Office Hours: Episode 2

Episode Extras

• Learn More About Dr. Mete Civelek

  Dr. Mete Civelek joined the University of California, Los Angeles after being a faculty member at the University of Virginia for ten years. He is currently a Professor of Molecular Medicine at the David Geffen School of Medicine at UCLA. His primary appointment is in the Department of Anesthesiology and Perioperative Medicine as well as Institute for Precision Health. He has secondary appointments in the Departments of Human Genetics, Medicine, and Bioengineering. He is an elected Fellow of the American Heart Association and the American Institute for Medical and Biological Engineering. He is currently the co-chair of the Diversity, Equity, and Inclusion Committee of the American Institute for Medical and Biological Engineering. He served as the chair of the Diversity, Equity, and Inclusion Committee of the Department of Biomedical Engineering at the University of Virginia for six years. He is a member of the Program Committee of the American Heart Association’s Vascular Discovery Conference and served as the chair of the Program Committee of the American Society of Human Genetics.

  Links to Dr. Civelek's Work

  1. Civelek Lab
     

• Read the Transcript

  [Intro Music] 

  Liz Wayne: Hi everyone, and welcome to office hours with Liz Wayne, a brand new podcast brought to you by the Biomedical Engineering Society. I'm Liz, an assistant professor in bioengineering, and I'm going to introduce you the world of biomedical engineering through my eyes or my voice, from genes to machines, biomedical engineers can do it all. We'll dive into how discoveries are made, how research becomes medicine, and what it's actually like working in academia today. So, we're really glad you're here.  

  So, I want to start with a little anecdote. And let's ask this question: if your genes could talk, what would they say? Now, if you're thinking about your genes and you say, Oh, my jeans would say “too tight, please go up a size”. Those aren't the genes we're talking about. I mean those little bits that tell ourselves what to do. I think about this a lot now, because I recently gave birth, I have a kid, and he's absolutely the cutest thing on earth. Don't argue with me unless you want to fight - absolutely adorable, Mete knows this. But I look at his face sometimes and I think, how come I did all of the work, but you look like your dad. I mean, think about it. Isn't it crazy how 27 chromosomes with all those genes can just encode all of our lives right from the beginning, our height, our eye color, hair color, and eyes and body composition even, and all those things sound kind of cute. But the other scary part about what our genes can do is that they can also encode information about what kinds of diseases we might get, or how we might even just respond to viruses. Like, does anyone remember the pandemic? It's actually a little too crazy to think about, but fortunately for us, we have an expert here to talk about it. I want to welcome to the show our expert today who's going to tell us everything about our genes. Dr Mete Civelek. 

  Mete Civelek: Thank you. First of all, thank you for having me on this podcast. It's really fun to chat with you and laugh. Of course, my name is Mete Civelek. I am currently a new professor at UCLA in the medical school. I actually recently moved here from the University of Virginia, and my laboratory does study genes and how genetic variants lead to cardiometabolic disorders. 

  Liz: Okay, that was a lot. So, first of all, as an east coaster, now turned West coaster. Welcome. 

  Mete: Thank you. 

  Liz: The humidity has been great. My skin loves it here. It's always green. I miss the hustle and bustle, but, you know, it might be the best coast, but that is not what our genes are telling us. I'm not saying that, because our genes are telling us. And so, you study genes and how they can inform how we get other diseases. Now, when I think about your research, I think I think of that pickup line, which I have never used. Maybe you have, I don't know, you know, when someone like, there's always this quintessential story of someone walking down the street and you go: “hey, hey, what you got in those jeans?” You know, it's like looking at like it's a compliment in the genes, right? But you're not talking about blue jeans. You're talking about molecular genes. And so can you explain to me in very layman terms, very easy terms, because I am a biomedical engineer, but I'm not a genetic one. What are genes and what do they tell us? 

