Transcript of Episode 48: Alex Shalek

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Transcript of Episode 47: Jamie Smyth

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Transcript of Episode 46: Amar Gajjar

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Transcript of Episode 45: Jill Reckless & Jon Heal

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Transcript of Episode 44: Adam Siepel

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.

Transcript of Episode 43: Erich Jarvis

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.

Transcript of Episode 42: Mauro Calabrese

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.

Transcript of Episode 41: Damian Kao

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.

Transcript of Episode 40: Nicholas Heaton

Grace: [00:00:00] Welcome to The Bioinformatics CRO podcast. My name is Grace Ratley. And today I’m joined by Nicholas Heaton, who is Assistant Professor of Molecular Genetics and Microbiology at Duke University. Welcome, Nick.

Nick: [00:00:11] Thanks, Grace. Great to be talking today.

Grace: [00:00:14] Yeah. So let’s start off with talking a little bit about what you study at Duke, and that is the influenza virus and other things occasionally. Can you tell us a little bit about that?

Nick: [00:00:26] Yeah, absolutely. So our lab, you know at the highest level is kind of broadly interested in understanding how these viruses make you sick. We basically modify the virus itself so that that virus will act as a tool that we can then use to ask whatever kind of scientific questions that we’re interested in.

Grace: [00:00:45] What kinds of things in particular do you study? Are you looking more at the genetics aspects of influenza viruses or at the structure or how they infect cells?

Nick: [00:00:55] Yeah. So a lot of the things that we’ve been working on, we kind of think about them as falling into the margins or like the gray space of what happens during a viral infection. So for the most part, when a cell is infected by a virus, scientists think of like a program being initiated. And the same thing essentially happens over and over and over. And what we found and others have appreciated as well, is that it’s more complex than that. The different cell types can respond different ways, and that happens at various frequencies. So that’s what we’ve been studying. The more rare outcomes of infections and trying to understand the nuances of how the virus can interact with its host can really then go on to dictate high level phenomena like disease severity or transmission or something like this.

Grace: [00:01:44] And could you tell our audience a little bit about influenza viruses? I mean, most people have heard of flu season and things like that, but what are some basic virology things that people may not have heard about?

Nick: [00:01:56] Yeah. So these viruses, they fall into a family they are called Orthomyxoviridae. And it’s a huge family of viruses. A lot of them are insect viruses, actually. But the ones that fall into the influenza virus family are influenza A, B, C and D. And when we talk about flu, like clinically people getting the flu or mostly talking about influenza A and influenza B. In a given flu year, it’ll be about two thirds influenza A and a third influenza B. Sometimes those ratios can flip. Influenza C’s can infect children sometimes, but they have animal reservoirs. And influenza D’s are rarely, if ever, detected in people. But these viruses, they have genetic material that’s encoded in RNA.

[00:02:31] So influenza viruses are called RNA viruses. And they’re envelope, which just means they take essentially part of the cell membrane with it when they leave. And that’s how they essentially incorporate it into an actual physical particle that can be sneezed on somebody or breathed in. And these are respiratory viruses like coronaviruses or other viruses.

Grace: [00:03:01] Awesome. So where do they originate? Influenza is a zoonotic virus, if I’m not mistaken. So what’s their normal reservoir?

Nick: [00:03:10] Yeah. So the answer is that it’s complicated. The family of influenza virus especially, so influenza A’s, which, you know are kind of the predominant flu strain. It’s really a bird virus. So there’s lots and lots of different subtypes of influenza that are all found in birds. Migratory waterfowl is where you find these things. And that’s probably almost certainly in the initial kind of introduction of those viruses into people or into mammals. But now we just have human strains of flu that just spread from person to person. It’s not like it goes from person to bird, back to person to bird. Now, we have human strains, and our human strains of flu are to a large extent still related to those bird viruses. But yeah, now they just circulate in people. And the subtypes I mean, the letters and numbers that you hear are like H1N1, that refers to the subtype of the virus. And so H1N1 and H3N2’s are what actively circulate in people right now.

Grace: [00:04:08] And for our listeners, the H’s and the N’s refer to the proteins that are found on the envelope of the virus, is that correct?

Nick: [00:04:15] Yes. On the very outside of the virus there’s two kind of dominant proteins:  H is the hemagglutinin protein and N the neuraminidase protein. And that’s where the H and the N come from. And basically there’s dozens of different types of hemagglutinin and different types of neuraminidase’s. So H1N1 that I referred to, that’s a subtype one hemagglutinin and subtype one neuraminidase. And again, these are just the proteins that are on the outside of virus that actually help the virus get into the cell. And because that’s what the immune response is mostly directed against, people have characterized them in these groups based on reactivity of antibodies, is essentially how the subtypes are defined.

Grace: [00:04:56] Yeah. So talking a little bit about how we prevent influenza infection every year, you know, we have our seasonal flu vaccine. But there’s a lot of, I don’t want to say controversy, but I feel like a lot of people are really reluctant to get those vaccines. And I feel like a lot of that has to do with people questioning the effectiveness of the vaccine. So can you tell us a little bit about what it means to produce a vaccine for influenza?

Nick: [00:05:23] Yeah. So we’ve been making vaccines against influenza viruses for a long time. So there’s different kinds of vaccines now. The majority of them are what we call split vaccines, which are viruses that are grown and then purified and then treated with a detergent so that they fall apart. So you just have all the pieces of the virus, but there’s no infectious particles there, and that’s what’s injected into your shoulder. Like I said, the vast majority of people get vaccines, get those. There are also some purified protein vaccines where just the virus is never involved. You just express different proteins from the virus that can be injected. There are some live attenuated vaccines, those are the ones that get squirted in your nose.

[00:06:03] And that’s essentially a version of flu that can’t replicate enough to make you sick, but enough that your immune system can react. And those are basically the three kind of flavors of FDA approved vaccines for flu. The flu vaccine, the efficacy is actually pretty good against matched strains. But therein lies the issue, right. So we know flu season is going to happen. We know when it’s going to happen, right. It’s in the late fall or early winter. And because it takes a long time to produce the vaccines and formulate them and get them distributed to hospitals and physicians and companies like Walgreens and things where people get their flu shot. We basically have to start making those vaccines early, like well before flu season starts.

[00:06:50] And so essentially what people at the World Health Organization do is look at the viruses that are circulating and they predict which ones are going to then circulate when the next flu season comes along. So part of the reason that the flu vaccines don’t always fully protect people is that sometimes a different virus that we weren’t predicting circulates. So you get vaccinated with something that’s kind of close to the virus that you’re going to be exposed to, but not close enough to give you 100 percent protection. The other thing that can happen is sometimes, even if we picked correctly the viruses that we want to turn into vaccines, they don’t grow particularly well under vaccine production conditions. And so in those cases, we essentially select for viruses that grow better. So it’s feasible to produce these vaccines.

