much more fluent in computational methods and the use of machine learning and the use of powerful computers. And we may not think of it as a distinct field. It may just become part of being trained to do biology. And I think that’s okay. I think often new fields emerge at the interfaces of other fields, and they may or may not remain distinct. They may be absorbed over time, and I think that’s okay. I’m personally very excited to see quantitative methods and computational methods become so central in biology.
[00:33:50] You know, our center at Cold Spring Harbor, it’s called the Science Center for Quantitative Biology. It has begun as kind of a distinct group of investigators doing developing quantitative methods. But increasingly we’re being absorbed by the broader scientific community at Cold Spring Harbor. And the talks when we gather at our annual symposium or some other event to talk about our research. The talks from the quantitative biologists are beginning to involve more experimental biology and more collaboration with experimentalists. And then conversely, the talks by the experimentalists are beginning to incorporate more data analysis and quantitative methods. And I think the logical conclusion of this process is that we probably won’t be a distinct group anymore. We’ll all just be biologists using whatever tools and techniques are available, a combination of experimental and computational tools and techniques. So I guess that’s what I think about the future of the field. It’s dying, and that’s okay.
Grace: [00:34:56] It’s dying, and that’s a good thing. Fantastic. Well, thank you so much for joining me today, Adam. I had a great time listening to your thoughts on evolutionary genetics.
Adam: Yeah, thanks, Grace.
Grant: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Erich Jarvis. Eric is Professor at Rockefeller University and investigator at HHMI. Thanks for joining us, Erich.
Erich: [00:00:11] You’re welcome.
Grant: [00:00:12] So can you tell us about what your lab does?
Erich: [00:00:15] My lab studies the brain basically, but we study more specifically brain regions that control our ability to imitate sounds like that I’m doing now, producing the learned speech. And we do so in non-human animals that have that ability, like parrots and songbirds. These are rare few species in the world that can do this that are shared with us, even though they’re not closely related to us. So this is why parrots can imitate us where a dog can’t. We also have a genome lab that is generating lots of high quality genome data, not only for our neuroscience projects, but for the scientific community broadly, ranging anything from neuroscience to conservation.
Grant: [00:00:53] How do you think the genome effort can inform the neuroscience effort?
Erich: [00:00:57] Most traits are controlled by what’s in the genetic code of your genome and the genes in your genome. And so if we find this specialized trait like spoken language in humans that we don’t find in our closest relatives or like in parrots, we can compare the genome of humans in chimpanzees or parrots and falcons and ask the question, what differs there in their genetic code? And the more species you have with these traits, the higher the N number, which helps you statistically find the specific genetic markers that will develop a brain pathway that allows you to develop a neural circuit for, let’s say, speech or learning how to fly.
Grant: [00:01:38] And how similar are these circuits between humans and birds with vocalization?
Erich: [00:01:43] What’s surprising is that some of the similarities outweigh the differences–when I say we, it’s me and other people in the scientific community that study this not just my lab–but overall, we find that humans and parrots and songbirds have a specialized forebrain circuit in the parts of the brain you would call the cortex and the basal ganglia that you don’t find in any species that cannot imitate sounds or find it to a very rudimentary degree. So the fact that they are there is similar, right. And then they have some connections that are similar. Like the cortical regions in us humans and the equivalent in birds project down to the neurons in the midbrain that control the voice.
[00:02:30] And this connection is direct from the cortex where you don’t find those direct connections in species that can’t imitate. What’s remarkable is that we’re separated from birds by 300 million years of evolution. We have so many species in between that don’t have these circuits. Further, what’s remarkable is that the mammalian brain, including us humans, the cortex is a set of layered neurons like six layers, one on top of another. Whereas in birds, the neurons are clustered in the cortex. But yet, from the layered structured or clustered structure, you get a similar type of neural network that controls speech.
Grant: [00:03:08] So is this a case of convergent evolution?
Erich: [00:03:11] Yes, convergent evolution. It happened more than once in evolution, and it happened in similar way.
Grant: [00:03:16] Do you have any idea how many times this has happened?
Erich: [00:03:20] Yes. So far we think at least five times in mammals, us humans, whales and dolphins that together are called cetaceans, bats, elephants and then another marine mammal, pinnipeds. Those are seals and walruses and so forth. So it’s five out of like thirty two major orders of mammals. So you don’t find it in horses, for example. And amongst birds, we have parrots, hummingbirds and songbirds. So far no reptiles, no fish, no amphibians are known to have this trait.
Grant: [00:03:52] And is the kind of neural solution to the vocalization problem, for example, in mammals always the same?
Erich: [00:04:00] Yeah, it’s similar. So the similarity is that in all the species we’ve looked at so far, which is basically the three bird groups in humans compared to their closest relatives. I would call this spoken language pathway or the vocal learning pathway, I think they’re nearly equivalent that it is embedded inside of a motor pathway that controls learning how to move, like learning how to move the hands, learning how to move other parts of the body. And this motor learning pathway we can find in all vertebrate species that move right. So what we think happened is that the brain pathway for spoken language evolved out of the brain pathways controlling learning how to move and evolved by a brain pathway duplication. And so that’s why we think it’s similar in birds and humans because the ancestral brain regions out of which the speech pathways evolved were already there.
Grant: [00:04:54] How generalizable do you think the circuitry patterns are? Do you think that everything is built on this motor substrate and there are many, many paths to get to vocal learning and this is just the most common based on what was pre-existing?
Erich: [00:05:10] I actually think there are limited paths in which you can get to vocal learning. It’s like the evolution of wings, especially amongst vertebrates. Each time they evolved in bats, birds and ancient flying dinosaurs, the pterosaurs, they evolved on the upper arms, not one on the back or one around the tail and one by the feet and so forth. And the reason why is that there’s an environmental constraint. And that environmental constraint is the center of gravity. So if you’re going to fly in the sky, you need your wings to be near the center of gravity to fly more energetically. I think the same thing is happening to the brain. There’s limited ways you can evolve a circuit that’s going to control, learned sound production, especially through our vocal organs like the larynx.
[00:05:57] And I like to think of the surrounding motor pathway as the arms of the wings, right. And the vocal learning circuit as the wing structures themselves. But does that theme happen multiple times? I think my prediction is yes, no one really knows. But my prediction is that the motor learning circuits for different traits can emerge out of sort of canonical motor learning circuit and then become specialized. The specializations for speech in us humans and songbirds are of two forms. One is this is an advanced sensorimotor integrative behavior. We need to take sound coming through our ears and integrate it with movement of the jaw muscles and the laryngeal muscles and other things that control the sound production.
[00:06:46] That kind of sensory motor integration of auditory input motor output, I think requires its own special formulas–or algorithms in computer science terms–to work with each other. In fact, after vocal learning evolved it looks like only vocal learning species are the ones that can learn how to dance, synchronizing your body movements to rhythmic beats of sounds. And the reason why I think that is the case is because you need to synchronize rapid auditory input with motor output. So you need something special to have the auditory information talk to the motor information. And once that happened, it contaminated the surrounding motor circuits that control not just the larynx, but the hands, the feet.
[00:07:28] And so to have auditory input synchronize our body to rhythmic sounds was a side effect of spoken language. The second specialization I think that happened is the larynx has the fastest firing muscles in the body to produce sound. You need to move those muscles really, really quick to vibrate the air and produce sound or modulate the sounds. And we find that the neurons in the speech circuit for humans and in the vocal learning circuits of these birds are over enriched with molecules that control rapid-fire neurons that control neuroprotection so you don’t kill the neurons just by speaking. That’s another specialization you don’t find in the surrounding motor circuits.
Grant: [00:08:15] You brought up some pretty interesting things there. So you mentioned dancing is maybe almost a side effect. But when I hear dancing in the context of evolution, I think sexual selection, right. Do we know which came first?
Erich: [00:08:27] Yeah, we don’t. But there are a lot of theories out there of what cause vocal learning to evolve, and that includes spoken language and sexual selection is one of them. I can say that I tend to believe that because all the vocal learning species use their learned sounds for mate attraction or in case of non-humans for territorial defense. What we can do it for territorial defense as well, very few of them use it for more abstract semantic communication like we’re doing now to communicate ideas, to communicate concepts. Instead, they sing to attract mates. The more varied the songs or the more you steal sounds from the environment incorporated in songs like in mockingbirds, then more likely you’ll attract the opposite sex.
[00:09:16] Now, you would think semantic abstract communication, referential communication should be the first thing we use it for. What most people don’t realize—and even a lot of scientists–is that referential communication like using a sound to mean a bear, using a sound to mean this object over here, that’s already happening before even spoken language evolved. Like vervet monkeys have an innate repertoire of sounds that through cultural experience, they will learn to use for different predators or food and so forth. But the first thing vocal learners do with their learned sounds is to attract a mate. So that’s why I think it came first.
Grant: [00:09:56] In what species that have vocal learning, do you see rudimentary elements of culture? You’d certainly expect groups of dolphins and so on to learn from one another.
Erich: [00:10:07] Yeah. I’m going to answer the question you’re asking, but I think you might be asking a different question, right. Because all vocal learners culturally transmit their repertoires, whether they’re using it for semantic information or what we call effective information, like to attract mates. So it’s cultural once you have vocal learning. And by being cultural, you get different dialects, like we get different language and so forth. They further geographically you are separated. Now I think the question you’re asking on top of that is what species will culturally transmit information about their vocal repertoire that’s more informative like this sound means predator and so forth. And there are very few species that do that besides humans. Dolphins are thought to be one, corvid songbirds, which are basically crows, and Blue Jays and so forth, thought to be another. And the parrots, like African grey parrots.
Grant: [00:11:05] I guess another really interesting question that this raises is obviously comparative neuroanatomy and comparative genomics are enormously powerful tools to study this. But of course, a complementary approach is classic genetics or human genetics, right. I mean, looking at broken genes, seeing what they do, studying diseases and verbal defects and so on. How complementary have those been and have they pointed in the same direction, since work on FOXP2 and so on?