  Mete: Okay, yes, I am not talking about the genes that we wear, although I like wearing them. These are the ones that are essentially in our chromosomes. So, a gene is essentially a piece of DNA, DNA sequence, and it has many different elements, but ultimately, it's a template for a messenger RNA. Genes are expressed as messenger RNAs, and then those messenger RNAs are translated into proteins. So ultimately, a gene can code for a protein. Not all genes code for proteins, because some genes stop at the RNA level, but the product of a gene gives instructions to cells to do things, right? Grow and divide and other things. So, it's a very fundamental piece that is kind of like the building block of life, in a way. 

  Liz: Okay, and why not just make protein? Why does a cell have to have these multiple layers? 

  Mete: Because it creates diversity, and DNA is an extremely stable platform to store information. You probably are aware of studies where researchers, typically anthropologists, isolated DNA from 10,000-20,000-year-old bones, because the DNA is so stable that we can isolate those pieces of DNA and actually sequence them and learn about human history. It's a very efficient and safe way to store information, and then it can be expressed in multiple different ways and lead to diversity of messenger RNA and proteins. 

  Liz: So, it's a way to get a lot from a little. 

  Mete: Yeah, exactly. That's a great way of putting it. 

  Liz: Okay, these small sequences with just really small changes can lead to big changes, and they're stable, so that you can pass them down, they don't degrade as much. Because I also imagine that if the signal isn't stable, then it can change when we don't want it to change, and that leads to cancer, or, you know, these other kinds of mutations that you don't want. 

  Mete: Exactly. It can also be beneficial, as you can imagine, right? Like those little changes throughout human history can also be beneficial or can lead to diseases. 

  Liz: How is it beneficial? 

  Mete: Well, you can change the way you store excess energy, for example, or our cholesterol levels are predicted to have changed over time, because there are differences in our diet, you know, then, you know, 1000 years ago. So, we're talking about evolutionary changes. I think what you were also mentioning are what we call those somatic mutations. So those are basically changes that occur in a cell, in a DNA, in a cell, that can lead to cancer, but they don't get passed on to your kids. For example, there are different flavors of changes that can happen in a DNA sequence. 

  Liz: Right, I've heard of the germline, the things that get passed on versus the things that accrue in your lifetime, right? But then these, these don't affect like, offspring.  

  Mete: They don't, exactly. 

  Liz: Very interesting, very confusing. So, I studied physics in undergrad, and I did not take biology. I actually signed up for biology, and then two weeks in, I was like, I can't, I don't know. I just, like, I just couldn't do it. They wanted to start you off with, like, the phylogenetic tree, and, like, you have to understand how everything's all related. And then, yeah, and then I learned my lesson, because then in grad school, they were like, Oh, you're a physicist. You'll have no problem. It's so easy to go from physics to biology, the biologist can't go to physics. And so, I signed up for grad level biology courses. And actually, it was called Supercell, and I think we just started nicknaming it Superhell, because it was just like, insane, yeah. So, I have more respect for biology. I would not crash course it the way I did, but it was super complicated.  

  But I do understand genes help us pass information. They tell cells what to do, and then by being able to make different changes on the genes and how they get transcribed and then translated, the cell can have more flexibility in how performance functions or adapts to our environment. But let's also get then to thinking about now that we have a basic understanding of genes, and I mean very basic, there's this whole field now of what you do with that kind of information, and I think that's linked to this idea of precision medicine, right? So, can you describe what precision medicine is? 

  Mete: Well… (Laughs) Everyone defines it differently, but let us, both of us, take a stab. How about that? 

  Liz: Okay, I’m laughing because academics are the worst. We are so terrible at this. Because if you ask a question, their brain just kind of like fries, and they're just like, what? Because they're like, wait, what exactly do you mean? Like, define the definition you want me to like, they just like, cannot process. It's like, knowing too much is like, I don't know where to start. That's not the right word. So, yes. Okay, now take me where you want to go with this. Help me understand precision medicine. 

  Mete: Well, let's actually look at it from a gene perspective, right? If you're a geneticist, you are going to define precision medicine as: let me sequence your DNA. Let's sequence Mete’s DNA. Let's sequence our listeners DNA and learn as much… 

  Liz: They signed up. 

  Mete: They consented to that, right? 

  Liz: We can get it through the airways – 5G, just kidding. 