[00:07:42] And the viruses that grow that are slightly mutated relative to the viruses that are circulating in people. Any time you are vaccinated with something that’s not exactly what you’re being exposed to, the vaccine doesn’t work as well. But I will say that the vaccines, even if they’re not super efficacious in preventing infection, they still do a really great job of keeping you out of the hospital. So, I think that flu vaccines get a bad rap because it’s true: sometimes you can get your flu vaccine and then you can still get sick with the flu. The chances of that happening are decreased dramatically, but it can still happen. But basically, it almost always keeps you out of the hospital which is important as well.

Grace: [00:08:24] Yeah. And then talking more about these prevention efforts. So what we’ve seen this past year is that there was a really large decrease in flu cases during the pandemic. Can you talk a little bit about what may have caused that?

Nick: [00:08:38] Yeah, absolutely. So there’s a ton of surveillance. We call it surveillance for flu. Places all over the world are testing people when they come in and they’re sick or even just testing people off the street. We test them and we look for viruses. So we have a pretty good understanding of when flu is infecting people, what kinds of flu are infecting people. Over the last year, the last flu season, there was very little flu and nobody knows for sure, but almost certainly the answer is that wearing masks and social distancing helps prevent the spread of respiratory viruses. And flu is a respiratory virus, which transmits in the same way as SARS-CoV-2. So when you take into account that people already have immune responses to flu, everybody is exposed to a virus either by vaccination or infection. Essentially when they’re born, within the first couple of years, they have antibodies against flu.

Even if they haven’t been exposed to the exact strain, their immune system has seen something that’s similar. So, when you take people’s exposure histories, combined with getting flu vaccines, along with social distancing and mask wearing, essentially flu can’t circulate the human population. SARS-CoV-2 has also been controlled efficiently with behavioral practices, right. But the lack of pre-existing immunity is what flu benefits from. And SARS-CoV-2 has benefitted from that at least until recently, now that we have vaccines against it.

Grace: [00:10:08] And so do you think that these measures will be implemented every year for seasonal flu, or do you hope that they will or do you think we’ll probably just go back to getting the flu, getting sick and all that?

Nick: [00:10:20] Yeah. I mean, it’s an interesting debate. You know, it’s kind of been an experiment, right. We’ve never made everybody in the country wear masks and stay away from other people before. And now we know if we do that, we can stop transmission of these and other viruses, right.  The question is where’s the line? Right. Like we could say nobody’s going to be around anybody and everybody’s going to wear a bubble and keep them away. And for sure, that would stop the spread of infectious disease. I think that we’ve done a reasonably good job of controlling flu with vaccination. And the other thing that we have for flu that is really limited for SARS-CoV-2 are antivirals. These are pharmaceutical drugs that you can get when you go to the doctor and you’re sick with flu.

[00:11:05] So if you’re vaccinated and you have an exposure history, you’re already reasonably safe from flu. But if you still got sick and went to the hospital, we could give you drugs that will stop the virus. So we have a lot of tools in our arsenal to combat flu, which we didn’t have when the SARS-CoV-2 pandemic started. So this is the big difference. And that was the concern. That was really the impetus for the public health measures. Like if you get this thing, you’re on your own, right. There’s no medical interventions which are proven to be efficacious to stop you from dying from this disease. But now the vaccines against SARS-CoV-2 are amazing. And there are antivirals which are approved and tons that are in development. So I imagine that in the not too distant future, we’ll have more ways to fight this virus, which I think will help, so that we don’t need to wear masks for forever. But essentially at the end of the day, everybody will make that decision for themselves. But it is interesting to know kind of the magnitude of how much can be accomplished if you implemented those measures.

Grace: [00:12:07] Yeah. I don’t mind mask wearing. I had really bad allergies when I was growing up. And one of the things that they said you could do was wear a mask to prevent pollen from getting in your nose and mouth. And I was like, oh, but if I did that, everyone would think I was weird, you know, like really sick and avoid me. So for my own personal reasons, I hope that mask wearing is a little more accepted, at least in allergy season.

Nick: [00:12:34] Yeah. And it’s worth pointing out. We’re obviously talking about kind of a US centric look at these practices. But in other parts of the world and other cultures mask wearing is much more normal. That could be an outcome of this, right. It could be normalized in the United States and more of a culturally accepted practice. It’ll be interesting to see. Again, it’s kind of a big sociology experiment, how are people’s behaviors and actions being changed by a biological phenomenon like this.

Grace: [00:13:02] Yeah, certainly. So kind of moving into a discussion more about the SARS-CoV-2 pandemic. I really loved your lab’s bio on Twitter, which for our listeners was “we study influenza viruses that cause disease except for when we get interested in something else…” And I can only assume that that’s something else is SARS-CoV-2, given that you’ve recently been publishing a little bit in that space.

Nick: [00:13:27] Yeah, absolutely. So, the SARS-CoV-2 pandemic happened and at least for us, we’re working on these viruses that are kind of similar. Maybe we would have some insight, some things to add to the field. But more than that, the university shut down. Everybody went home. And the exception to that was if you were working on coronavirus. And so that helped increase our motivation to take on this new challenge because it was that or it was sit on the couch. There’s amazing groups who’ve been working on coronaviruses for a long time, and they’ve really led the effort, and a lot of this work. The viruses are different than influenza viruses, obviously, but they still use the same cell. They still use the same host, right.

[00:14:14] And one of the things that we had been working on with influenza was trying to understand what do they need to take over? What do they need to co-opt from the cell they’re infecting in order to replicate? We had been studying those questions for flu and we thought maybe that’s an area that we could work on to try and understand what the coronavirus would need to take from the cell such that it could efficiently replicate. I think people appreciate this, but there really is a dramatic difference in the genetic potential of a virus and its host. Even a big RNA virus, like coronavirus is a pretty big RNA virus, encodes less than 50 proteins, maybe on the order of 30 or so. Influenza viruses encode anywhere between 12 and, I don’t know, 20 proteins or something like this.

[00:14:58] And human cells encode genes is probably about 20,000. And if you take into account splice variants and these types of things it can be hundreds of thousands of different kinds of proteins that all have different jobs. So when you think the virus is going to be able to replicate itself, it’s come up with 20. It’s going to gather some from the host. And that’s an area of interest. A lot of groups, including ours. What does the virus need? Because it opens up kind of a practical option for not just stopping virus proteins, not just inhibiting virus proteins, but if you can inhibit either the interaction between a virus and a host factor that it needs or inhibit the factor that the viruses use. These are new possibilities for kind of antiviral treatments.

Grace: [00:15:38] Yeah. I mean, there was a lot of movement when the coronavirus pandemic started from different fields into virology. So I imagine you had a bit of a head start on that moving in from one field of virology into another compared to someone who moved from engineering, I don’t know something like that.

Nick: [00:15:56] Yeah. It was interesting, you know, science is always better with diverse perspectives. And I think the field has benefited from somebody who thinks about a totally different question or usually thinks about different questions and now says based on how I think about things, what do I think is going on? It has moved the field forward rapidly. And, we know a lot more about these viruses than we did a year ago. I mean, that’s been the good side of the spirit of scientific collaboration and discovery that’s been all focused in this area.