Erich: [00:11:35] Those two questions basically describe the approach we take in my research broadly. So yes, we use comparative genomics like a natural experiment. The more genomes you sequence out there with species with different traits, the higher probability you will find whatever genetic difference is responsible for those traits. So we sequence genomes and we compare genomes to find out if there’s convergence in all these different species with and without vocal learning. And if we find convergence, then we take those genes that have these convergent genetic changes. And what we’re doing now is taking the genes of a species that learns how to imitate sounds and putting it into the genome of a species that don’t like a mouse. And see if we can induce a change in the brain circuit to get us further along the path to becoming a vocal learner.
[00:12:34] And if we do, that proves that this gene is responsible for contributing to the trait and we can study what it does, its function and so forth. And if it doesn’t, we falsified our hypothesis. Another way of doing it is within one species like humans is you compare different people, and you find a family out there that has a speech disorder, speech deficit. They can do everything else fine, but they have difficulty in learning speech. And then you sequence their genome and you find something that’s different from them compared to all the people who can produce speech normally. And this is exactly what happened, more than a decade ago in the discovery of the FOXP2 gene. This is a gene, it’s a transcription factor, meaning that it regulates how much a gene product is made for other genes in the brain, that controls neural connectivity.
[00:13:30] And when this gene is mutated, it causes people to have difficulty learning how to produce speech. And we put that same mutation in mouse in collaboration with Simon Fischer in Germany. We found that even though these mice are producing mostly innate sounds, like humans they had difficulties switching to the more complex innate sounds that females like to hear in their courtship, right. We find this gene in songbirds and if you block its function in songbirds, just like in humans, it also prevents vocal learning from happening normally.
Grant: [00:14:15] So how do you think about translatability?
Erich: [00:14:18] For a long time, I was hoping that the work we were doing in songbirds–because I started out with songbirds–people would take those discoveries that we found and then try to translate it not only into understanding human knowledge, but to for human health. And I found that people were not doing that. So the first thing we did was find out if we could find convergent parallel changes in human genomes that we see in songbirds for vocal learning. And the answer is yes. Not everything is convergent for the genes but there is a lot of overlap in the genetics. And now what we’re trying to do is to see if we can induce a mouse to become more of a vocal learner species and study things like stuttering or autistic types of speech deficits and these FOXP2 mutations in mice. So that one day those can be translated to helping humans not only understand the disorder better, but to repair it.
Grant: [00:15:24] And how do you think about that in the context of developmental windows, right? Because by the time someone is diagnosed with a speech disorder usually that ship has sailed. But do you think that can be reopened in some way?
Erich: [00:15:37] Because you can have multiple people out there with the similar speech disorder all affecting the same genes. You don’t have to wait for somebody to grow up, to become an adult, to discover it. So by doing a population analysis as opposed to longitudinal analysis, you can get at discoveries quicker. Although waiting 13-14 years for a person to go through puberty and then find out whether they can produce normal speech and so forth is also necessary. That leads to that other part of the question the critical period of years. In all vocal learning species, there is a critical period or what we now call sensitive period, where it is easier to learn how to acquire speech early in life and later in life. And then once you pass puberty, it’s harder, like it’s harder to pick up a new language.
[00:16:24] There are certain set of genes that are turned up or turned down in the brain during those critical periods that close off the ability to learn as much as you can when you’re a child. And there are people who are now trying to switch those genes back on. So that allows you to learn, in this case, spoken language as well as you did when you were a child or at least getting closer to that. And I think that’s going to one day be possible, but it’s going to cause some problems also. And the problem that people don’t appreciate is that. Why can’t I learn as well as when I was younger? Why is it taking me so long to learn how to ride a bike as an adult then when I was a child?
[00:17:04] And the reason is that if your brain is in a very plastic stage where you can mold it and learn a lot quickly, you’re also going to forget quickly. And this is why, sometimes it’s hard to hold on to early childhood memories because you forgot a lot of it because there’s only so much capacity in the brain. If you’re going to learn, sometimes you’re going to erase. And yes, you can erase memories. So if you’re going to reopen the critical period, you better do it for a brief period of time. Learn what you can so you don’t erase a lot.
Grant: [00:17:35] This sounds like a premise for a good novel.
Erich: [00:17:38] That’s right.
Grant: [00:17:39] So what excites you the most about this field? What do you think is most promising, and what do you think are some of the biggest as yet unanswered questions?
Erich: [00:17:51] Yeah. What excites me most? Well, maybe I went into neuroscience because I was interested in something that was mysterious. The brain is one of the organs that we have the least knowledge about, but the biggest investment in or one of the biggest investments. I guess the biggest investment is in cancer broadly, but cancer affects the whole body. And so I’m talking about organ systems. Maybe the heart gets bigger investment. But I’m just fascinated by the fact that we have this kind of behavior or spoken language that allows us to culturally transmit knowledge from one generation to the next. It makes us humans the advanced species that we are. That’s what really fascinates me.
[00:18:32] Jumping many years later from me, starting this lab over 20 years ago. The biggest problem now is I would say mental health. It’s one of those things that’s a real mystery and is hard for us humans to figure out how to repair. It’s not as simple as stitching a wound and fixing it. And I think mental health is a bigger problem in humans than in other species. Not a lot of people think about mental health in other species, but think about your dogs who are home lonely and so forth, right. That can cause mental health disorders. Now people will listen to this, and they won’t want their dogs to be home alone. Get another dog to play with it, right. I think the problem is goes to another gene that’s actually involved or interacts with genes involved in language and spoken language circuits.
[00:19:22] In us humans, we have extra duplication of a gene called SRGAP2, spelled out as SLIT-ROBO GTPase. SLIT is a molecule that binds the receptor called ROBO. When they interact, they influence connections in the brain. Those two genes, SLIT and ROBO are turned up or down in certain brain regions that control speech that we think control the connection to the muscles that control speech. I’m sorry, I’m giving you all these molecular terms that the general audience might know, but I’m going to do it anyway. This GTPase modifies this SLIT-ROBO interaction to influence connections in the brain. It either dampens down its function or enhances its function. And so we humans have an extra copy of this GTPase molecule.
[00:20:16] And what that extra copy does is it inhibits the function of the normal gene. And by inhibiting the function of the normal gene, we slow down brain development in humans compared to other species. So our brain is developing at a slower pace and staying in a more juvenile state throughout adulthood compared to all other mammalian species or vertebrate species. And I think it’s leaving our brains in a more immature state, which then leads to more mental health disorders compared to other species. So some of these mental health disorders, are a consequence of having a more advanced civilization and our brains staying in a more juvenile-state so we can continue to learn throughout life.
Grant: [00:21:04] That’s a fascinating hypothesis. So I guess in that case, you might expect that there could be SRGAP knockouts, assuming its survivable out there walking around. Do you know if anyone’s kind of looked into what those phenotypes look like? Are there people whose brains basically mature much, much faster?
Erich: [00:21:22] That’s an interesting question. Because I have never thought of that, but it’s actually a doable question. And it makes me think, in humans there are either one or two extra copies of this SRGAP2 with people walking around. And it’s making me think, why have two extra copies in some people? And I’m thinking, maybe because one extra copy is not enough, you knock it out and then we become primitive human beings. The additional extra copy is like a safeguard in case one of them goes awry. I don’t know. There’s lots of genome sequencing being done on many people out there nowadays, and this question can be answered, theoretically. It might be difficult because what a lot of people don’t know in the genome world is a lot of the sequencing that’s being done on humans out there is being generated with what we call short reads.
[00:22:15] These are nucleotide base-pair sequences that are like 100 to 150 nucleotides long, whereas the genome is three billion, right. Whenever you have repetitive sequences like the SRGAP2 duplications, with short reads, it’s hard to figure out which copy is which. So you need long reads, long reads are more expensive, like from PacBio, Pacific Biosciences and Oxford Nanopore. And in the genome world when we produce high quality data, we’re using long reads. So they’re really figuring out this question. To answer your question, we’re going to need to sequence the genomes of a lot of people with long reads and then look to see if somebody is missing these extra copies of SRGAP2.
Grant: [00:22:57] That’s interesting. Yeah. I just had a quick look on gnomAD as well. And there are six observed putative loss of function SNV’s when there thirty-eight expected. And there are exactly zero homozygotes out there in all the genome sequencing databases that Nomad aggregates. So even the heterozygote knockouts are pretty rare.
Erich: [00:23:18] Interesting. And to think that this gene is in extra copies in humans. So you would think if we lose it, we will be like all mammals and would be OK, but probably and not.
Grant: [00:23:31] Maybe it’s especially important. So all this kind of leads us almost into our next segment. We’re talking a lot about dancing and its relevance for vocalization, and you were on the verge of going down the path of being a professional dancer, right. Can you tell us more about that?
Erich: [00:23:51] Yeah, that’s right. Actually a lot of my family were into the performing arts, and that’s the direction I was headed in. And a lot of them sang. I was an okay singer, but not as good as the rest of them. So what could I do? I started dancing in dance clubs and so forth as a teenager, back when they allowed teenagers to go to dance clubs. And I started winning dance contests, and I thought, oh, so I can dance. And I auditioned for a high school of performing arts here in New York City and got in. And was on my way to becoming a professional ballet dancer and jazz dancer. But at the end of high school, you know, I was really trying to make that career decision that many teenagers are trying to make. What are you going to do when you graduate high school?
[00:24:32] And I liked science as well. And my mother always said, do something that has an important impact on society. And that stuck in my head and I was choosing dance or science. And I thought as a scientist, I could have a bigger impact on society than I could as a dancer. And I think as a dancer, if you become a well-known dancer, you can have popular influence like anybody in the performing arts like actors and so forth. But as a scientist, I can make a direct impact. So that’s why I chose science. I went to Hunter College here in New York City. I got into a laboratory they were doing bacterial molecular genetics, studying genes that are involved in synthesizing proteins.
[00:25:18] And I found out there that being trained as a dancer trained me to become a scientist. They both require a lot of discipline. They both require creativity. Lots of failure before success. They’re not nine to five jobs. And so, so many things. Basically, I consider being a scientist is also being an artist.
Grant: [00:25:40] It’s fascinating. I never really thought of that before. What do you think doesn’t translate as well beyond the obvious?
Erich: [00:25:47] Being an academic scientist is like running like a small business. You have a lab, you have a people in your lab, you have the staff scientists, and you have the students and so forth. If you’ve got to raise money, your publications or your product. The more you publish, the more likely you’ll get money. So let me backtrack a little bit on that, right. It’s not as ruthless as the business world maybe, but politics and science, they seem to be two different worlds to me, especially at this time. You know, especially in the past four years before the current era where politics seemed to be anti-science.