  Mete: Yeah, let's not get you in trouble. But you know, the idea is that we learn about your genetic variants. You know, what essentially makes you unique, and these certainly include things like that increase your propensity for, let's say, a heart attack, or Alzheimer's and things like that, right? And then let's take preventative measures, you know, so that you live a longer or healthier life. So, let's say you have an increased cumulative genetic score for heart attacks. Well, you might want to see a cardiologist earlier in your life than later in your life, or you might want to get your cholesterol checked, because that is a very well-known risk factor for cardiovascular disease. So that's the way how geneticists, I would say, define precision medicine.  

  In addition to this, of course environmental factors play a big role, right? Whether you live in the south or north or east coast, west coast, a Scandinavian country versus, you know, Africa or an Asian country. These will also determine your environmental exposures.  

  Liz: And can you give an example?  

  Mete: Diet is different, right? Dietary patterns are different. Your work pattern might be different. I mean, as academics, honestly, we're sitting most of the time, right? We are not very physically active. So, these all include, you know, this kind of information is only included in the precision medicine approach, where you're trying to tailor both preventative measures and also treatment measures, you know, or treatment paradigms to the individual. Breast cancer is a perfect example, right? Somebody is diagnosed with breast cancer, you do these sequencing studies to look at brca1 mutations, because actually the treatment is different if you have the mutation or don't have the mutation. This is all part of the precision medicine approach. 

  Liz: So, like you're saying, it encompasses everything that you could do where you're gathering information about an individual, combining that with what we scientifically know, maybe from large studies of populations, to say if something is upregulated, or if we know this factor has changed, it increases your risk. And how do we apply the information that we know from our scientific studies to an individual's life in a way that might help them with preventative strategies, or if they already do have the condition for management and treatment. 

  Mete: For sure, for sure. Just to give you one other quick example, there is, for example, pharmacogenetic studies that show that a certain percentage of the population will have muscle pain when they take statins, these cholesterol lowering drugs, and that is really dependent on a single nucleotide change. And there are alternatives for those individuals. So, if you have high cholesterol and you need to reduce it, if you know your genetics, you're probably not going to try and take statins, because you know it's going to cause muscle pain, and you're going to discontinue it. So, it can be important even in the treatment stage. 

  Liz: I see, and you know, this was actually a question I wanted to ask you, because when I think about precision medicine and the incorporation of genetic information, which correct me if I'm wrong, this means, now you're taking someone's DNA. You sequence the DNA, because if DNA is a code that gets translated into protein, then you can produce a big script of all those codes and then read them and compare them, right? So you have a code. Then why start at the gene level? And not to say, well, everyone should exercise more. But what you're saying is either -that it's not just single, it's combined, that you would combine both of these factors. 

  Mete: Right. That's a really good question, actually. Of course, we should all exercise, right? Because we know the beneficial effects of exercise, but if every one of us exercises for 30 minutes three times a week, it's not going to have the same benefit on us, right? There have been some studies that have been replicated multiple times, and this is in the realm of cardiovascular disease. So, let's take one example. Let's say I have this high genetic risk for having cardiovascular disease, and then someone has a low genetic risk for having a cardiovascular disease. Now, that someone with the low cardiovascular genetic risk can lead a quote-unquote “unhealthy lifestyle”, and that could include, for example, smoking. That could include, you know, having a sedentary lifestyle, or, you know, having a lot of saturated fat in their diet. I, on the other hand, can lead a “very healthy”, quote-unquote, lifestyle, right? Exercise, eating, you know, well, blah, blah, all those kinds of things. At the end of the day, both of us may have the exact same risk for cardiovascular disease, so just doing exercise and eating healthily is not going to work equally well in every individual. And many, many studies actually, population-wide, genetic epidemiology studies showed this. 

  Liz: Yeah. I'm really glad you mentioned that, because there's always that one story that you hear where, like, well, my grandfather smoked until he was 80 and never got lung cancer, right? I'm like, well, your father had great genetics, right? Versus people who've never smoked the day in their lives dying of lung cancer, right? So, there are differences, and so it's almost like, the way I see this too, and I think my work, I like to think about these things with me from different perspectives, is that this is what precision medicine is also what happens when the intervention doesn't work for every person. 