Grace: [00:16:29] How do you think that the pandemic has changed the way that the general public thinks about virology?

Nick: [00:16:36] Yeah. I mean, that’s an interesting question. I think people think about viruses now. For some people the level of resolution is germs. And there are things that make you sick. There’s all kinds of things, right. There’s bacteria, there’s viruses, there’s protests and all kinds of things. The idea of a virus has certainly come to the forefront in people’s mind. I mean, which is cool, right. These are things that we think about all the time. But not everybody does. So it’s kind of cool to have more universal recognition of the types of questions and things that we’re interested in. I think there’s also been an interesting kind of realization and attention paid to the scientific process in general, which I think is really going to be a helpful thing to come out of the pandemic because it costs money, right.

[00:17:24] A lot of the biomedical research in the US is funded by taxpayers. I mean, the lion’s share for sure, is people’s taxes, right. And the question is, what are we studying and how is it helpful? What’s the return on this? Right. And, you know, I would say that the coronavirus pandemic has demonstrated what this return is. You know, it sometimes frustrates people that we don’t have all the answers right at the beginning. But when there’s a question, we can activate this biomedical research machine. We can understand things like how transmissible are these viruses? How long do they stay in the air? Can you transmit them by touching? These are all experiments that are done that answer those questions.

[00:18:03] And I mean, the most dramatic one is the development of these vaccines. I mean, the development of vaccines takes decades. And the fact that collaborative teams were able to come together, pharmaceutical companies and industry and academic institutions for testing and development of all these things, and have a vaccine essentially a year that is highly efficacious. I mean, this is what that money is going to. Discoveries that enable this type of stuff. This is the payoff for supporting that kind of endeavor.

Grace: [00:18:31] Do you think that sort of interest will continue beyond the pandemic or do you think people will as soon as it’s over, be like, oh, forget biology, let’s go back to normal life?

Nick: [00:18:43] Yeah. I mean, I think that’s a natural tendency that will occur to some extent. But, hopefully this will remain fresh enough in people’s minds that I mean, you see these things in congressional hearings, right. Where they’re discussing the budget on science. Why do we need to give this much money to research? At least for the short term and hopefully for longer we’ll be able to say this is why. God forbid, there’s another pandemic. But if there is, we need these people working on these kinds of questions and developing these things. We can’t be caught flat footed.

Grace: [00:19:11] And would you care to make any predictions about what you think the next pandemic might be? Do you think it’ll be pandemic flu or another coronavirus or Ebola? What do you think?

Nick: [00:19:22] Yeah, historically, at least from 1900 on, there’s been a series of pandemics and they’ve all been flu pandemics. There’s been coronavirus outbreaks and they’ve been epidemics. This is like SARS and MERS, the viruses that are very similar to SARS-CoV-2, but they were regionally contained. So just numbers wise, starting again with the 1918 Spanish influenza epidemic or pandemic. We’ve had a number of these flu. Most recently in 2009, the H1N1 swine flu pandemic. They’ve all been flu. So I think if you were betting, you would bet on it being a flu pandemic, the next one. But you know, who knows? And as the environment changes and people are living more places in more proximity to kind of reservoirs of animals. I mean, we’re just increasing the contact between people and things they weren’t exposed to before, including viruses.

[00:20:18] You know, I think one thing that will come out of it is the flu field has been engaged in the surveillance that I was talking about earlier in the development of interventions or countermeasures for pandemics. With the idea that another one is going to happen at some point, we need to be prepared. There is much, much less emphasis on coronaviruses because we hadn’t had a coronavirus pandemic at least since the molecular age. And we could really identify what these viruses were that were causing disease. But obviously, now it’s clear that they can. So I think the same type of effort and in terms of surveillance and predicting these things will be applied to coronavirus. And if it should happen again, I think that we will be much better prepared.

Grace: [00:20:56] Yeah. And I know there’s been some talk in the science community that the frequency of pandemics might be increasing. Can you speak a little bit on that and why you think that may be?

Nick: [00:21:08] Right. So it’s impossible to predict. But if you look at the things that affect transmission of disease, which is required for a pandemic, people move across the world at an unprecedented rate compared to what we used to do. That I can be anywhere in the world and can be anywhere else in the world within 36 or 48 hours is a bad thing in terms of transmitting pathogens around. So that’s a big part of it. The second thing is that we have more people, right. More people closer together. The cities are bigger and it’s much easier for pathogens to transmit when there’s just more people that are infectable around. So that’s a factor. And then, of course, this is what I was referring to before: a lot of viruses are transmitted by vectors, mosquitoes, for example, right. And as climate changes, animals and insects change their distribution as well. So populations come into contact with these viruses or the animals or the insects that carry viruses, which then facilitates these spillover events, which probably happened with SARS-CoV-2 at some point.

Grace: [00:22:13] Do you think there’s anything that we can do to reduce the rate of these pandemics happen?

Nick: [00:22:18] I mean, these are hard questions, right. Because people will have to live and work and travel where they travel and these types of things. Obviously dramatic things like that. I don’t think that’s likely to happen. I think that what’s much more likely in terms of being able to prevent pandemics or to contain them as epidemics and prevent them from becoming pandemics is just surveillance and countermeasures, right. If we know when these things start, we can detect them right away and quarantine people or if we can rapidly make countermeasures, right. If we could have made these vaccines within a month of detecting the virus, that would have presumably made a big impact on how far the virus was able to spread. So I think that’s probably where most of the effort is going to be focused. Understanding where these things are coming from. Understand quickly when they’re in the human population, and then being able to respond rapidly is probably the direction to go.

Grace: [00:23:15] So moving into more of a discussion about you as a person and as a scientist, I’d love to hear a little bit about how you came to virology and influenza and how you made it your way to Duke? Yeah. So start from whenever you first got interested in science at all.

Nick: [00:23:34] Yeah, it’s been a while. So I guess it started in high school. When I was in high school, I took all of the science classes that were offered and there was nothing else to take. And then I ended up going to the university. It was in my town, Utah State University, for credit to work in a research lab. And then when I went to college, when I was doing my undergraduate studies, I had this experience now like I was qualified or at least more qualified to work in a laboratory. And so I got a job working in a lab to pay the bills. I was in a great lab, they gave me some opportunities to develop some of my scientific skills. And then basically I became good at it. You know, I thought this is something that I like doing and something that I think I can do well.

[00:24:19] And from there, I went to graduate school based on that interest. My undergraduate degree is actually in bacteriology. And I went to graduate school to work on bacteria. And then in this kind of fortuitous event, the guy that I ended up doing my thesis work with, we were in the elevator. He was asking me how graduate school is going. We start graduate school. You do rotations where you essentially work in a few different labs and kind of figure out what’s a good fit for you. He asked how my rotations were going, and I said I didn’t know which lab I was going to rotate in next. He said, well, we just got this grant to work on a virus called dengue virus.