Grant: [00:26:22] It’s interesting, right. You had a postdoc, who went on to be a pretty prominent politician in Puerto Rico.
Erich: [00:26:28] Yeah, that’s right. Ricky Rossello, my former postdoc, the governor of Puerto Rico. And I guess he’s a scientist, but I guess his politics didn’t mix with science that well, because he stepped down. I very much appreciated his time in the lab. He and I got along very well. I mean, he was a very creative, forward-thinking person. But I do encourage my students and postdocs and others not just to go into academia, but to go into other walks of life and including in politics. And even though I said they don’t mix that well, I do think we need more scientists in politics to help the world.
Grant: [00:27:08] And outside of academia, which is obvious. What paths do you think the PhD route is especially good training for?
Erich: [00:27:16] I think the PhD route is training you for the kind of jobs that require lots of problem solving. You know, let’s say city planning in the business world as well. I think it helps with problem solving because in science, you’re always challenged with a mystery, an unknown that you’ve got to figure out and solve. So maybe being a detective, you know, maybe being a forensic scientist, something like this.
Grant: [00:27:44] And are you glad you did it? I mean, if you had it to do over again, would you be a scientist? Would you be a life scientist? Would you study vocal circuits?
Erich: [00:27:52] Well, I guess if I had to do over again. There are two questions that for me are like: would I come back to the same field? And outside of science, what would I have chosen? And within science, I was always fascinated with the origins of the universe. So I was trying to choose between the origins of the universe and of the brain. So I might choose that or how life began or something like that. Outside of science, I was fascinated with history. So maybe origins of human civilization, origins of culture. So I might have chosen that or maybe I would’ve just stuck with dance. Those are the ones that come to mind. You know, I also considered becoming an astronaut.
[00:28:32] Well, you can’t just say that. You have to competitively apply for that. To me, it’s kind of connected to science, but you know what I mean. I could have been flying planes or something like that. I have no idea. But that’s something I was considering.
Grant: [00:28:46] So it sounds like pretty much big questions like fundamental questions as opposed to kind of applied questions.
Erich: [00:28:54] That’s right. So fundamental questions and that’s what excites me. I’m as excited about being a scientist as I was being a dancer. Because I feel like I’m getting to fundamental principles, I’m doing something that is fun, even though, you know, it’s not fun to try to raise money and get rejected from grants or get your papers rejected and so forth. But hey, you know what, in the interim, I’m having fun.
Grant: [00:29:18] So walk us through your kind of key decisions in your career. You know, in college, you decided to go the life sciences route and then you stayed in New York for your PhD, right?
Erich: [00:29:29] That’s right.
Grant: [00:29:30] And what made you decide neuroscience over, you know, cosmology or something?
Erich: [00:29:35] Yeah. Well, let me give you a little bit more context because actually, I started out wanting to be a magician when I was a young child. And I was emulating Houdini as a teenager, going down different parts in the New York City with my cousin Sean to be tied up and escape from chains and ropes and so forth. And we would figure out how to trick people into believing that something magic happened when it really didn’t happen. And so that kind of actually got me into science. And plus my father was interested in science. Because I started getting tired of trying to trick people. I really wanted to know how things really work. And then, jumping years later into my transition into neuroscience, it was really connected to dance. I felt with dance, the brain controls dancing. It’s something I can hold in my hands. It’s here on Earth. I don’t have to look up to the sky to try to figure it out.
[00:30:30] And I don’t know, it was something I felt more biologically grounded. It was a simple holistic way of thinking, and that’s why I chose neuroscience. But during my undergraduate years at Hunter, I was still kind of undecided in that trajectory. You know, is it going to be something in the biological sciences like neuroscience or is it going to be or is it the universe? So I double majored in biology and in math because if I went into physics and astrophysics, I knew I needed a strong mathematical background so I would have the choice by the time I graduated, which one I was going to do. The mathematics gave me a decent bioinformatics foundation for biology.
[00:31:06] And nowadays, you know, biology is so heavy with big data that that mathematical background is helpful. And then I was going to add one more thing onto that, which is I was toying around with the idea of should I go into activism, into politics? My mother said, do something has an important impact on society. And I did think about politics and so forth. And even within the sciences, me as a person who’s an underrepresented minority of African-American descent and mixed up with lots of other things, I thought about becoming more active in trying to change the culture. And I got asked to do this a lot, but I find that it’s actually like having two jobs.
[00:31:50] And so I figure I want to make a change in society by being a role model, by being an example that as opposed to putting a lot of energy in trying to change policy, which means politics, right. So that’s something else I had to consider that I had to toy around with and make a decision. Including now I was asked to be a director of this or X, Y and Z, because now I’ve made a name for myself in science. Maybe I could or help change. You know, you can’t do it alone. But I’m really still, even at this time in my career I want to make those discoveries about how the world works, how the brain works.
Grant: [00:32:31] What are your thoughts on how people should think about changes that need to happen in the culture of science and so on, right? Are there things out there that you think are especially productive? Things you think are counterproductive? How do you think about it?
Erich: [00:32:45] Yeah. I think what’s counterproductive is for the scientific community as a whole and individual institutions or departments to expect that the folks who are–I don’t want to call them victims, but who are being negatively affected by discrimination and so forth–are the ones who should be given the keys to try to find the solution. You know, I think the solution to what I call society’s racial disease is everybody needs to be get involved, whether or not they are perpetrators or racism or benefit from racial discrimination and so forth, we all need to be part of the solution for it to work.
[00:33:40] The other is that the scientific community needs to do more scientific research on not just social research, right, where you’re looking at behavior only.
But what is it in our human behavior that leads to tribalism in the form of racism? Why does that happen? And is there something genetic about that? Or is there some type of nature versus nurture influence of your social upbringing that leads some people to become white supremacists and others to become activists against those white supremacists? I think there needs to be more hard science that goes into this to find solutions.
Grant: [00:34:07] What do you think are the most important questions to answer along those lines?
Erich: [00:34:12] I think some of the most important ones are where is the overlap or the interaction between tribalism, economics and health? Ok, so I’ll say it again tribalism, economics and health. Because I think those three together are the problems that are contributing to this racial disease where the economics and the health become a problem, right, then the tribalism breaks out. And so where is that tipping point? Then we can find ways to prevent that from happening.
Grant: [00:34:44] What are the ways that you kind of approach this problem that you think differ from maybe how others may?
Erich: [00:34:51] What I think I do that’s differing from many others–I won’t say many others, but enough others–is I think I learned how to have more resilience than I realized. It’s a level of resilience that I don’t think should be necessary, but it was necessary. And that resilience is not only resilience to folks who say or do things that would be purposely discriminatory. Some people really thought I had an unfair advantage with affirmative action programs, for example, or that I am less than or because of the color of my skin, my ethnicity, that I’m not as smart. I’ve met people who think that way in the sciences, right. So you’ve got to have resilience to that. And you’re going to need resilience to implicit bias where someone is saying or doing something, but they don’t realize what they’re saying or doing is discriminatory and they have all good intentions.
[00:35:50] And I say that because I have had enough people in my office, people of color who come to my office crying about something or feeling less than. And I’ll say resilience also to the impostor syndrome. Do I belong here? Do I belong at Rockefeller? I’ve had that question both as a student and interviewing as a faculty member. You’ve got to have resilience to your own imposter syndrome as well.
Grant: [00:36:15] And that’s interesting because at least most recently, right, when you returned to Rockefeller, you were already very, very well established at Duke, right. And even at that time, was that still kind of a something you were dealing with?
Erich: [00:36:27] Yes, it was something that I was dealing with. And it even surprised me when I realized that. And it was a few other famous scientists basically saying that, Erich, you’re lowering yourself too much. And I was surprised because I would rather underestimate what I am, than overestimate. But, some people were saying that I was doing too much of an underestimation and I realized it was because of my minority status that I was doing that to myself.
Grant: [00:36:57] And so how would you encourage earlier career scientists of color?
Erich: [00:37:02] Yeah. I found a way to evaluate myself because like I said, you don’t want to overestimate either. If you get too confident, you might do something stupid in the field and send a grant proposal in that’s really horrible. And then get a reputation that you’re sending in horrible grant proposals for your work. I try to balance my evaluation with my own self-evaluation and what others think. In the beginning phase it’s going to be hard because you’re just starting. So I would say to the younger scientists, and that includes the students, you know, go get some opinions of different faculty members. Don’t depend on one because especially as a person of color, you might find one or two that are going to undervalue you anyway.
Erich: [00:37:46] And if they start saying to you that it’s okay that you’re not going to achieve as much as somebody who’s white, be careful because they might undervalue you. That’s why I say get multiple opinions. Don’t accept everything they say, but listen, understand everything they’re saying and try to improve what you’re doing based upon that knowledge. Later in life as you start to publish papers and so forth, the way I do a self-evaluation is at the end of every year, I see what the citation rate of my papers are, what impact my papers are having on the scientific field. And now I have out of the hundred and sixty or so papers we’ve published over my career even since I was an undergraduate student.
[00:38:33] Something like 20-25 of them, maybe more than that are cited in the top one percent of papers in their field, according to the metrics. So that can’t be because of the color of my skin, you know. So I use that as a metric to answer that question.
Grant: [00:38:53] That’s fascinating. Yeah, we actually, on our blog have compiled, to my knowledge, the only database of H-index distributions at different institutions for biological scientists who involve computational biology in their work. And so you can go and say I’m an assistant professor, associate full professor at this kind of institution, like what the H Index distribution looks like.
Erich: [00:39:21] Wow, that’s good to know. I’ll check it out.
Grant: [00:39:24] Cool. So what do you think has changed about how science is done today from when you were, just entering the field?
Erich: [00:39:34] I was born in 1965, right. And so I entered science when I was eighteen years old, right. So we’re talking early 80s. So when I started out in the early 80s, science was a lone ranger approach. Especially when I got to graduate school, I was taught, you have to figure out everything yourself. And you have to be first or last author on the paper. You know, I mean, this kind of thing is still the same now, but it’s less so than it was before. And I found that that was a Western European model of thinking because I grew up in an African American family, with some Native American culture mixed in there. And of course we were surrounded by European culture around this right. I was thinking of like a Martin Luther King approach, you know, bring everybody together be very collaborative.