  Mete: Absolutely. 

  Liz: And it no longer, in a way, is even ethical to still tell everyone to do the exact same thing when we actually have data that tells us that they will not get the same benefits from them. 

  Mete: Absolutely, I mean, because that is a psychological cost. There is an economic cost, there is a health cost to all of those things. 

  Liz: Right? Or, well, this person has a higher BMI than I do, and they have perfect blood pressure, whereas this other person maybe has no weight, and they're doing this. Or I think we can also think back to Covid. This is probably another big example, very fresh in everyone's memory, where the general trend is, if you were older, you were at higher risk of having severe complications, hospitalization or morbidity if you have preexisting conditions. But then there were also things where you weren't under any indications of having any severe adverse effects from getting covid, but you did have adverse effects that were, in a way, unexplainable, but probably had some other background information. So, this is also really interesting. My question now is, how do you do that? How do you possibly do that? Because it still feels like a fishing experiment, where, like, you get all this information, but what do you do with it? How do you organize that information? 

  Mete: Yeah, it is definitely a fishing expedition, but most of population genetic studies are, really. The best cohorts, if you will, are the ones where you track people's health over a long period of time with multiple time points and looking at various phenotypes, it could be, you know, gene expression in your blood, it could be, you know, your BMI, it could be your cholesterol levels, because then you can really create predictive models, right? Somebody that started, if you will, with these genetics, which you, you know, get from your parents. Obviously, that's not something you can change. And grew up in this kind of an environment and had this kind of work and had smoking, or, you know, drank maybe one beer a week, etc., etc. And then look at what happened in terms of their cardiovascular risk in, you know, a 30-year period, there are certainly such studies, right? Framingham is a very, I guess, famous example. This is a study that started in the 1960s that essentially established cholesterol as a risk factor for coronary artery disease. I think, you know, you can analyze the data, obviously, in multiple ways, numerous statistical methodologies, but the key is to get what we call a longitudinal cohort and have the genetic epidemiology along with that. 

  Liz: I see. You want to see how things change over time. 

  Mete: You want to see how things change over time. And it's truly important to emphasize the importance of the diversity of the cohort that you start with, and diversity at all dimensions, right, where genetic ancestry being one of them, but male, female, you know somebody who lives in an urban environment, somebody who lives in a rural environment, and all of those things truly play an important role in the accuracy, if you will, of our predictions. 

  Liz: Yeah, I think diversity really means everything and everyone and we need- the more information we have, the better the modeling. I'm thinking about the 2016 elections, not for the purely political outcome of it, but I think this was one of the first instances that people were using Facebook to help with targeting voters. And I think what Cambridge Analytica, but I remember the stat at what they were saying is that with 200 likes you knew, someone better than a friend you know, and at 400 likes like they were just being on the scale up. And at a certain amount of likes or information you had, you knew them better than their mother knew them. Yeah, right. And so, every bit of information helps people build a profile that they understand you better. But this is maybe not just a big brother kind of way we're kind of thinking. This really helps people better predict and understand the trends that are going to be important, so that when you do walk into the doctor's office, they can give you information about you that can help you and then kind of clarify. How do you feel about public trust here? 

  Mete: This is exactly what I want to talk about, too, because, you know, not everybody maybe wants to know these things, right? For example, the famous APOE gene and Alzheimer's, right? There are certain variants which can increase your risk of basically getting Alzheimer's by nine-fold compared to someone who does not have that genetic variant. These are the APOE gene variants. First of all, I should say that I did one of those genetic testings. It was 23andme and of course, I personally was super curious about this genetic variant, and I went and I looked for it. And first of all, they say, “hey, are you sure you want to see this variant”, right? And I'm like, oh, shoot, yeah, okay, I want to see it. And then they say, “are you really sure?”  

  Liz: You can’t take it back. 