[00:24:52] I don’t know anything about dengue virus, but it sounded kind of exotic and cool. And so he said, come rotate if you want. And I ended up working on that virus. It’s one of those mosquito transmitted viruses that’s prevalent in the tropics. And then I was good at virus research. That’s what I was qualified to do. So I kind of kept doing it. I did my postdoc on influenza, then I’ve been working on flu and the respiratory viruses ever since.

Grace: [00:25:18] I always love hearing about the serendipity of people’s journey. I think that’s really awesome. What exactly was your thesis research on?

Nick: [00:25:28] Yeah. So we were working on dengue virus. And one of the questions that we were really interested in was the interactions between the virus and the hosts. And the reason that we were interested in that question in particular was the virus replicates in mosquitoes, right. It lives in mosquitoes. And that’s how you get it. A mosquito bites you. And so this virus has to exist in mosquito cells and in human cells. And those are really different environments, right. A mosquito is very different from human. And so, anyway, those were some of the questions that we were interested in. We ended up doing a series of experiments looking at the role of cholesterol and fatty acids, which make up membranes and how the virus essentially reprogrammed the host cell to make the membranes that it needs for its replication and its assembly.

[00:26:13] That’s what my thesis was on. We published a couple papers on that. Towards the end of it. So we had looked at the host side, and what we really wanted to then do was look at the virus side of things. But the genetic tools for dengue virus at that time were not particularly developed. It’s really hard to make a mutant virus. We were trying to make a virus that couldn’t reprogram the host cell to move these membranes around and see what would really happen. So anyway, this was something we were interested in but were unable to do at the time. And then I was thinking about what I wanted to work on next and this is one of the reasons that I picked flu, because flu had a really good genetic system and there were a lot of things you could do with the virus. And that’s kind of what I was interested in getting trained to learn how to do.

Grace: [00:26:54] So when you aren’t busy being a scientist, what do you do? What do you do for fun? Who’s Nick the nonscientist?

Nick: [00:27:03] Well as you know the process of science takes a lot of time. My wife works in the lab with me, so it’s kind of a family business. And we have two young sons. So between the two of us working and then taking care of the kids, that’s essentially the full schedule of events for us.

Grace: [00:27:20] That’s the life. That’s awesome. What’s it like to work with your spouse?

Nick: [00:27:24] Yeah. As you can imagine there’s pros and cons. There’s way more pros. I think in our particular case, we met in graduate school. And so we’ve always kind of interacted doing science and talking about science and troubleshooting experiments. And so it’s just kind of been a natural evolution. And now it’s really great because, you know, she gets it right. Like when there’s an experiment that has to be done or a time point that’s really late or something spills into the weekend or something like that, it doesn’t take explanation. It’s just as things go and she knows the details, she gets it and we figure out how to make it work. That’s been a huge benefit.

Grace: [00:27:59] It’s better than someone who has no experience in science, because it really does take a lot of work and odd hours and that’s pretty cool. And you always have a carpool, buddy.

Nick: [00:28:09] Yeah, that’s right. Reduce our carbon footprint.

Grace: [00:28:13] Yeah. So as we wrap up the episode. What sorts of advice would you give to people who might be entering the field of virology today?

Nick: [00:28:24] I guess I would have two pieces of advice. The first is that you really need to become good. And what I mean by that is there are metrics in science, right. If you publish papers or you get fellowships or you get grants or these kinds of things. And I think a lot of people focus on hitting tangible metrics so you can put a line on your resume. I got this or I published this or whatever. And I think sometimes there’s less of an emphasis on really becoming an expert in the process of doing science. How do you set up the best experiment so you can make the clearest conclusion from it? And so that’s what I would tell people to start with. All the rest of that stuff comes if you’re doing good, thorough, reproducible science.

[00:29:09] You get all the rest of that, but at the beginning, that’s really what the focus should be on. And it’s satisfying. You know, sometimes from a CV building point of view because there’s nothing you can see, right. You’re thinking about questions better and more precisely. That’s really important. The other thing I would say is that there’s lots of reasons why people go into different fields or pick different topics to study. And I think that science is an endeavor where it’s really important to be excited about the specific thing that you’re working on. And it means different things for different people, even within the context of like working on one virus. There’s all kinds of different directions that you can approach it from. And figuring out what excites you, which little nuance of the questions are you most excited about?

[00:29:55] That’s no small part of being able to be successful and invest what’s required and when an experiment doesn’t work, something like that. I think it’s that that gets you out of bed the next morning. You know, you really care about it and you’re really working on the right question, helps you get through the lows, which always happen.

Grace: [00:30:11] Two excellent pieces of advice. And I hope our listeners can take those away with them after this episode. Thank you so much for joining me, Nick. It was really awesome talking to you and hearing your perspective about viruses and the pandemics and advice for life.

Nick: [00:30:27] Thank you. It’s been a pleasure.

Transcript of Episode 39: Becca Chodroff Foran

Grant: Welcome to the Bioinformatics CRO podcast. I’m Grant Belgard. And joining me today is Becca Chodroff Foran.

Becca: Hi, Grant. Thank you so much for having me today.

Grant: Thank you for coming on. So Becca is the head of R&D at Wisdom Panel, a pet tech company focused on pet genetics. Really looking forward to hearing about Wisdom Panel.

Becca: Yeah. I’m thrilled to tell you more. It’s been a pleasure working at Wisdom Panel, and we’ve been through quite a journey.

Grant: So you’ve been at the company two years, right?

Becca: That’s right. I’m coming up on my second anniversary. And just to tell you a little bit more about Wisdom Panel, like you said, we’re a pet tech company. We’re focused primarily on strengthening the bond between pets and their parents through genetics.

Becca: And what that actually means is that we offer an array of products that give pet parents insights into their pets and that’s cats or dogs, breed backgrounds, health risks, and different types of phenotypic traits. In the background, because I’m on the research side, we’re also endeavoring on the largest ever dog DNA study.

Becca: We’ve tested over two and a half million dogs to date over 15 years. And we’re just starting to scratch the surface in analyzing those DNA sequences with phenotypic information that we’ve collected along the way.

Becca: And we’re starting to uncover some really exciting associations. Our help, actually, is that we can use that information to both strengthen precision care offered to cats and dogs. And my personal hope is that we can start translating some of those insights to human medicine as well.

Grant: Like Elaine Ostrander, right?

Becca: Exactly. So we were in the same lab, and I’m sure that you shared this similar experience of walking by her lab every single day and her rows of accolades. One image that specifically is burned into my mind is a framed picture of, I think it’s a Nature cover where there’s a really, really big dog and a really, really tiny dog.

Becca: And that study uncovered one of the first major associations of size in dogs. The gene was IGF-1. And what Elaine Ostrander found is that mutation in that one gene was responsible for a significant amount of the size variation in dogs.

Becca: It also laid a lot of the groundwork for why dogs are such an important model in genetic research, taking size as an example. In humans, there are probably hundreds of single nucleotide polymorphisms that are responsible for the differences in size.

Becca: In dogs, it’s likely a few dozen that can explain 90 percent or more of the variability in size. Interestingly and quite usefully, the same logic applies to a variety of diseases in dogs, which is why I’m so excited to have the opportunity to do the research that I’m doing today.