[00:40:21] And the advice that I was getting is, I’m doing too much collaboration. That I’m not distinguishing myself enough and so forth. Even from when I was getting tenure and so forth and some other people pulled me aside and whispered, don’t pay attention, you’re doing just fine. So I took this more collaborative approach to science, and now I’m finding that I’m good at it and I’m leading large international consortia for genomics or neuroscience and so forth. And I’m getting credit for it. We produce more papers and we switch around authors on different papers and so forth. And they’re coming out as some of the most highly cited papers in the field.
[00:40:58] And also for me, what really counts is not so much your credit, but the discoveries that are made. And so that was my Malcolm X training, right, which is by any means necessary. So if you need to collaborate, do it. If you need to take the lone ranger Western European approach, do it. What is the necessary approach to make the scientific discoveries? And what I’m finding is that as science diversifies more and as big data in the biological science grows, collaboration is necessary. This lone ranger approach is becoming less and less viable and prevalent.
Grant: [00:41:37] And there seem to be a lot of people who are unhappy with that. You see a lot of complaints about money that goes to the consortia, even though their data are indispensable, right. At the CRO, we rely on GTex, we rely on TCGA, we rely on ENCODE and so on, right. We rely on these big consortia data all the time, every single day.
Erich: [00:42:00] Yeah. I’ve heard people who aren’t into big consortia projects. They just want to have six people in their lab and that’s it. They complain about our consortium projects, about how competitive they are.
Grant: [00:42:14] What do you think is done poorly right now in science? What do you think most needs to be improved?
Erich: [00:42:24] I think two things right. One of the biggest things we poorly do in science is communicate to the public about what we’re doing. I think the public is undereducated in the sciences and sometimes miseducated and purposely so for political reasons. This is something that is changing. When I jump back to the 80s and 90s, we were taught don’t talk to the media that much. Don’t do a podcast, right. Because you’re selling yourself or you could say something wrong. Or somebody could misinterpret it because they’re not a scientist. And therefore, you get a bad reputation in the scientific community. Or you are trying to make a big name for yourself, like Carl Sagan.
[00:43:08] But I think that attitude does us a disservice. I think when it comes to the public good and the scientific community throw the humility out the door, educate the world as to what we’re doing and learn from them as well. So that’s one. And the other is, I think we don’t have a big enough financial investment in the sciences. There is a lot of money going into, you know, I guess the one that get criticize all the time is the military, right. But I think the business world and political world could invest more money into science education, even if those students don’t become scientists. I think it’s going to be better for whatever they go into and for the scientists themselves. Of course, that’s self-serving since I’m a scientist. But I think we can we can do a lot more for the climate turning around what’s going on, climate change for our own health and so forth. And just for basic understanding of the world if we invest it more.
Grant: [00:44:05] Totally agree. And to your first point, the reason I went into the life sciences in the end was actually reading some pop science books that Richard Dawkins had written. And then when I went to grad school and was classmates with the number of people in his department and so on. I learned that within the department, people would complain about him a lot and really it sounded more or less like they were jealous that this guy who gets all this attention. But you know, they’re publishing better and all this stuff. And my thought all along was well, but yeah, OK, maybe his publications aren’t as impactful internally. But big picture, he’s probably having a lot more impact because he’s drawing a lot of people into the field who otherwise may not have gone.
Erich: [00:44:46] Right, exactly. So we need we need people like him.
Grant: [00:44:51] Cool. Do you have any final words for our audience, words of encouragement?
Erich: [00:44:57] You know, pigging backing off this last topic. I feel that what scientists need to learn how to do, including myself, is to translate. Not only to translate from bench to bedside type of translation of discoveries, but translate knowledge from the scientific establishment to the general public. And we don’t have enough good translators. And so it’s good to have you as a translator. But we scientists, we need to learn how to make that vocabulary and grammar and so forth understandable.
Grant: [00:45:34] Totally agree. Well, thank you so much for joining us. It was a lot of fun.
Erich: [00:45:39] You’re welcome.
Grace: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley. I’ll be your host for today’s show. And today I am joined by Mauro Calabrese. Mauro is Associate Professor and Director of Graduate Studies in the Department of Pharmacology at the University of North Carolina, Chapel Hill. Welcome Mauro.
Mauro: [00:00:14] Hi, Grace. Thanks for having me on the podcast.
Grace: [00:00:17] Yeah, we’re happy to have you on. So can you tell us a little bit about your current research on long noncoding RNAs?
Mauro: [00:00:25] Yeah. So broadly speaking, we are trying to understand fundamental mechanisms through which our genome is regulated with the understanding that by defining those mechanisms, we’re going to learn a lot about the basic biology that goes on inside of our bodies. And also we’re going to learn about really important events that give rise to and sustain human disease. So what we study in my lab are these molecules called long noncoding RNAs, unlike a typical messenger RNA that encodes information for protein. These RNAs themselves are sort of the end product. Our genomes, the mammalian genome makes lots and lots of noncoding RNA, like billions of base pairs of it, actually, the majority of which we really don’t know what its function is. It may not have a function or maybe it does.
[00:01:16] And we know from a few really amazing examples, these are genes that people have discovered now upwards of 30 years ago, that at least a subset of long noncoding RNAs play really incredible roles at gene regulation. I can give you a specific example. It’s an essential gene, it’s an RNA, a piece of RNA that’s expressed from the X chromosome in all female mammals. And the function of this RNA is to turn off one X chromosome in every cell, essentially for the life of the organism. So this piece of RNA can transcriptionly silence a hundred and sixty five million base pairs of DNA for 100 years and a billion cell divisions. That’s really incredible. And we don’t fully understand how it works, which is interesting. And then beyond that, there’s this sort of universe of long, noncoding RNAs that get produced by our genome and we have no idea how they work or what they do or whether they have a function at all. And so we use genomics and genetics and cell biology and microscopy and biochemistry and a lot of computational biology to try and understand how long noncoding RNAs regulate gene expression and develop new experimental and computational approaches that will enable others to do the same.
Grace: [00:02:33] I noted that your lab also looks at these long noncoding RNAs in the context of cancer. Can you tell us a little bit about that?
Mauro: [00:02:42] Yeah, we are not so explicitly focused on cancer, but some of the RNAs that we study do play roles in cancers. But I think sort of the biggest area that we hope to impact, like in regard to human disease and cancer, is a really I don’t know if low hanging fruit is the right word, but there’s a lot of really great genomic data in cancers. We know what RNAs are expressed in different types of cancers. And so I think that area is really ripe for discovery. And the biggest roadblock in the field is we really don’t have an understanding of at the basic level, what’s the relationship between the sequence of a noncoding RNA and its function in the cell. For the listeners that know a little bit about proteins, relatively have a much more sophisticated understanding of how protein sequence relates to function to the effect that you could take a protein that’s never been studied before and compare it to all other proteins. And chances are you might find a piece of protein that was similar to a previously studied protein, and that would give you really important clues as to the mechanism of your unstudied protein. So like if a protein has a kinase domain, there’s a good chance it’s a kinase.
[00:03:58] And those types of understood relationships just don’t exist in the long noncoding RNA field. And so the effect of that is that in a disease like cancer, people have looked at this many different ways, thousands of different long noncoding RNAs that are differentially expressed that correlate with different forms of metastasis, that correlate with different cancers that appear to be, if you like, knock down these transcripts, they appear to have therapeutic effects. And I believe clinical trials started last year targeting a few long, noncoding RNAs through Ionis and possibly some other pharmaceutical companies. So these are like there’s RNAs that get expressed in cells. Some of them almost certainly are drug targets. But we have absolutely no idea how to know which ones we should be thinking about targeting and what they might do in the cell. By sort of focusing our efforts on understanding a few noncoding RNAs whose functions are well known, we’re starting to get insight into what are the building blocks that noncoding RNAs use to encode function and ultimately hoping to sort of develop a framework that will allow us to computationally predict or identify regulatory function in essentially any noncoding RNA. And I think cancer is an area that I’m really excited to move into once we have a better handle on the approaches that we’re trying to develop, because I think there’s a lot to discover in that space.
Grace: [00:05:24] Yeah. Cancer, I think is a really great place to start because you have such amazing databases of genetic information and gene expression and all of those sorts of things. And I was going to ask how you ended up in the Department of Pharmacology, because from what it sounds like, a lot of the work you do is in genetics. So can you tell us a little bit about that?
Mauro: [00:05:44] Yeah. Great question. People ask me that all the time. You know, science is so interdisciplinary these days. I think our work certainly fits within pharmacology, and I’ll tell you why in a second. But easily, we could be in a genetics department or a cell biology department or even a biochemistry department. So everything is just cross disciplinary.
But I didn’t necessarily apply specifically to pharmacology departments when I was trying to get a faculty position, but there was a position that was open in this department. And the former chair of the department, who was Gary Johnson at the time, I think sort of recognized the potential that the things that I was just talking about in regards to cancer, like there are these really tantalizing examples of like, OK, we know there’s a few noncoding RNAs that appear to be drivers of metastasis and probably can be targeted with antisense analogues or even small molecules targeting structure of RNAs.
You know, there’s sort of a next wave of therapeutics that are going to involve RNA. And this was true in 2014 when I started the job, which is I think why I got brought into this department. But of course, everyone can appreciate it now, the power of RNA delivery through these nanoparticles that has saved, hundreds of thousands of lives through vaccines. So RNA is not something that historically has been drugged or a drug. But I believe in the future, the next 10 to 20 years, we’re going to see more and more RNA based therapeutics. And so that’s how our research fits into pharmacology.
Grace: [00:07:17] Yeah. Yeah, it’s so fascinating. And I’m excited to see how the technology that is the basis of the mRNA vaccine for COVID-19 is going to influence a lot of science, because, I mean, like you said, historically, it just wasn’t possible because RNA is so unstable.