  Mete: And I said, Yes. And then mine was basically neutral, because there's a protective one, there's a risk one, and mine was neutral. But I have a very close friend who found out that, you know, she has this super high risk for Alzheimer's disease, and she regrets finding that out. She's like, I don't know what I'm going to do with this. I don't even know why I looked at this, because it's obvious that I am going to get Alzheimer's. So where do we draw the line? 

  Liz: I don't know. I mean, I think I've also run into sprinkling of data coming my way myself. And friends have thought about data like that comes in after a diagnosis and like, what do you do? I don't have any answers. 

  Mete: The one thing I thought about is, if it's going to inform treatment, then it's probably important, like, again, breast cancer is a perfect example. But there are certainly other cancers where you can sequence and know the mutation truly. You know direct treatment based on her two positive brca, positive, etc., in breast cancer does make a difference in the treatment regime. But if there's no treatment, that becomes a more difficult, I think, question to answer, do we tell people whether they have this variant or, you know, risk allele and things like that? I mean, if somebody wants to know, of course they should know. 

  Liz: Well, but they can't take it back.  

  Mete: Yeah, exactly, you can't take it back. What do you do with that information? 

  Liz: And something I can imagine is if they're thinking about children, or if they're trying to make that decision to have children, or for family members to even know, hey, we are carriers. So I mean, something that's come up for me is maybe not been, whether it's important, is it going to help me to know, but more so is it going to help my relatives to know that this is a thing that our family has which could be passed on just to be considerate of, or even it may, may make you think of that Aunt or grandfather who passed away in a really confusing way, make more sense if you think, oh, wait, were they also carriers for this thing? Were they? Did they also have a higher risk for clotting that we didn't really know of, right? Which is a thing I've actually heard of more often than I would have thought, right? But then you don't know that when someone just dies suddenly, right? And then you may test later on. But so, what we've been talking about a lot, too, is when we have tests, or there's some sort of building up a foundation of like, we know this one matters, and we're asking people, do you want to know? But there's still the other side of it, which, and I'll just put this as in the researcher category, where you collect this data, and then what happens to this data? Even if you don't tell anyone, right? You still have that information, or you have samples that you can assay genes and do scientific studies on, that are beneficial because maybe you're trying to find new biomarkers or new drug interventions. But how do people protect that data or think about data protection?  

  Mete: I think that's a really good point for when we do genetic- first of all, I should say, when we do genetic studies from populations, almost all the time, we cannot return the genetic results back to the individuals. Now there are studies that have been designed to do that if people opt in, part of it is because we don't do our -for example- sequencing in a clinically certified lab, right? So, there is obviously that kind of a difference in a clinical lab. There are more, I guess, precautions that are taken when they're doing sequencing and identifying pathological genetic variants, but there are certainly studies that have been. designed to do that, where they send the samples to a clinical lab, and then when they're consenting, the participants, and they're not all patients, because most of the population studies, they are not patients, they ask if we find something, do you want to know? So, there's that aspect of it. The other aspect is obviously 99.9% of the time the data is de-identified. So, if I'm downloading a data set from an NIH repository, I may get the genetic data, but I don't know that it belongs to you or someone else. So, the data is anonymized, and that certainly increases the protection. I should say that though there have been some studies that essentially identified the individual by just using their genetic sequence, which was kind of fascinating.  

  Liz: How is that possible?  

  Mete: Well, there are databases where people have consented, you know, to share their data, genetic data, right? And if you happen to be a relative, a close relative, of that person, you know, there are methods to infer, oh, it could be x, y, z, and it's kind of interesting. There are actually even cases, the crime cases that have been solved by using this kind of approach. 

  Liz: Yeah, I’ve heard of these, a little scary. 

  Mete: So what is kind of scary, right, is that you may consent to have your, let's say, genetic data to be shared, but your son may not, and if I have access to your son's data, I might still be able to, you know, if I am kind of nefarious, I might be able to find who he is because you decided to share your data. Again, it's another topic that I think we as a population, and, you know, individuals should talk about. 