Grant: How much work do you do in linking that back to what we see in human genetics or mouse genetics efforts? Do you see any similarities in genetic architecture?

Becca: Yeah. So I’ll start with the genetic architecture. And by and large, the genetic organization across humans and dogs is remarkably similar. We share 86 percent of our genomes with dogs.

Becca: The genes are in the same order. We have 23 sets of chromosomes, dogs have 39. But that essentially just means that the genetic order is extended across more chromosomes in the same order.

Becca: Going to phenotypes, there’s also a remarkable amount of similarity, some of that’s attributed to physiology. A lot of it’s also environment because dogs, more than any other species, shares our environment as far as lifestyle, the food that they eat, the houses that they live in, in my case, the beds that they sleep in.

Becca: So in that way, we can study a lot of the same diseases that impact both humans and dogs. There have been some discoveries in autoimmune disorders, cancer, neurological disorders, and dogs that have helped us elucidate the underlying mechanisms and human diseases and vice versa. So it’s more of an interplay, as opposed to taking one and then shifting over to the other.

Grant: How about dog evolutionary genetics? So if you look at recent selection events in humans and so on, you see prolonged tolerance for lactose and things like this, are there similar selective events in dogs with them sharing human diets and so on and so forth?

Becca: There are, and I’ll start first by giving a bigger picture of dog evolutionary biology because it’s unique. They’ve been subject to both natural selection and a host of artificial selection events. At this point, they’re one of the most diverse species on the planet, ranging in size from two pounds to over 200. They have different behaviors. They have vastly different phenotypes. And a lot of that diversity has just emerged over the past 150 years. That’s also the case when you look at nutrition. So you’ll find that different breeds require different types of nutrients.

Becca: And we’re just starting to understand, in molecular detail, what those requirements are. There are certain disorders, for example, hyperuricosuria in which certain minerals are not processed quite as well. And the list goes on. Dogs have allergies just like we do. They have preferences for different types of food. And we believe that a lot of that is associated with genetics.

Grant: What phenotypes are you looking at? Are you looking at behavioral phenotypes above and beyond just breed level differences? How do you gather that data objectively right? Because if you ask pet owners, I’m sure you could get very different answers from different owners of essentially the same dog.

Becca: You’re exactly right, which is one of the reasons why there have been an array of standardized surveys developed over time that ask questions in a sneaky way. So as opposed to asking, is your dog excitable? We can ask a series of questions about whether your dog barks when there’s a visitor, whether they jump up and down when you shake a treat bag, and so on and so forth.

Becca: As far as your initial question, we’re looking at a full range of phenotypes. We have the privilege of being partnered with Banfield Veterinary Hospitals, which has vet clinics across the country. And they’ve offered our genetic task to hundreds of thousands of puppies. We can take those genetic results and associate them with their medical records.

Becca: We’re just now starting to link the genetic data with the clinical data and try to find associations between various disorders. We started the study back in 2019, and we enrolled puppies. These puppies are now at most two years old. So most of them are just starting their lives. There haven’t been that many disorders, but we anticipate that over time will get more and more data so that we can understand the ideology of a variety of diseases. And we’re primarily focused on several of the same diseases that afflict humans, ranging from cancer, epilepsy, diabetes, neurological disorders, and osteoarthritis. And the list goes on and on.

Grant: So that sounds like an incredible data set. What are the long-term plans for that?

Becca: There are multiple. So the first is, actually, the medical data gives us a small window into the dog’s day to day life. We’re also in the process of launching a community science platform in which we’re going to start surveying pet parents about their dog’s daily lives, their health, their behavior. And then we’ll combine those massive data sets together and hopefully build out some risk prediction models.

Becca: One of the goals on the horizon is to start building out risk models for more common disorders so that pet parents can make more informed decisions early on in life.

Becca: If the dog has a really high risk of a particular cancer, they might choose to engage in additional screenings. If the dog has a risk of osteoarthritis, there might be some recommendations as far as curving the dog’s weight. So there are a variety of opportunities we have short term.

Becca: In the long term, we’re hoping that these insights can be coopted by veterinary clinics to make care more precise, more personalized, or pup-sonalized, whatever you want to call it.

Becca: And we hope that our insights will help veterinarians make more informed decisions about what types of treatments are given to dogs or what types of wellness protocols are offered to dogs. And finally, as I alluded to earlier, we’re hopeful that some of these insights will be translated to human medicine, and that on both the human side and the veterinary side, some of these insights could lead to additional discoveries in therapeutics.

Grant: So we have a career-changed service dog. It’s a nice euphemism for a flunky from Service Dog School. And obviously, it is quite expensive to have a dog sent out of a Service Dog School a year and a half or two years in. Do you think there are still variants to be found and so on that could be used as part of the screening process for service dog training? For example, where they can say this dog would be better as a pet versus we’re going to spend tens of thousands of dollars training this dog to be a working dog?

Becca: Absolutely. We’ve been in contact with a variety of service dog organizations to help build out screening paradigms so that they can identify dogs that are more likely to go through their training programs, which can be thousands of dollars and a lot of resource for each one.

Becca: So if they’re able to more effectively identify, as you say, the flunkies versus those that will be successful. It’s a lot of time and money spent. So I’m aware of the a few programs that are in action right now, and we’re hoping to kick off many others.

Grant: Can you comment on the linkage disequilibrium (LD) structure in dogs? In my understanding, which could be wrong, the LD blocks are sometimes a bit larger and so on. How does that impact fine mapping? With a database of millions of dogs, how are you getting around these issues?

Becca: Yeah. So I think it depends. The general answer is the LD blocks are much larger. When people give that answer, they’re typically talking about purebred dogs, AKC registered dogs that have been selectively bred for multiple generations.

Becca: When you’re thinking about the entire dog population of the world, close to a billion, I believe about 750 million of those dogs are what’s called village dogs or street dogs or some combination where humans don’t actually control their breeding. In those cases, the LD blocks look much more like humans.

Becca: So pedigree dogs, those pure bred dogs, are much more frequently used in studies right now. And in those cases, it is generally more straightforward to fine map to the causal mutation. So those dogs, their LD structure, as well as the really great record keeping that breeders have at their disposal have made studies into the genetics of a variety of diseases very fruitful.

Grant: I guess maybe you could do a dog decode genetics or something, right? Where you have the pedigrees going back a bunch of generations, and you can genotype the living dogs.

Becca: Yes, exactly. We have a team based in Helsinki. We’ve talked about doing something quite similar. We’ve also been very lucky to work with dedicated breeders across the world who keep those pedigrees. And we’ve had efforts on going to map a variety of genetic traits and diseases through generations and generations.

Grant: And you also have cat products, right? Can you comment on what are the major differences between working with dogs and cats?

Becca: Everything. And I’m not a cat owner, but I’ve had cats in the past, and I think cat owners will appreciate that as well. At present, the products are quite similar. So we offer an ancestry report that shows that cat’s breed background, health risks, and traits. What we’ve found in working with cats is that their population structure is much more similar to humans. There’s much more admixture. There’s a lot of free breeding across populations.