Mauro: [00:07:33] It’s unstable, it’s big. And I mean, although I guess the technology that Moderna and and Pfizer are taking advantage of, the idea that you could deliver messenger RNA as a therapeutic even in the 80’s. It just has, I think for reasons that you just said, taken some time to take off. But, yeah, it seems like there’s just a lot of new possibilities. And I also am excited to see what happens.
Grace: [00:08:02] Yeah. And there’s also that kind of awareness piece of it. You know, it’s like I know it existed before, but I wasn’t familiar with it. I guess I can’t say that I’m an RNA biologist, though.
Mauro: [00:08:14] Well, yeah. I mean, I’ve been studying RNA for a long time, and I wasn’t familiar with it either. So that doesn’t mean that, maybe I should have been familiar with it, but anyway, like the awareness is a big deal. And now we’re all aware.
Grace: [00:08:28] Yeah, exactly. How was your lab affected by the pandemic when it started?
Mauro: [00:08:33] Yeah, I think it was brutal for us and brutal for many industries, and it continues to be brutal for many industries. But it’s a lot better for us now than it was. I remember it well, I’m sure I’ll always remember it. Like you heard about this virus and it was in China and then you heard like, oh, my gosh, they’re like shutting down essentially all of China with pop up hospitals at Wuhan. And then and it was like getting closer. Then in the beginning of March, it was like, oh, maybe it’s going to come here. A week later, it was like, okay, it’s here.
There were a few days where a UNC hadn’t shut down, but we knew it was coming because everything else had shut down. So my lab shut down like a day earlier and we’re just like, we can’t handle this. It’s definitely happening. We’re just going to close everything. And so, yeah, we just stopped research, just pulled the plug on it. And we were out of the lab for three months. But UNC opened up in June of 2020 with masks at 50 percent capacity, and that enabled us to at least get back into the lab.
But it was just a mentally extremely challenging year. People that I know, they lost loved ones. Many people experienced extreme forms of mental stress, and we weren’t spared from that in the lab. So as a father of two young kids and my wife also has a job, we had zero child care. So how do you do that? Like not very well. You know, it was like really hard on everybody, it was very hard on my family because our kids got pulled out of school and my son is like six months old and my daughter is three and a half and somebody needs to pay attention to them because they’re kids and they need that. So it was a wreck.
[00:10:13] We were exceedingly careful in the lab and we managed to make some progress during the year, but it was really limited relative to what we would have expected. And things began to come online once we all got vaccinated. But it’s still a challenge. I mean, I think we need to be wearing masks at work. I’m in a private office, so I’m not wearing a mask right now. But everyone that’s in the lab wears masks all day. So I think people do more work at home than they would otherwise, which I think is fair. You know, your face gets hot. So it’s a fact.
So we’re still like kind of a little fragmented as a lab because we’re doing more remote work than we were used to. The remoteness of Zoom is enabling on one level, but it’s also stifling. And I think it limits the creativity that we get from being all together for a full eight hours a day. And so we still haven’t gotten that back. And I don’t know whether maybe in a year or two, when we stop having to wear masks at work, we’re making discoveries, we’re making good progress. We’re figuring things out that are interesting and important. But I don’t think we’re operating at the same level that we were before. But we’re close.
Grace: [00:11:31] It’s good to hear that you guys are rebounding a little bit now, although it does make me worried, looking at a lot of the numbers, especially around North Carolina. I have a lot of friends going back to school and everything is increasing again. And it does make you a little nervous.
Mauro: [00:11:47] It does. You know, I think I totally agree. I guess the flip side is that UNC research operations did a pretty good job last year. Of course, the Delta variant is different than it was: it’s far more contagious. But I don’t think there was like a single case where they could say there was workplace transmission at UNC at all last year. Like in all the school of medicine, people are wearing masks, they’re adhering to it. And over the last year, I think objectively it was a very safe place to work.
And so even though the Delta variant is like 10 times as contagious as the original coronavirus, I think it still remains like a pretty safe place to work. For now, fortunately, most of us are being vaccinated. And so I’m not immune to getting COVID, but it’s less likely that it’ll be severe. And so this time, even though cases are similarly high now and probably will be higher than they were in January of last year soon, I don’t think a lot of that is happening at work at UNC or even in classes. I think it’s the personal spaces where people are relaxing and interacting closely where the bulk of the transmission is.
Grace: [00:12:58] Yeah. Yeah, very true. Very true. Those darn college students partying. Yeah, it’s been a hard year, hard year and a half. It’s been interesting to see how the pandemic has sped up science in some ways, but also how it’s slowed science down in other ways. It’ll be interesting to see how it works out going forward.
Mauro: [00:13:22] Yeah, but I think I’ve always enjoyed what I do, and I don’t necessarily do it for the benefit of public health. I just I think I’m interested in it. But I do firmly believe that there’s a strong benefit to humankind, as well as an economic benefit to research. And I think the pandemic on one level is inspiring for what you just said/ Being able to develop these vaccines in a record amount of time, and they’re safe and they’re highly effective. And all the amazing research that’s going on in regard to the coronavirus, I think is really underscored the necessity for science and its power.
Grace: [00:14:00] It’s been great to see all of the collaborative efforts, industry and government and universities working all together. And it’s kind of like a world war. You know, it’s like everyone, all the industries, all the people are coming together to fight, not an enemy, but a virus.
Mauro: [00:14:17] Yeah, I think that’s when we’re at our best, when we come together.
Grace: [00:14:21] Yeah. So tell me a little bit about how you got into RNA biology and how you became interested in these long noncoding RNAs.
Mauro: [00:14:31] Happy to do so. It’s a little bit by chance. You know, you make these decisions over the course of your life and then the next decision follows. So I guess I was interested in gene regulation in college, like I found it to be like just really interesting. There’s like all this DNA and it gets read in different ways in different cells at different times. And that was like really interesting to me. And then I went to graduate school at MIT and when you’re first year of graduate student, you rotate in different labs and pretty much by dumb luck I rotated in Phil Sharp’s lab.
Phil Sharp won the Nobel Prize in 1991 for the discovery of splicing and has just made all these really incredible contributions to our understanding of biology and RNA. And I knew I was interested in mammalian gene regulation. So I rotated in Phil’s lab and it was really great. So I ended up joining that lab. And so that sort of left me cultivate my interest in RNA. And when I was in Phil’s lab, we were studying micro RNAs, which are short, twenty one to twenty three nucleotides long, but they’re like genes. Some of them are extremely conserved, incredibly, even though they’re teeny, tiny and short. And they do amazing things like RNA tends to do.
[00:15:51] So we were studying microRNAs and then I became aware of some of the work that was done by researchers, in at that point, the nascent, long noncoding RNA field. So this RNA, I’m not sure I mentioned it by name, but I referenced it very loosely at the beginning of our conversation called Xist. The function of this RNA is to turn off the whole X chromosome. There were a lot of really interesting breakthrough studies on this Xist long noncoding RNA when I was a graduate student, and those were pretty cool to me. There were also independently some breakthrough studies on a different long noncoding RNA that has a function that’s analogous to Xist. All these RNAs have weird names. They’re just gene names. But this is called Air. Actually, we study it in my lab now. It is this amazingly strange RNA. It’s huge. It’s ninety thousand nucleotides long, which is like crazy. It’s un-spliced, highly unstable. But the function is to silence gene expression over about one third of mouse chromosome 17. And it’s not even conserved outside of rodents. And so it’s got this incredible biological activity that has appeared to have evolved very recently in evolution.
[00:17:04] Anyway, some really amazing work from a researcher in Austria who has unfortunately since passed away, Denise Barlowe. But I remember reading some of her papers, as well as these papers from the Xist field, while I was a graduate student at Phil’s lab they piqued my interest. And ultimately I decided to pursue that area of research for postdoc, I thought it was really interesting. And so I did that. I was a postdoc here at UNC and Terry Magnuson’s lab in the genetics department. I wasn’t necessarily set on starting my own lab, but I was just doing what I thought was the next best thing, which was to pursue a postdoc in an area that I found to be interesting. And I still found it to be interesting at the end of my postdoc. And I had enough success to convince the pharmacology department to hire me. And so that sort of brings us to the present day, I guess is an abridged version.
Grace: [00:17:55] Yeah. In your lab, you have such a variety of approaches and computational biology, microscopy. How did all of that come together?
Mauro: [00:18:05] Yeah. I think you just sort of do what needs to be done and get into it a little bit at a time. I was a graduate student right at the inflection point of the sequencing revolution. When I started graduate school we were sequencing micro RNAs by hand. We would like take these little bacterial colonies and after like three weeks of work, you would get three hundred sequences back. And then all of a sudden there was this instrument that came online from a company that was bought by Roche and then I think has gone under. But all of a sudden, instead of for three weeks of work, instead of getting three hundred sequences, you would get ten thousand sequences, 300 times more. And then like a few months later, instead of ten thousand sequences, you could easily get 10 million sequences. So like in the span of nine months, it was like the genome revolution. And so I learned computational biology for that.
Then I was a postdoc in a genetics lab. And questions arise and you want to figure out what are the most important questions to answer and what are the things I need to understand to answer these questions? And if they’re interesting enough and if they’re within your capacity, then you learn the skills necessary to answer them. Like I’m not a math person. So structural biology is probably something I would never be able to pick up myself. But you just sort of over time, I think, figure stuff out.
Grace: [00:19:25] Yeah, certainly. So we work a lot with biotech companies and generally, people who have such varied experience and who have a lot of random skills tend to do very well in biotech companies. Have you ever considered joining a biotech or starting biotech?
Mauro: [00:19:42] Yeah, I have considered it. The benefit of working in an academic lab as long as you can convince somebody that it’s important, they should fund your research. You can do what you find to be the most interesting. And I think we’ve sort of like fallen into this path. I think, the computational objectives that we have, like we’ve started to make some insight into how we can computationally predict the function of a noncoding RNA. And I don’t think we’ve really finished that work yet.
So I’d like us to get there. And I think when we get there, it’s going to be pretty exciting. But I’m not sure that anything that we have done so far or maybe that we will do is–what’s the word–is like IP-able, you know, like it’s information. We’re trying to figure out how to figure things out. And when we figure that out, we’re going to publish it and make it public.