  Liz: Yeah, and at least be aware. So, you know, I also, in the course, I was teaching an immune-pharmacodynamics course, and we went over pharmacodynamic modeling study, and it kind of came up through this paper, we were reading that, like lab core, all these other kind of companies that you give your samples to, that they there's like a market to kind of resell that data, right? Let me put it this way, students really didn't understand or know before that point that there was even a possibility that the data records from when they had gone to the doctor's office and gotten a blood draw were even records that could be, although yes, de-identified, could actually be given or sold to people, or how those were used in models. And I thought it was, you know, kind of interesting, because one, you know, there it helps you get towards that longitudinal question that you were getting at where that is really important information, that a pool of information to learn from people, so that you can think about questions like, what is the appropriate time to give a drug? Or, like, how do these other values or their assays help people, right? I think that is generally helpful, but I just wonder about, like, the data market, so you're an expert, and I think you probably have some confidence that this is okay. What would you tell people who knew nothing to kind of feel more assured about the system and like the benefits of precision medicine or the gene sampling that we do? 

  Mete: Well, you know, I certainly don't want to paint a grim picture. Obviously, your data may leak at some point. I mean, our passwords and social security numbers are somewhere in the dark web, right? I think the key will be to really strengthen the existing laws so that you're not discriminated based on your genetics, right? There certainly are laws that you know prevent that from happening. So even if your data leaks, someone gets access to your genetic data, etc., you're still protected. I think that researchers, for the most part, are careful. I cannot vouch for everybody, obviously, but there will be some bad actors. You know this, unfortunately, we're learning so much about human nature in the last 5-10, years, let's say maybe like witnessing nefarious human nature. So, there is always that possibility, if you're truly, truly scared, and you know that, not just for you, but for your close relatives, this could certainly include your mom and dad and siblings and your kids? Yeah, I understand why you may not want to share your data. I personally shared my genetic data with my class that I was teaching because honestly, I thought it's going to be somewhere anyways, you know, I might as well just share it, because they were doing some predictive model using genetic data. You know, it was actually kind of a fun exercise where I also gave them two other people's genetic data that was out in the open, and then I wanted them to kind of predict which one was mine based on, like, the features that they could find. 

  Liz: That’s actually really interesting. Did they do well? How did they do? 

  Mete: Well actually, most groups did really well because, and they also told me things like, oh, you have, like, this increased risk for high blood pressure. And this was maybe 10 years ago, and I think like five years ago, I started taking blood pressure medication, so they were able to predict stuff. 

  Liz: I want to go back to the point that you were making earlier about the benefits of this, because again, study after study has shown that giving everyone the same intervention does not lead to the same benefits for each population, and we're doing a disservice to people when we don't include this information. And I think if you think about People's Hospital experiences, they anecdotally know that, or they feel like they're not being heard or listened to, or like, hey, I don't think this works for me, but you keep telling me it does. So, there's always this pro or con, the same as like, engaging in anything in our digital society now, where what you gain from the internet, social media, all of these things are communication, access, awareness, information, having your voice heard. And then there's other things that may not be as desirable, but I think increasing the literacy around them, and a very important point you mentioned are the laws. That's really what we need. Instead of saying, let's avoid it altogether. 

  Mete: I couldn’t agree with you more, really. There's obviously the practice and the sociology of medicine that is totally independent of the data sets and things like that that we're trying to, you know, collect. I mean, chest pain in women, for example, is often times ignored by cardiologists because they do certain diagnostics and they [say] “well, you don't have an obstruction in your coronary artery. This has to be psychological” where there is actually a microvascular disease in these women, and they needed to do these additional tests to figure this out. So, I would argue, and this is obviously not my field, but the practice of medicine also needs to adapt to this whole precision medicine idea. 

  Liz: But I think that’s hard, yeah. So, when I think about cancer a lot, and I think about how there's a standard of care, the first option. You fail the first option; you go the second one. You fail the second one, you go to third and so forth. But because of what we know about cancer and its evolutionary nature, and every drug you give changes it, we much better if we could really know which one is going to benefit which person before we put them on that wild goose chase. And I think we're still figuring out, like, what really helped people. And I think that leads us to a computation in a data management and, you know, knock on a table, you know, AI machine learning, because there are some high dimensional problems have so many variables that we do need some way of thinking about these that aren't just why, well, actually it is y equals a mix plus b, but it's more complicated than that. 