Becca: And as some of our veterinarians like to say, that’s primarily because cats were doing a pretty good job at the job that humans gave them, which was to be pest control. It’s only in the past 100-150 years that cat fanciers have come in and have started to control cat breeding. And in those cases, you start to see the LD structure that’s more similar to what you see in pedigree dogs.

Grant: So what’s new?

Becca: A lot actually. We are super excited to announce that we’ve recently launched a brand-new breed detection system. And we’re happy to say that we’re now the most accurate breed detection system available anywhere on the market.

Becca: And this was a huge effort on our side. So we pretty much started from scratch two years ago. That’s where I came into Wisdom. And I also brought in a few lead population genetics from Ancestry.com and 23andMe. And one of our primary goals was to bring our pet parent community the best and most up to date science available.

Becca: So where we started was with a database that’s now over two and a half million dogs that represent breeds across the world. We’ve collected samples from over 50 countries at this point and over 15 years of documentation on the profiles of those breeds.

Becca: And we wanted to bring the insights that we’ve gleaned from all of that dog DNA to our customers. So in order to do that, we reasoned that we wanted to create a local ancestry classifier, which basically means that we could pinpoint ancestral breeds to very specific locations in a dog’s DNA.

Becca: The other thing we knew is that we wanted to leverage as much as possible of that massive two and a half million dog DNA database. And one of the challenges that population geneticists have faced since starting ancestry detection is a problem of computational power and efficiency.

Becca: So we started with that problem first to figure out if we could increase the speed of the ancestry calculation and decrease the computational power needed. One of my scientists named Daniel Garrigan, had this idea that he could take what’s called the Burrows–Wheeler transform, it’s an extremely efficient computational construct, basically, that rearranges character strings into runs of similar characters.

Becca: It’s used all over the place. So the primary uses for data compression, and it’s the basis of the bZIP compress. It’s also the B and the W in BWA, which a lot of people on this call, or excuse me, on this podcast probably recognize.

Becca: So he used that perspective. And he also recognized that back in 2014, Richard Durbin, who is a scientist at Sanger, published a paper on using the positional Burrows-Wheeler transform, which is a much more computationally efficient method versus something like a hidden Markov model.

Becca: So we could rapidly go through DNA sequence very quickly. It’s primarily being implemented in phasing right now. And finally, he thought he could apply the positional Burrows-Wheeler transform to the Li and Stephens chromosome painting model.

Becca: The chromosome painting model is, if you can imagine a map of chromosomes and colors distinguishing the most likely ancestral population in specific chromosome regions. So he proposed to apply the Burrows-Wheeler method to approximate the Li and Stephens chromosome painting.

Becca: And what he was able to accomplish is a much, much more efficient way to index thousands and thousands of reference samples and assign them to their closest match with a test DNA sample.

Becca: So what we’ve established is a new way of processing lots of data very quickly. And what that’s allowed us to do is to create the largest dog reference panel available and the most accurate way to predict a dog’s breed backgrounds.

Becca: We’re really excited, as I mentioned, and this product is now available through wisdompanel.com in addition to cat DNA testing there. They happen to be on sale now.

Becca: So it’s a really great opportunity to experience the new science that we’re bringing to our customers. One of the other really exciting opportunities that we’re going to open up in the next few months is a community science effort.

Becca: So I mentioned that we use a lot of the data that we’re collecting from our customers in key studies to help elucidate some of the genetic architecture underlying K-9 diseases.

Becca: In order to expedite that research and discover more in a shorter period of time, we’re going to start asking our community of pet parents about their dogs, and we’ll ask questions about their dogs behavior, their dogs health, their dogs longevity, and a number of other questions, similar to what human genetics companies have done.

Becca: And our hope over time is to start researching some of the information that our community has given us and then bring that back to the product itself so that we can start telling our pet parents about some of the health insights we can glean from looking at their dogs genetics.

Grant: Very cool. What do you think is to come? If you look way out, say, 10 years, how do you think genetics will impact pet owners, will impact veterinarians and veterinary care, will impact pets and service dogs?

Becca: Ten years from now, I believe that every single human, every single dog, every single cat, will have their whole genome sequenced. And we’ll be able to use that information in all aspects of our life from what type of diet we need, what type of exercise is going to enhance our longevity, what type of medications, what wellness regimes are ideal for our underlying genetics.

Becca: So much of what you’ve heard on the human side is also going to be true of the dog and cat side. We’ve seen over the past two decades or so that dogs and cats have evolved from a possession to an actual member of the family.

Becca: And what also happened during that period of time is that people and pet parents are expecting the same level of medical care for their pets as themselves. So now there’s a tremendous focus in bringing precision veterinary medicine up to the same level as human medicine.

Becca: So we’ve seen the market pay much more attention to this. Pharmaceutical companies focus much more on veterinary pharmaceutical pipelines that resemble human pipelines. And my belief is that that’s going to continue over time, so that the same types of genetic technologies that humans are going to start using on a day to day basis will also be applied to their free family members.

Grant: Do you think there could be a pet to patient pipeline, treat aging and dogs, and then take those learnings over to people?

Becca: Well, that is exactly what Daniel Promislow and Kate Creevy are hoping. They recently launched the Dog Aging Study. I think it was about a year and a half ago, and it’s been very successful. It’s NIH funded.

Becca: And their general position is that, as I said before, dogs in a lot of ways are sentinels for the human life and that they can be examined on a much more condensed time scale. So human life average is 70 years, dog average life, 10-12 years.

Becca: They can collect a lot of information about dog longevity over that period of 10-12 years and then hopefully translate those insights into applications for both humans and dogs.

Becca: To date, there has been a lot of interesting observations. One that we’ve known for years is that small dogs tend to live longer than large dogs. Why is that? We have some hypotheses. It’ll be nice to test them further. Are there exceptions to those hypotheses? Are there certain genetic signaling pathways that are underlying longevity or shorter life? And then can we reverse some of those pathways and actually extend lives and dogs? And then finally, the big question is, can we also identify similar pathways in humans and thus extent human life as well?

Grant: I wonder if you bootstrap your way into right into it. You get a revenue stream going in dogs and then use that to fund on the human work. Earlier, while you’re talking, I was hearing K-9 in the background. Can you tell us about your dog or dogs?

Becca: So, Peanut. Yes, I actually brought up Peanut during my job interview. I think it’s the only time that I’ve ever brought up my dog during a job interview.

Becca: Peanut is six years old and is a pretty funky looking dog. So we never really knew what she was. I tested her, I think my first couple of weeks on the job. When we bought her, she was supposed to be half Shi Tzu, half Bichon. For those that are familiar with what those dogs look like, each of them have what are called furnishing.

Becca: So they have furry eyebrows and a furry mustache. And Peanut has a naked face. So she looks more like a long haired chihuahua. That’s always what I thought she was.