So I think some people’s research, especially in pharmacology is good for biotech. Like we have this protein that we study with a mutation and we’re developing a small molecule to fit in this pocket. And you can then spin off that molecule in IP. We haven’t quite done that work, but I think about it, many of my friends actually from graduate school went into industry, and now they’re like doing all these amazing things at high levels in these biotech companies. And I’m like, wow, that would have been cool. But this was my path. And it’s been great so far. But I’m not averse to doing work in the biotech sphere as well.
Grace: [00:21:04] Yeah. Your path hasn’t ended yet, so you’ve still got time. And if IP-able isn’t a word that’s used in biotech, I think they should add it to the biotech dictionary. What skills do you think are most important for scientists today?
Mauro: [00:21:23] You know, I think a lot of them are skills that are transferable to everything: you know, focus. This is maybe a weird thing to say, but we live in a very distracted time, especially during the pandemic. I mean, the news cycle is like from one crisis to next. And there’s all these things that take our focus away, like text messages and email and different forms of social media. And I think to really do excellent work, somehow you need to put your blinders on and think deeply about stuff, especially if you’re trying to make discoveries. To figure out things that we don’t know yet really takes a lot of ruminating and deep thoughts. So an ability to focus, a strong interest in the work that you do, because that’s going to give you those insights. You’re going to be thinking about your work and you’re going to have the insights, but if you don’t find it so interesting, then you’re not really going to be thinking about it that much. And that’s fine. But it’s not great for research.
[00:22:29] And then I think an ability to communicate. So it’s not enough. It’s never enough, I think, to work in a silos. Our best advances as humankind have come from collaborative efforts. And I think it’s probably always been true, but it’s definitely true today. Especially the more complex a project is, the more likely it is that you’ll need to rely on multiple people to achieve an objective. And at The Bioinformatics CRO–I’m sure you understand this–you have computational biologists and you have biologists who don’t understand computational and communication is really key. Like if you have people that communicate well, then the speed of the project is like orders of magnitude, greater than two people that are not communicating well. So I find that to be extremely true with our work as well. Those are the big ones, I think focus, interest, and an ability to communicate. If you can do those three things, you can pretty much do anything.
Grace: [00:23:30] So the things that you’ve mentioned are traditionally very difficult to learn. So the communication, like team management, that’s very difficult and focus I mean, it’s impossible. I mean, really. So what are some ways that people can cultivate these skills?
Mauro: [00:23:45] Yeah, and I think that’s a great question. And I do think we all need to cultivate them. They’re not necessarily taught in class and they’re not really taught in society either. The way that the news communicates to us is not really the way that we should communicate to each other. The way that like Facebook inundates us with distractions is not healthy. So you really have to like steel your mind against the forces of the world that we live in. To varying success, I have been able to do this. I mean, I think there’s times, especially last year during the election where I was unable to focus. Actually, much of last year, I was unable to focus for a lot of reasons, and I wasn’t alone.
But I think dedicating a time to shut off external communication via text message, take your Apple watch off, don’t listen to music, turn off Slack. Setting aside time to make sure that you do that, I think that’s what I do. Whether or not that’s a transferable skill, I don’t know. But I find that to be very helpful. The times when I’m able to not check my email every five seconds and not look at the news every five seconds. Like those are the days that I feel the best about the work that I’ve done. And those are the days that I’ve accomplished the most.
[00:24:59] I think, similarly in terms of communication, that’s something that I’ve learned. Writing in particular in my job is essential, and it’s not anything that I was formally trained in. I took like an English class in high school, and I learned that a paragraph has a topic sentence and three sentences after that support the topic, which is true sometimes, but that’s not actually the best way to write most of the time. But there is a structure and understanding what people expect and how to include it in a document and how to write words that are going to give you the highest probability of success communicating to your audience. That’s something I’ve read about, I mean, by necessity. If I can’t write, I don’t have money to fund my research. So like anything, essentially, you have to sort of set a goal and work towards it in practice and seek out resources that can help you. And in terms of communication, if you’re a natural communicator, then you have a leg up. But if it’s something that you want to improve, there’s leadership courses, there’s books that people would recommend, I think, in practice and engage and self evaluate. And over time, you will improve.
Grace: [00:26:11] So as we wrap up this episode, I always like to ask our guests what advice they have for early career scientists or graduate student postdocs who are looking to go into a similar path that you’re following. What are some of the lessons that you’ve learned and words of wisdom that you might share with younger scientists?
Mauro: [00:26:33] Yeah. You know, the thing I tell many of the graduate students I interact with and I would share here, is to do your best to figure out what it is that you are interested in. And if you’re going to join a lab or do a postdoc or pick a research project or join a company that you find interesting and that you believe in the mission. Like this work is interesting to me and I believe that it’s worthwhile. And I feel like that’s like the number one thing.
I couldn’t tell you why I’m interested in the work that I do. I just find it interesting and I don’t think I need to justify it. It’s interesting to me, and I can tell you why it’s important. And so I think that’s true. I suspect for other people, like you don’t have to justify it to anybody. It’s meaningful to you and just tap into that feeling, like don’t overthink it. But if you find yourself feeling like, it’s a slog–it’s bound to happen–just sort of reflect on that. And when it comes time to make another career decision, think about the things that you’ve done and what you enjoyed and what you didn’t enjoy. I think follow your interests as best as you can and that’s going to get you really far because it’s the more interested you are, the more excited you are, the better work you’re going to do, the harder you’re going to work and the greater successes that you’ll have.
Grace: [00:27:53] Excellent advice. So thank you so much Mauro for coming on the podcast and sharing your wisdom and your experiences. I had a really great time talking with you today.
Mauro: [00:28:02] Yeah, Grace. It was really fun.
Grant: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Damian Kao. Damian is the Chief Operating Officer at Basepaws. Welcome.
Damian: [00:00:10] Thanks, Grant. Happy to be here.
Grant: [00:00:11] Happy to have you. So, can you tell us about what Basepaws is? What do you do there?
Damian: [00:00:15] Yeah. So, Basepaws started out as a kind of a 23andMe for cats type of business. I think a good analogy would be Embark for dogs. However, I think in recent years we’ve been kind of pivoting into more of a pet health care angle. So, the goal of Basepaws is really to utilize as much genomics data as we can for our pets to profile their health and try to predict health outcomes, basically. So, preventive medicine in some sense. Right? So, being able to predict whether your cat or dog will have a disease before it happens, so we can save you that huge veterinary bill that you might have down the line.
Grant: [00:00:56] And you mentioned dog. Are you guys moving into dogs?
Damian: [00:00:59] Dog is a very competitive market, as you may know. Embark actually recently just got a lot of funding from SoftBank. I think they’re valued at 700 million dollars now. So, that’s obviously a big competitor. We are thinking about other animals. It’s a bit intimidating to go into the dog space so we might try to go in there with some other products. So, for example, we just came out with a dental/oral microbiome product, actually. Where we give you a risk score on whether your cat will develop periodontal disease or some other health issues. So, we might try to enter the dog market with that type of product.
Grant: [00:01:39] How actionable are those reports? So, if you’re a cat owner and you find your cat is in the 90th percentile of risk for periodontal disease, do you brush their teeth? The way you’re supposed to, right. I don’t think most cat owners do routinely brush their cat’s teeth.
Damian: [00:01:55] Most pet owners in general don’t brush their cat’s teeth, right? Or their dog’s teeth. It’s, as you may imagine, very difficult to brush them.
Grant: Getting your hands anywhere near their mouth is this just asking for trouble I think.
Damian: [00:02:16] Yeah, I think the goal is to at least let the cat owners around the world know that this is an issue. Periodontal disease in cats is a huge issue and huge vet bill. So, there are a series of products recommended by veterinary council that is not always active brushing. Maybe you can give them chews or water additives or certain types of food that might prevent those types of problems. And that’s what we are advising people to do right now.
Grant: [00:02:37] And what kind of dynamic range do you have? You know, so a cat at either extreme, what kind of difference in rescue you’re looking at?
Damian: [00:02:45] So, let me talk a little bit about the bioinformatics of that, I guess. Our product is really just a swab that we provide to our customers. They swab their cat’s mouth, they send it back to us. And from the very beginning, we’ve noticed that after sequencing the DNA up to 10 to 15 percent of the DNA is not cats. So, what could that be? It’s just whatever is in the cat’s mouth. That could actually be residual food. That could be the oral microbiome flora. That has always been really exciting and interesting for us. And we didn’t really act upon it until maybe a couple of months or a half a year ago. But what we did find is we have a cohort of at least 30000 cats. So, 30000 oral microbiome samples. And then we have good phenotype data that tells us whether the cat is on a certain type of diet, whether they’re indoor or outdoor cats or whether they have any systemic diseases. So, this became a really interesting thing for us, because now we can try to look at a microbiome profile and correlate it with all this phenotypic data that we’ve gathered. Dental disease is obviously the most direct thing that we can look at.
[00:03:53] So, in our cohort of cats, we have hundreds of cats with periodontal disease and cats with other oral issues. And we’ve found that certain populations of microbes in combination seems to be correlated to these disease states. We don’t do 16S-rRNA seq. We are doing the WGS. So, we are looking at everything that’s in the mouth. So, we do see fungus. We do see bacteria. We do see some archaea. And we actually see a lot of residual food things, too. We pick up on plants, maybe a spider that the cat ate while it was outside. So, we do pick up on those type of things, too.
Grant: [00:04:30] Do you have a longitudinal component to your data? So you can certainly imagine if a cat has active periodontal disease, that their oral microbiome probably looks pretty different at that point. It would be super interesting if you had good predictive power years in advance to say like this cat is at high risk.
Damian: [00:04:48] Yeah. So, there are a series of studies that we’re actually conducting right now with various clinics all over the country that are gathering these samples for us. So, we’re working with dental specialty clinics that are gathering samples before and after examination, for example, and maybe also follow up weeks or months after examination. It’s through these samples that we want to start looking at what the predictive power really could be.
But the longitudinal question is really interesting for us, too, because we are seeing signal not just for oral or dental issues. We are seeing interesting signals for some systemic diseases, too. And this is reported in literature somewhat. For example, Ckd, chronic kidney disease in cats. There does seem to be some link between that and periodontal disease. And in our data set, we are seeing a signal coming out for chronic kidney disease via our microbiome dataset. We are also seeing some signal for other autoimmune diseases or even allergies, which is kind of interesting. It’s hard to kind of tease all of these things apart, though, right? So, getting good high resolution phenotypic data, I think is really our next big thing.