  Mete: You know, AI and Machine learning is going to be with us right in the next several years in medicine. But then again, I want to emphasize the importance of the diversity of the data sets in every dimension, so that we're learning the right lessons. You know, in machine learning, there are many, many examples of, you know, when you learn something in, you know, let's say a European ancestry population that may not apply to an Asian ancestry population or an African ancestry population, etc., You know, obviously, the data sets really need to sample the population. That's it. I mean, that's the bottom line. 

  Liz: There's a question I want to ask you, because, you know, when do people get to talk to a genetic expert, right? But something I think about is it may be hard for people to understand when we say, for instance, we need to do things on looking at Asian ancestry and versus European or African, and also the shift from the word ancestry, the shift from race, which is a social construct, but even the way we use race, or had used race, to not be a social thing, but a biological construct, to looking at ancestry, but maybe talk about why we care about ancestry and why that pathway matters. 

  Mete: Yeah, you know, I think I may be able to give you know, certain examples, right? A power, l1, genetic variants are at a higher frequency in African ancestry individuals, and this leads to kidney disease, right? There are certain variants in people with East Asian ancestry where some anticoagulation drugs do not work in these individuals, for the ones with these specific variants. So, don't you think it's important for us to know this data about your, let's say patient, so that you can monitor them for kidney function, or you are not going to prescribe that specific anticoagulant and another one? I mean, in many ways, of course, there's a lot of admixtures happening in the world these days, and so the frequencies and things like that of the genetic variants will change in the next 1000 years. But as we are practicing medicine right now, this additional information is going to be important in certain cases. 

  Liz: So, when you think about a gene not working, what we really mean is that let's say a drug has a function, and that function is to target a protein. If a protein does something and it's upregulated and it leads to high blood pressure, I'm just using it as an example, then the drug is targeting that protein, and the protein, remember, is made from the genes. And so, if someone has a variant in that gene that changes the protein, it may mean the drug is not going to be responsive to that version of the protein.  

  Mete: That's one example, absolutely. 

  Liz:  And so that's the link in the connection where you want to actually look at these different variants or even develop drugs that work for that variant. And then when we think about ancestry, this is the way of saying, because we know our DNA, you mentioned at the beginning of our talk, that DNA is very stable, and we can measure DNA from thousands of years ago, and our DNA gets propagated from our offspring. And so, this is a way where we actually do carry information from thousands of years ago,  

  Mete: In many ways, yeah. 

  Liz. And so, in our genes, you get influenced by what we're expressing, our environment, the things that were- to some extent- what we're doing. And so, this ancestry is also about where were you at a certain time, where things were changing, or natural selection was occurring and evolving, and so it matters where you were from and where you're going, 

  Mete: Right. 

  Liz: And where you're living in the environment, and it would be plausible to have people who can look completely different, so the genes that account for skin color and eye color are only so small, so we can look the same, but then have different ancestries that actually make us more common than we might have thought we were before.  

  Mete: For sure, for sure. Yeah, absolutely. I mean, I am Turkish, and I don't know why, but Turkish people carry a certain genetic variant that typically leads to lower good cholesterol, the HDL cholesterol. I don't know why that happens, actually, but I think these are important things to look at and know basically. 

  [Music Break] 

  Liz: BME is a beautiful field, because you're usually drawing from so many things, and in our conversation that we just had, I heard genetics, I heard computer science, computer engineering, maybe, I heard epidemiology, I heard public health, I heard clinical medicine, and I also heard some information security and cyber security, and like, a little bit of sociology and psychology, because you all think of the emotions of the data, and like maybe a little bit of law here, because the data that you deal with is so sensitive that someone needs to think about how this is processed, right? That's a lot that you've had to accrue over a career of information, where you have to dabble in all of those to do your job well. 

  Mete: So as an academic, I'm thinking, Am I really doing my job well? But sure, let's assume I'm doing my job okay, but thank you. I appreciate that. 