Becca: And then I did the genetic testing and low and behold, she actually is a Shi Tzu and a Bichon, but she carries this unusual trait for both of those breeds and that she has a naked face, she doesn’t have furnishing. And for a while, when we had the old breed detection system, we didn’t have resolution into breeds on particular chromosomes. It was a different approach to hone in on the breed background.

Becca: This new approach is based on local ancestry, which means that we can map breed specific positions in the chromosome. So I got the Peanut’s full genetic map. And really interesting, on chromosome 13, on the top tip, she had two different colors, and those colors also mapped to the furnishing gene.

Becca: So it turns out that on that little tip of chromosome, her breed is actually Japanese Chin. So somewhere along the line, there was a Japanese Chin, or there was an unusual line that introduce this unusual phenotype.

Becca: So I have to say it was pretty fun to learn about her background and understand more of her behaviors and the reason why she sheds. And it really did help me connect more with our little Peanut. So that was a lot of fun.

Grant: That’s pretty cool. Is Wisdom planning to or do you maybe already have narratives like you get with direct consumer human genetics companies, where you have big explainers for things, and you can take someone through a little bit of a journey for the ancestry of their dog?

Becca: Yeah. So we actually were, I believe, the first company that introduced genetic family trees. So a representation of what a dog’s family tree could have been based on their DNA.

Becca: So I mentioned that we use local ancestry now, and we can use that information to basically determine what breeds came from mom, what breeds came from dad. And then we can go further from there, much in the same way that you can walk humans through their ancestors, migration through Russia, Europe, Africa, what have you, you can do something similar with dogs.

Becca: So you can trace back a little bit of Rottweiler all the way back in the grandparents, a golden retriever that was one of the grandparents, and so on.

Becca: We’re also in the process of looking into mitochondrial DNA and chromosome, and with those additional measures, we can track specific migratory patterns from the maternal line and the paternal line.

Grant: Super interesting. So what are you most excited about in the pet genetic space?

Becca: I’d say that pet genetics is a decade or so behind human genetics, and some people might look at that as a negative. I’m taking it as a positive, because what that means is that we can apply the learning from the past decade of them half from human genetics to pet genetics and hopefully leap frog with that information, even past human genetics to the next stage.

Becca: What that next stage is, is hopefully injecting some of the insights that we’re getting from the genetics into clinical practice. I’m optimistic that the change in veterinary medicine will be faster than the change in human medicine, and that’s for a few reasons.

Becca: The primary one is that the regulation is different, and in veterinary medicine it can be faster. Key example here is drug development. Instead of going through animal models and then eventually graduating to clinical trials, you can test the drug in the subject animal at the beginning. That does have an elevated level of risk, but it also means that drug development can go a lot faster.

Becca: At the same time, there are different types of regulation as far as devices and clinical decision support tools, where we have some more opportunity to work directly with practitioners to observe how these tools are impacting clinical decisions going forward.

Becca: So I’m hopeful that in 10 years, genetics is going to be one of the key elements in the tool box for veterinarians and vet techs and will be leveraged just as much as the standard blood panel that’s used today.

Grant: Vet schools better get ready, yeah?

Becca: That is certainly something that’s top of mind for a lot of vet schools now. There are just a handful of vet schools that have geneticists on the team, and I think that we’ve spoken to several that are interested in incorporating more genetics education into their fundamental program, similar to medical schools.

Grant: Very cool. So let’s talk about you. Where did you grow up? What were you interested in as a kid?

Becca: So I grew up in Delaware. I was born in Philly, and then I moved out to Delaware shortly after. And I was a pretty quiet nerdy kid. I don’t think I really realized that I had an affinity for school until seventh or eighth grade. And then I started to bring home good report cards. And I got attention from my parents, and I realized, oh, this is fun. So I kept on going that direction.

Becca: In high school, I’ll say that I was a legitimate nerd. I remember I was in this AP biology class. And we started talking about evolution. And I brought in a book that I had just been casually reading at home about the origin of humans. I mean, what 14-year-old reads about that stuff?

Becca: So surprisingly, I didn’t have a date to prom, but I think that that interest eventually evolved. I did in college, developed social skills, or maybe I just found a whole bunch of other nerds to hang out with who appreciated my nerdiness.

Grant: Now you go to the right college, you’re no longer weird?

Becca: Exactly. So I went to college, I went to UPenn, and I majored in anthropology. And I had no idea what I wanted to do. I tried out everything. I thought about being a doctor. I thought about being a lawyer. I thought about just not doing anything, being a consultant, which I think is what people do when they can’t decide what they want to do. So the whole thing.

Becca: Anthropology was always had a strength through my entire career trajectory because I was truly interested in human evolutionary biology, the origin of consciousness, migration through various continents, and that seed continued to go through grad school.

Becca: I did eventually decide to go to grad school. And I think part of that was thanks to some amazing mentors that I had as an undergraduate who encouraged me to stay curious and interested, and just enjoy graduate school and then figure out what would happen.

Becca: So I ended up in the NIH Oxford program like you as well, Grant:. And I did a PhD with Eric Green at the NIH in the Genome Institute and Zoltan Molnar at Oxford.

Grant: Shoutout to Zoltan, you’re probably listening to this. You need to come on the podcast.

Becca: So I’ll say hi to Zoltan, too, and I hope that you come on right after me and correct everything that I’m saying, or hopefully not correct everything that I’m saying. And I have to say that at the beginning, the connection between Eric and Zoltan was almost incidental.

Becca: So Eric is one of the pioneers genome technology in sequencing and had built a lab around comparative genome sequencing. Zoltan focused on neuroanatomy and development, which seemed like two completely different areas.

Becca: The way that they were the same is that they were both using a variety of organism across reptiles, birds, mammals, fish. There may have been some amoeba work in Eric’s lab, but the whole gamete.

Becca: What I thought is that, hey, maybe we can apply these really high tech genomic sequencing technologies to neuro and anatomical fundamental and figure out whether we can identify some key pathways that are conserved across very distantly related species.

Becca: In the end, we settled on an investigation of a variety of noncoding, excuse me, long noncoding RNA genes, and I can still rattle off their sequence of letters and numbers that don’t make sense to anyone else.

Becca: So I should credit Chris Ponting for first identifying these long noncoding RNAs and claiming their functionality, and Jasmina Ponjavic for doing some of the initial computational analysis to expose the exquisite conservation of these genes, which was really striking. They looked just like protein coding genes with a few exceptions.

Becca: So we just couldn’t figure out what they were doing. The other thing that was really interesting is that they were very precisely expressed in specific areas of the human brain, the mouse brain, the chicken brain, and that expression pattern was conserved as well.

Becca: I wish that I had a huge message at the end of this, and we discovered them, and we were intensely important for some biological pathway, unfortunately, and it’s often the case in graduate research, we couldn’t. We still believe that they are likely involved in regulatory processes. I actually haven’t looked at them for a while, so I don’t know if there have been further studies on them.

Becca: I actually made a knockout mouse for one who didn’t have a phenotype, which was is quite disappointing, but I think it certainly gave me a lot of tools that I’ve used through my professional life.