Grant: [00:06:01] How careful do you have to be about how you ask those questions, right? Because you can imagine if you’re asking about behavioral traits, the same pet owner may describe their cat’s behavior in very different ways.
Damian: [00:06:13] There is definitely an art to asking questions from our customers, and that’s something that we’ve had a lot of trial and error on. I think the best thing to do is to ask them the same question in multiple ways, many times, spread across multiple time points. And that’s really how you can increase the confidence of those answers. That’s something we’re building into our account system right now. The whole idea is we would have a question bank in our backend where we might ask the same question 10 different times, and we would present those questions to the customer at various times. And hopefully they will be consistent in their responses.
Grant: [00:06:51] Have you ever looked at what’s predictive of inconsistency, right? Do you have certain respondents who are just consistently inconsistent?
Damian: [00:07:00] No, we have not looked at that. But that is actually really interesting. Yeah, we should definitely do that.
Grant: [00:07:07] It would be super crazy if you found something different about the cats, right? Maybe certain cat breeds are associated, you know. But this brings up an interesting point, right? Most of the people we have on the podcast work for companies that are essentially B2B or they’re just drug development companies. What are your experiences with running a B2C Science company? Because generally, in tech, people always talk about B2C’s having a lot more challenges.
Damian: [00:07:35] I have never worked at a B2B, so I can’t really make that comparison. However, I could say that B2C has definitely been a huge learning experience for me. So, I would say that you have to balance satisfying your customers and maintaining the scientific integrity of your work. And that’s always difficult because they’re not always in sync.
Grant: [00:07:58] People always want more information. Right? Like what kind of wine do I like based on my DNA kind of thing.
Damian: [00:08:04] Yeah. Because it’s very easy to kind of stretch the science to accommodate what people want to see. And there’s a limit to that, right? It’s always a push and pull, to be honest. So, at Basepaws I’m the COO. I run kind of the science and the tech and the lab operations. And then we have the other half of the company, which is run by Anna, which deals with customers and acquisition and marketing and all that. And I find that it’s a great push and pull between me and her, because I tend to always think I need to be crazy rigorous. We can’t show anything. But she’s always like, if it’s interesting, as long as we explain it well, we should be able to show to our customers. Because we have to trust that they’re smart enough to understand not to take this at face value. So, I think there’s always this push and pull between kind of the marketing side of things, that B2C side of things, and the science side of things. And that’s been a really good experience for me personally, I think.
Grant: [00:08:59] What have been the biggest surprises for you in your Basepaws journey?
Damian: [00:09:04] I think part of the reason why I decided to not stay in academia and go industry was… There are multiple reasons why. I’m sure most graduating PhDs or postdocs will understand the reasons why. But I guess the biggest epiphany I found was that I enjoy building things rather than answering questions. And I think that’s kind of the biggest epiphany I had doing Basepaws. Building up a Lab, setting up these processes, seeing things happen and producing a product is extremely enjoyable.
Grant: [00:09:36] What elements of that do you think you are prepared for through your education and training? And what did you really have to pick up on the job?
Damian: [00:09:44] The advice I can give to any students going into their PhD is learn transferable skills. You’re not there to learn a very specific lab technique that only five labs in the world can do. You’re there to learn how to think. You’re there to learn how to pick up a new skill when it’s presented to you. So, learn those type of skills, don’t memorize concepts, don’t learn some niche technique. I think bioinformatics is very much a field where you don’t learn specific techniques because there aren’t really any standardized techniques for bioinformatics. It’s kind of a Wild West still in some sense. I feel like you really have to understand algorithms very well, data structures, etc. I think all of these things that you have to learn through your bioinformatics, PhD helps you in industry. It definitely helped me, sitting in my lab knowing how to analyze the data that comes out of the pipelines I set up. What I had to learn on a job is really more management skills I would say. In the lab I manage a couple of technicians. I manage the tech team. So, how you get all these different people to kind of share your vision and execute on that vision, that’s very difficult. It’s not something you learned during your PhD. And I had to learn that on the job.
Grant: [00:10:59] Let’s talk about you. So, when you were a kid, did you wanted to be a scientist?
Damian: [00:11:05] I was a very unremarkable student. As a kid, I would say in university, I changed my majors a lot. I didn’t really know that I wanted to be a scientist. I actually liked graphic design. I was a graphic designer in high school.
Grant: [00:11:17] Have you ever used that skill? Has that been one of those transferable skills that came in handy?
Damian: [00:11:21] Actually, the current Basepaws logo, I designed it all and I coded it all. So, there were some transferable skills there. I did film studies for a little bit in college, and I decided to go in genetics because I also studied a lot programming in high school. So, I made my own websites that type of thing. Back then, in the late 90s, that’s what a lot of computer geeks did. And of course, I did that. So, those programming skills led to my interest in genetics, because there are those obvious parallels between programming and genetics. After learning more about genetics, you realize those parallels don’t really apply that much. But I think that’s what kind of made me want to become a scientist: through computer science.
Grant: [00:12:04] You ended up landing on genetics at UC Davis. What did you do after that?
Damian: [00:12:09] I actually did not think about going into bioinformatics. I wanted to do lab work. So, I was a lab technician for a couple of years, working on drosophila. I did a lot of molecular work, did a lot of injecting stuff into Drosophila eggs to make transgenic lines and all these things. After a while honestly, I was a bit lost for a little bit, just didn’t know what to do. At some point, I decided that I needed a change of scenery. So, I said, I should do a PhD. Let’s go to another country and do it. So, I went to U.K. and did my PhD there.
Grant: [00:12:43] What attracted you to the UK?
Damian: [00:12:44] It was a different country. That was a main reason. You know, I felt like I’d been in California for so long. I feel like when you’re in one place for too long, you lose opportunity to kind of reinvent yourself because you’re surrounded by all the things that you know. Going to the UK allowed me to kind of reinvent myself, I guess, to maybe see myself in different lights. And it was kind of there that I developed that state of mind where I wanted to do a PhD. I want to do all of these things. I was able to, I guess, be more aggressive about my goals in some sense.
Grant: [00:13:16] And what are your thoughts on the British PhD training system as compared to the American system?
Damian: [00:13:23] I mean, there’s pros and cons to both. I think the biggest pro for me on a very practical level is that you’re done in four years, five years, max. After that, it’s really looked down upon if you’re not done. The universities I think lose funding if they have PhD students for longer than a certain amount of time. So, they really are incentivized to get them finished. So, that’s practically that’s one of the biggest con. And then personally, the UK is extremely strong in bioinformatics, as you probably know. So, my supervisor and a couple other PI’s around the UK would yearly set up a genomics conference that I would be a part of where I get to meet all the other great bioinformaticians there. And that was a real, really good opportunity for me to connect with others and learn as much as I can about the entire field.
Grant: [00:14:10] And you must have liked it a lot, because after you finished your PhD, you stayed.
Damian: [00:14:14] Yeah, I stayed for a couple of years. Yeah. After the PhD, I thought about staying academia. I worked on some genome assemblies. I worked on some transcriptomic stuff. So, doing a postdoc in Oxford was very eye-opening to me because there are a lot of really, really smart people. And you just learn so many things, new things and interesting things every day. I was in a zoology department where you got to look at other people’s research and a variety of animals and biology that’s out there. I think that’s one of the biggest reasons why I stay around for a couple of years.
Grant: [00:14:49] Why did you leave?
Damian: [00:14:51] I left because I decided I didn’t want to stay in academia and if I didn’t want to stay in academia, I might as well go home. And I feel like the startup culture in the US, especially California, is just more vibrant.
Grant: [00:15:06] And when you returned to the U.S., you started HHMI, right?
Damian: [00:15:10] Yeah. So, through some work that I did in Oxford, I became a consultant at HHMI where I worked on some single cell transcriptomic projects and some genome assembly projects, and that was a really cool experience for me, because Janelia is just a great place to work. They basically built this entire compound where you can live and everyone just loves science and does science. It’s great.
Grant: [00:15:37] Bit of a scientific monastery, right? Isolated and such.
Damian: [00:15:40] Yeah. Monastery. That’s a really good word for it. Yeah.
Grant: [00:15:44] What was your thinking in leaving there? Was it basically you really wanted to do the startup thing? And what’s the story there? How did you and Anna meet?
Damian: [00:15:52] Yeah, I really wanted to do start up things. So, I was in California remote working for HHMI. I just felt like doing the same type of analysis on the same data sets is just going to be boring for me. And I really wanted to get into the startup world and see how that works. My mom was entrepreneur in Taiwan and she’s a successful businesswoman there. And I’ve always wanted to see what that was like. I think at the end of day, I wanted to work for myself. Didn’t want to work for someone else. So, my wife did PhD with me at the same place. We came to California together. We both are kind of into this whole startup scene. So, we put out some feelers.
Grant: [00:16:32] Is she also American or?
Damian: [00:16:35] She’s actually a Bulgarian. She did her PhD in the same lab, got together there, got married in the UK. So, we put out some feelers in the startup world. I had some NGS experience. Anna was in need of someone with that experience. So we met at a coffee shop one day, talked about our respective skills and our interests, and I thought that we were a perfect fit to do this company together. So, I joined and I set up the lab, did all the pipelines, and we went from there.
Grant: [00:17:10] What were your biggest challenges when you got started with Basepaws?
Damian: [00:17:13] I think something that carries over from academia and into the first few years of industry is imposter syndrome, I think a lot of people have that. The first year at Basepaws you know, I’m the scientist. I know the science. However, the business side was not something I’d experienced. And so whenever I had to make a business decision, I would always second guess myself. And I think it stems from that imposter syndrome. But I think at the end of the day, what I learned is that business decisions are like any scientific decision. You get a lot of data, you analyze it and you make a decision. There’s nothing special about it. So, getting over that imposter syndrome and having more confidence in the decisions that you’re making. Yeah, I think that’s what I learned.
Grant: [00:18:01] Do you think you’ll ever leave the startup world?
Damian: [00:18:04] Well, I just had a kid who’s one year old, so it’s a Covid baby. If I ever leave the startup world, it will be because it’s becoming too crazy and I can’t spend enough time with my kid. That’s probably going to be the reason.