  Liz: Let's swap roles. I'll be the full professor. You be the untenured assistant professor. 

  Mete: Come on. But yeah the biomedical engineering field does draw, if you will, expertise from multiple different fields, like, like you mentioned, I cannot imagine, you know, a biomedical engineer who doesn't know some basic physiology about whatever system that they're studying, right, whether it's immune system, whether it's cardiovascular system, whether they're doing imaging or studying at the molecular level, just to give an example. 

  Liz: So, what do you think a world without biomedical engineering would look like? 

  Mete: A world without biomedical engineering will actually have fewer advances in healthcare, honestly. And I think that biomedical engineers bring a very diverse perspective, because they have to be experts, like you said, in computer science or data science and molecular biology, and they look at things in a much more quantitative way. So, I think, honestly, precision medicine is a perfect field for biomedical engineers to study. But so many advances in imaging, in diagnostics have happened because of biomedical engineers. The DNA sequencing itself. The technology behind DNA sequencing, where you generate billions of DNA sequences in, you know, in an hour, is engineering. So, I think a world without biomedical engineers will have fewer discoveries done in, you know, the research labs and definitely fewer translations into healthcare. And I don't want to be in that world. 

  Liz: In your field, specifically, what do you think? What would be happening without biomedical engineering? 

  Mete: I think that the accuracy of predictions, predictions of okay, you have these set of genetic variants that will increase your risk by 20%-55% etc., will certainly be affected. And the cheap, rapid, widely available genetic tests will certainly be affected. 

  Liz: That’s interesting. 

  Mete: You know, most of the time we also think about, you know, commercialization, like of, you know, our technology and that has to do with reach, and that has to do with applications, etc. These are not necessarily things that you know. Let's say, quote, unquote, a pure geneticist will always think about. 

  Liz: And so you know, when you're in conversations with other people from other disciplines, because I imagine these are all multiple multidisciplinary projects that you work on, what do you find your role as a biomedical engineer is in those conversations, and in particular, the ones you think were like, if there wasn't someone doing my thing, my role, this wouldn't work.  

  Mete: I mean, it really, it's usually more on the quantitative side. I am always the annoying one who is like, did you use the appropriate statistical test?  

  Liz: Okay, first of all, do not just call all biomedical engineers annoying. 

  Mete: [Laughs} I said I am an annoying biomedical engineer. Not you, certainly. But you know, I have this one collaboration with a targeted drug delivery person. She's a friend of ours. You know her, Lola Eniola-Adefeso. So, you know, the two of us came together because I said, hey, you know we can use human genetics to identify targets for you. And she said, well, I don't know how to do that. I said, I don't know how to do the drug targeting part. And that is the role I think that we, the two of us, played. I usually find myself to be pretty multidisciplinary. Honestly, I can talk to the geneticist, I can talk to the engineer, I can talk to the computer scientist. I think that's what a lot of biomedical engineers can do much more comfortably than what I find [compared] to, you know, people who train in like, say, pure genetics or computer science and things like that. 

  Liz: Yeah. So basically, no one would talk to each other without a biomedical engineer. It would be less fun. 

  Mete: Not easily, not easily. We facilitate those conversations.  

  Liz: They would have to use Chat GPT, like the engineer said this, but I don’t know what you’re talking about. 

  Mete: Or we oftentimes say, “so you really meant to say this, or did you mean to say this?” But right, you have collaborated with people from different disciplines. I mean, the initial hurdle is always to learn each other's jargon and approaches.  

  Liz: What a wonderful conversation this has been. This has been so much fun. I've learned a lot. I feel so powerful knowing my genes contain so much important information and scared because I don't know what the information means and if I want to know all of it, but I'm glad people like you do know it all. 

  [Outro music] 

  Liz: If you have questions or ideas for a podcast episode, then email us. Our email is communications@bmes.org You can also follow us on our socials at BME society and our website for podcast details at BMES.org forward slash podcast forward slash office hours. Thanks for listening, and we hope to see you next time. 

   

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