Becca: And what I tell my team over and over is how important failure is. It sucks in the moment, but it makes you stronger and it makes you more creative, and it makes you more intuitive. And it forces you to think in different ways and think about how you can not fail the next time or just fail better or faster so you can move on to the next thing. And I think that that skill in and of itself has been so critical to my success in startups.

Becca: And now at Wisdom Panel in product development, in particular, I think one of the mantras is to fail fast so that you can move on. And that’s certainly something that I think PhDs do very well.

Grant: So how did you get into biotech?

Becca: For a number of years, I had a curiosity in biotech, and I think that that started mostly during my time at the Genome Institute because it was so connected with biotech and academia. So it was nice to see the interface between them and the differences.

Becca: I moved to New York after grad school. I just followed my husband there. I was finishing up my PhD. I had just submitted my thesis and I was waiting to defend.

Becca: So moved over with him and thought it’s New York, I’ll find a job. I thought that I wanted to be a professor, so I was looking for a postdoc in the area. And I did find a quite short-lived postdoc at Cornell. It was at a great lab, but I realized very quickly that it wasn’t for me.

Becca: And this was 2010, 2011, funding was not great at that time. So I worked there for a few months. And I have to say Cornell had a really great professional development program, in addition to working directly with postdocs on an academic trajectory.

Becca: So they hosted a number of career development events and I attended all of them. One in particular stood out to me. So I went there. I listened to the presentation. It was given by an alum named Piraye Yurttas Beim on a new company that was called Celmatix.

Becca: At the time, it was five or six people, and she was talking about stepping across the line from academia to startup world and how she did it. And I was so inspired by her presentation that I actually just walked up to her right after and I said, I love what you’re doing, how can I start?

Becca: And two weeks later, I was at her office in the meat packing district in New York, and the rest of history. One of the lessons that I last thinking about it now that I’ve told several of my team afterwards is how important networking is. And it’s something that I hated so much. I hated getting that advice, but it’s really the best advice.

Becca: And through my career, I think that’s really how I’ve navigated. I’ve figured out where I want to go. It’s just by talking to people and making connections and maintaining them.

Grant: What do you think people do wrong when it comes to networking?

Becca: Not networking. I don’t think there’s too much you can do wrong. I think the worst thing that will happen is that you’ll walk up to someone and they’ll walk away from you. So you don’t really have that much to lose.

Becca: So I’d say just go in with an open mind and introduce yourself and talk about what you’re interested in. In general, most people are relieved that you’re making the first step in introducing yourself.

Grant: And now that you’re on the other side, what’s changed about your perception? What misconceptions did you have as a grad student and postdoc that you can now dispel?

Becca: I’ll start as a college student because I was so focused on success, and I had a really narrow definition of success. So I define success as getting good grades and being in the good graces of your professor.

Becca: I hit this realization in grad school that that doesn’t really matter anymore. It doesn’t matter if you got an A or a B or a C or a D. What matters is that you’re doing work that you think is important and engaging.

Becca: It took me a really long time to process that and actually make it part of my view on the world that success is great or financial success or the other way that people see you, but that’s not actually going to significantly impact your state of being. It really comes down to how happy you are, how motivated by work you are, that you have a good work life balance and so on. So it’s something that I’m still working on, but I think that it’s so key. And I wish that I had known that earlier.

Grant: Yeah, I think that’s a process a lot of top students go through as they get through their 20s and sometimes into their 30s.

Becca: Yeah, it’s funny. Actually, I had this professor and he had these two young sons who called his PhD students gradual students instead of graduate students. And that really stuck with me because I really did feel like I had this extended out of lessons through my PhD.

Becca: You don’t have quite as much responsibility, I’d say, as if you just jump into the corporate world. There are a lot more people looking out for you, which is really nice. I had great relationships with my mentors, and I think I’ve really lucked out because they were watching out for me and making sure that I was making productive decisions. But at the same time, I didn’t feel that push or that weight of responsibility until I finish grad school.

Grant: Right. What advice would you have for yourself five years ago?

Becca: Let’s see, five years ago, I had just had my daughter. So I had a six month old at home and I had taken some time off of work. And I was really confused about the next step, actually, because of all of the emotions when responsibility is running through my mind and probably running through most professional women after they have their first kid.

Becca: So I have to be honest, I desperately wanted to stay home. When she was three, four months old, I thought about just taking a couple of years off of work.

Becca: I ultimately chose to go back to work to start out part time and then ultimately to go back full time. And I’m so happy that I did. I can say it’s a personal decision. And I have many friends that have chosen other paths that worked out best for them.

Becca: But I think that what I’d probably tell myself five years ago is that looking now at my colleagues, and there are different trajectories, you eventually get to the place where you want to be.

Becca: It might take a couple of years longer if you choose to spend more time at home, but you’ll be grateful for the time that you spent at home, or you can choose to go back to work earlier.

Becca: Something that helped me later on is a call that I had with one of my mentors, Mark Adams. And it was actually when I was considering switching careers, moving from human fertility, where I have been for a number of years at Celmatix to pet genetics, which was pretty drastically different.

Becca: I was worried that if I took a step away from human genetics that I wouldn’t be able to go back. And what he told me really stuck with me. He just said, maintain your storyline. Just make logical steps that continue to build on your experience. If this is going to give you the opportunity to grow as a person, to get more experience in population genetics, to explore something that’s more consumer focused, go for it, and then obviously you can bring it back to other areas. So I could also pass that along to my younger self.

Grant: And how have you found the transition from individual contributor to manager?

Becca: In a lot of ways, it’s like going from a single person to a married person to a person with a family. So it’s actually nice that my wife followed my career that way, and I was able to apply some of my mom’s skills to play professional life and backwards.

Becca: So I think what that means is that you start thinking about people besides yourself, you need to. And I think if you’re a good manager, you need to pick your team’s interest ahead of your own in order to succeed. Otherwise, your team’s not going to be functional.

Becca: So what I try to do every day now is think about how this is going to impact this person, this person, and this person before actually making a decision. I’m also much more intentional with my messaging and my explanations.

Becca: I think that being a manager has helped me grow quite a bit as a person. And as I mentioned, I think it’s made me a better mom in some ways. As an individual contributor, I think that there was a bit more freedom to try and fail. But I’ll say that that might also have been because of the environments where I happen to have very supportive managers that offered me constructive criticism or support at key points that I needed it.

Grant: And do you have any final words of wisdom for our audience?

Becca: I think I found the most success in just pursuing my interests and satisfying my curiosity. And in general, even if things felt uncertain, they usually work themselves out.

Becca: And what that’s given me is the opportunity to have a really interesting career and work with really interesting people. So I hope that I can pass that insight along to my team and my mentees and my kids.

Grant: I think that’s pretty core. You’ll always be the best at being yourself and at doing what you like to do. And so where you find that intersection with what the world needs and so on, it’s a good place to be.

Becca: For sure.

Grant: Well, thank you so much for joining us.

Becca: Thanks so much, Grant. It was great catching up.