Grant: [00:18:17] It is tough. What advice would you have for people considering doing the startup thing? Like we have a number of clients who started their company after, spending a long career in big pharma, where everything was taken care of for them. And they would focus on their one area and they were the expert on that. But, they could access any kind of expertise they wanted just by going down the hall. And obviously that’s not the case at the start up. Right? So, what advice would you have for people like that considering making the jump?
Damian: [00:18:51] I think hugely depends on what your business is. I can tell you from a kind of B2C point of view that scaling up is very, very hard, especially in a biological context. It’s very easy to get an assay or a product or a test to work a couple of times, but to get it working consistently for thousands or tens of thousands of times, that’s extremely hard. So depending on what industry you’re in, if you’re in an industry where you have to do that thousands and tens of thousands of times, you have to think about that. And you have to think about the long term cost of what you’re doing, because it’s also very easy to over-optimize and over-engineer in the beginning, buy all this fancy equipment that you just never use because there’s simpler solutions out there. So, I would say worry about scaling up if that’s the industry you’re in.
Grant: [00:19:44] It brings in a very interesting point. The funding climate right now for human therapeutics is quite hot. How well does that translate to pet health?
Damian: [00:19:56] Pet health is actually one of the industries that grew a lot last year during Covid and is steadily growing, and because of that, there are actually plenty of investors interested. I think the problem that stops an investor from actually putting in the money at the end, though, is that there haven’t really been any big exits that they can see in this sector. So, I feel like maybe that’s what’s holding it back a little bit. So, I think there’s a lot of money pouring in. And because most of these investors are looking for a relatively quick turnarounds, they’re a little bit more hesitant to put their money in.
Grant: [00:20:30] Can you tell us about how you use bioinformatics at Basepaws?
Damian: [00:20:35] Yeah, so just a brief overview of what we do. I think we’re actually one of the few companies that use NGS for this type of a product. I mean, as many of you may know, 23andme and Embark and these other companies, they all use microarrays, which relies on an already good existing resource for that organism, like humans and dogs. When we first started Basepaws, a really good feline genome was produced actually a couple of years back. So, we were able to take advantage of that. However, in terms of whole genome data sets for cats, I think when we first started, there were less than 100 in NCBI. So, we had to sequence a lot of those things ourselves, build up our own reference panel, our own imputation panel.
Grant: [00:21:20] So, that you could create a crazy cat assembly. This one, if you have 30000 cats at this point.
Damian: [00:21:25] Yeah. I mean, that was a project that we were thinking of doing. However, there was a really interesting paper as it came out earlier this year where they were able to a haploid assembly of feline genome by sequencing a wildcat and domestic cat. And then the offspring. So, from those reads from the offspring, they were able to say, all of these reads are the domestic cat, all these reads are the wildcat. And then they did a pseudo haploid assembly using that method is really cool. So, that’s a really, really good genome. And I don’t know if we can beat that, to be honest. One interesting thing we are thinking of doing is to try to produce a haploid stem cell line, because that is something as possible to get an oocyte out of a cat and use strontium chloride to kind of activate it. And it can sometimes become a haploid stem cell line. And when you have that then you can sequence the genome and it’s a haploid genome.
[00:22:17] So, you don’t have to worry about heterozygosity or any of that. So, I Basepaws we do low-pass sequencing. We sequence at around 0.5-1x. And then using our imputation panel, we impute a lot of other markers. Usually, we end up with a couple of million markers at the end. And then using these markers, we use a machine learning algorithm. We just use the random forest-like algorithm really to assign haploid type segments to a known breed. And then we calculate a similarity of your cat to this breed. So, that’s what we do for our breed analysis portion of things. And for the health markers side of things, we have a multiplex amplicon panel that we’ve developed where we interrogate I think right now, 40 or so loci using this multiplex amplicon approach. And then we give you the status of whether there are heterozygotes, whether you have copy. How many copies you have, that type of thing. And we are expanding that panel to about 120 markers by the end of the year.
Grant: [00:23:19] What conditions have you found good predictive power for?
Damian: [00:23:23] So, this is the stuff I get excited about, right? Because I feel like when people talk about bioinformatics, they have this artificial separation thing, genotype and phenotype. I think the correct view of looking at it is it’s just all data. It’s all just some kind of dimension of the sample that you collect. And I think when you throw all that together into multi-omic analysis, that’s where the power comes in. So, that’s what we’re working on right now. So, like, you know, like I was saying that CKD, the chronic kidney disease signal that we’re seeing from the oral microbiome, we’re seeing a big signal from that. However, it overlaps with the periodontal disease signal. So, it’s hard to tease apart. Does this cat really have chronic kidney disease or does it just have periodontal disease? However, if we apply a layer of genomic data or some other phenotype that we get, we find that we can pry these apart a little bit more. So, we’re still trying to find the set of features that can best tease those things apart. But I think we’re getting close to some interesting set of features.
Grant: [00:24:20] And how translatable do you think your findings in cats will be to, for example, human health?
Damian: [00:24:26] So, there was a great review paper by Leslie Lyons, who is kind of the main person in the feline genetics field. She wrote a review talking about how if you compare the feline genomes to the human genome is actually one of the closest mammals that exists. I think it’s the most syntonic aligned genome compared to every other mammal out there. You know, if you look at something like genes involved in eye development, I think all of those genes are syntonic with the human block of genes. So, I think there’s a lot of translation potential by studying felines. And I think a lot of the known health markers in felines have almost a direct homologous variant in the human genome, too.
Grant: [00:25:08] Is that something that Basepaws is planning at, looking at in a systematic way?
Damian: [00:25:12] It’s one of my pipe dreams, to be honest. I mean, there’s so many things we can do, but I think maybe like five years down the line, whatever it is, let’s say we collect a ton of cat data. We collect a ton of dog data. You know, can we have a pan-mammalian database where we just like all the variants and use that to narrow down disease markers? So, in humans, you find 80 potential markers for diabetes. You get to narrow that down to 10 because you find homologous variants in these other animals. I think that’s a great usage of this data.
Grant: [00:25:41] How do you think about R&D and kind of building capacity versus having a sustainable revenue driven company? Because generally in the biotech space, most companies are pre revenue for a very long time. And obviously Basepaws already is very actively engaging with customers and has been for a long time. But at the same time, you’re doing a lot of internal R&D work. So, kind of what’s your framework for that?
Damian: [00:26:13] In terms of R&D I always separate in two buckets. One is maintaining or optimizing what we have currently. That means lowering costs for library preps, how we can normalize things better. And the second bucket is what new products we can get from that. In terms of new products, I think for the last one or two years, we’ve mainly been focused on the bioinformatics side of things because it’s cheaper. That’s really the reason we have a lot of data. Can we generate new products from that data? Which we have with the oral microbiome product, for example. But I think now we’re actually close to the end of our series, a funding. I want to start focusing more on kind of lab assays or products or tests that we can do. Something I’m kind of interested in doing is one of those epigenetic clock aging test type of things.
Grant: [00:27:02] Oh, that would be cool.
Damian: [00:27:03] Yeah. You know, one thing I’m kind of interested in figuring out, and there are a couple of papers on this already, is the DNA that you get from on saliva, does that correlate with the blood DNA that’s traditionally done in the epigenetic clock studies. There are a couple of papers looking at that and saying that it does correlate. So, maybe you can do all these epigenetic aging tests through the saliva DNA. That would be cool.
Speaker3: [00:27:26] Very cool. Very cool. How would you think about the kind of commercial path for in, you know, a cat saliva based epigenetic test of aging?
Damian: [00:27:36] I think biological aging is something that people are just interested in. And being able to gather that data and compare it to its real age can give you a lot of insight into longevity and a host of other interesting biological concepts. Longevity is something that me and my wife are personally interested in. So, I think a lot of people might be interested, too. And we’re always looking for products that are not the standard 23andMe ancestry or health marker type of tests. And this is just another one of them, because I feel like if we want to enter, especially a dog space or other animals, you need to have something different.
Grant: [00:28:12] How do you think about engaging with your community?
Damian: [00:28:14] So, the cat community is very different from, as you might imagine, other dog or human communities. People are a lot more obsessive about their cats, I would say, in a good way. I don’t want to suggest that that’s a bad thing. And I think they are a lot more curious about their pets than they are about themselves, actually. That’s one trend I’ve seen. I wonder about this in the human space, too. I would much rather get a DNA test for my kid than for myself because I think most people are like, oh, I know myself, I don’t need to know more. So, I think in the pet space, that kind of applies too. I would much rather find out more about my cat, my dog, who can’t really tell me what’s wrong, than about myself. I think maybe that’s one advantage we have over the human space in some sense.
Grant: [00:29:01] Great. Is there any advice you’d have for scientists looking at transitioning into the biotech startup world?
Damian: [00:29:09] I think as an academic scientist, I don’t want to paint the situation with a broad brush here. But I think the academic mindset, sometimes it’s like I have a choice. I can do really good science or I can have enjoyable personal life. You know, I think it’s a false choice. Personally, I think you can have both. When a scientist gets into an industry, they maintain that mindset a little bit. And I think industry sometimes will try to take advantage of that. So, I think any academic scientists going into industry should change their mindset. They should see their value, get over that imposter syndrome and know that you’re probably one of the few people who can answer or solve these types of problems, have that confidence. I think in academia, when you’re surrounded by a bunch of really intelligent people, it’s kind of hard to have that kind of confidence. I guess, don’t carry over your academic baggage into industry would be my best advice.
Grant: [00:30:05] Right. It’s like a lot of really smart people who enjoy shooting each other down, right?
Damian: [00:30:09] Exactly. Yeah. Yeah.
Grant: [00:30:12] Do you have any parting words for our listeners?
Damian: [00:30:15] I mean, since this is The Bioinformatics CRO Podcast, I would say that I’m excited about the future of this field. There’s a lot of interesting things happening. And I would encourage more people to join this industry because there is a lack of bioinformaticians. We’re hiring, by the way. So, apply for a job with us, if you’re interested.
Grant: [00:30:36] Thank you so much. It was great having you on.
Damian: [00:30:38] Yeah, no problem, Grant. Thank you.