On the surface, it might not seem like autism spectrum disorder, obesity, and multiple sclerosis have much in common. But despite the fact that these conditions share very little in terms of symptoms, treatments, and even affected populations, the way they're being explored by researchers on the cutting edge of neuroscience is more similar than you might imagine.
On the surface, it might not seem like autism spectrum disorder, obesity, and multiple sclerosis have much in common. But despite the fact that these conditions share very little in terms of symptoms, treatments, and even affected populations, the way they're being explored by researchers on the cutting edge of neuroscience is more similar than you might imagine. Some brain disorders hide in the smallest details of our genetic and protein code - so host Fiona sits down with researchers using tools like next generation sequencing and proteomics to unravel these hidden codes, and hopefully open new paths to treatment.
Featured guests:
Shreejoy Tripathy, Assistant Professor in the Department of Psychiatry at the University of Toronto and a Senior Scientist at CAMH.
Simon Thebault, Clinician Scientist and Assistant Professor at the Montreal Neurological Institute at McGill University.
Paul Sabatini, Assistant Professor in the Department of Medicine at McGill University.
This work is supported by the Azrieli Foundation.
[theme music]
Dr. Paul Sabatini 00:06
In the past, we once referred to scientists as, you know, “the last of the explorers”. We’ve mapped out at least the surface of the Earth, really diving down into biology, or one of the last pursuits where you can truly discover something new. I don’t know if there’s any feeling like when you get new data, especially if you’re proven right. Not always. But when there’s times when you had a hypothesis and the data comes in and it looks like you’re right, across all the defeats and challenges that science faces, that for me is what keeps me going.
Fiona 00:35
This is Bold Minds: Future Leaders in Canadian Brain Research. I’m your host Fiona Sanderson. I work at Brain Canada, where our mission is to bring together funders and researchers to enable health innovations for Canadians. The Future Leaders program is made possible thanks to an anchor gift from the Azrieli Foundation and matched by Brain Canada through the Canada Brain Research Fund. Come along with me as we journey into the bold minds and labs of researchers who are redefining our understanding of the brain. [music ends]
[rousing music] On the surface, you might not assume that autism spectrum disorder, obesity, and multiple sclerosis have much in common. But despite the fact that these conditions share very little in terms of symptoms, treatments, and even affected populations, the way they’re being explored by researchers on the cutting edge of neuroscience is more similar than you might imagine. This episode we’re zooming in, way, way in, beyond even the microscopic level. Some brain disorders hide in the smallest details of our genetic and protein code. So, today I’m talking to researchers using tools like next-generation sequencing and proteomics to unravel these hidden codes and open new paths to treatment. This is Bold Minds. [music ends]
Today I’m joined by...
Dr. Shreejoy Tripathy 02:03
I’m Shreejoy Tripathy. I’m an associate professor at the University of Toronto and a senior scientist at CAMH.
Fiona 02:12
He and his team dive deep into the genetics of neuropsychiatric and neurological disorders like schizophrenia, Alzheimer’s, and autism to understand the significance of genetic variations. How they affect the health and behaviour of brain cells, and how these may inform how we think about, diagnose, and help people living with these conditions.
[whooshing] I’m also joined by...
Dr. Simon Thebault 02:35
My name is Simon Thebault. I’m a clinician scientist and assistant professor and I work at the Montreal Neurological Institute at McGill University.
Fiona 02:43
He and his team are investigating how the immune system contributes to tissue injury and repair of the central nervous system in multiple sclerosis, and developing and testing biomarkers to enable earlier detection and precision medicine approaches to treat people living with MS.
[whooshing] And I’m also joined by...
Dr. Paul Sabatini 03:02
Paul Sabatini, assistant professor at McGill University.
Fiona 03:06
He and his team study the neurobiology of feeding behaviour and energy expenditure, specifically how a subset of neurons in the brain called POMC neurons control our appetite and energy balance, and how dysregulation of these cells contributes to obesity. Shreejoy, Simon, Paul, welcome to Bold Minds.
Dr. Paul Sabatini 03:25
Thanks for having us.
Dr. Shreejoy Tripathy 03:26
Thanks for having us.
[theme music]
Fiona 03:30
So, Shreejoy, let’s kick it off with you. You study the genetics of various neuropsychiatric and neurological disorders, one of those being autism spectrum disorder. We know that autism is very complex, so in its presentation, there are people living with autism with a huge spectrum of characteristics and severity. On a genetic level as well, there’s no one gene we can point to and say, “Yep, the mutation in that gene results in autism.” So, tell us about your work and how you go about understanding the effects of an intricate genetic landscape.
Dr. Shreejoy Tripathy 04:02
I think about autism, or at least genetic forms of autism, as basically disorders of brain development. If someone has a mutation in a gene, one of, let’s say, thousands of genes that are important for how the brain develops, how it grows from a single cell, from when we’re first made as a zygote all the way until we develop into, you know, full babies, like, there’s hundreds, thousands of genes that are important for that process. So, if you have a mutation in any one of those genes, that might lead to autism. So, rather than thinking about something like, let’s say, Huntington’s, where there a single-- where mutations in that gene have a single cause, in autism there’s mutations in many, many genes that could all disrupt neural brain development, and any one of those could cause what we refer to as autism.
Fiona 04:47
And Paul, you also study a very complex condition with a variety of physiological effects, obesity. So, we heard from another researcher, Alex Caron, in episode three, about how obesity can be thought of as a disorder of the brain. And your work digs even deeper, so into a subset of a specific type of neuron in the brain that regulates energy balance and appetite. So, can you tell us about your research?
Dr. Paul Sabatini 05:11
I would love to. I study what’s-- in a field known as the melanocortin pathway. It’s a system of neurons in the hypothalamus that is fundamentally and critically important for regulating both short-term and long-term energy balance. So, this is the fundamental system that controls body weight. It’s implicated in both monogenic forms of obesity, as well as, you know, the common forms we see in relation to the modern food environment. Both of these monogenic and common forms of obesity impact the melanocortin pathway. And while the melanocortin pathway has been studied for many, many years, POMC neurons, which are a fundamental component of the melanocortin pathway, there’s still many unanswered questions about how they function, how obesity impacts their ability to control appetite, and how to target them to treat obesity.
Fiona 06:00
That’s so interesting. So, you know, there’s this pocket of neurons in our brains that are affecting our physiology in such huge ways, and we don’t really know much about them yet.
Dr. Paul Sabatini 06:10
Yes, lots and lots of questions.
Fiona 06:11
So, Simon, your focus is on multiple sclerosis, another complex disorder with system-wide physiological effects. And your expertise is in neuroimmunology, so how the immune system and the brain contributes to both injury and repair. Can you explain to us what exactly is MS? So, at the level of the central nervous system, how do we determine someone has MS, and how does the immune system contribute to this?
Dr. Simon Thebault 06:36
Yes, MS is what we consider a complex neuroinflammatory condition. It doesn’t fulfill the classic postulates you’d need to fulfill to call something autoimmune, although in my opinion it almost certainly is. But it is very common, so it’s the most common non-traumatic reason for a young person to have a neurological injury. And it sort of plays out in two ways. So, on the first hand, there’s a fairly well-established series of events which we can sort of encapsulate with the word relapse biology. We think it’s an outside-in sort of wave of inflammation against a particular antigen that comes from the periphery and comes into the central nervous system and is targeted against myelin. So, myelin is the-- if you like, it’s the coating around the electrical sheath of the nervous system that helps the electricity get conducted efficiently. So, you lose your myelin, and in these focal inflammatory attacks, it can affect many parts of the nervous system. So, there’s that.
On the other hand, there’s a much more kind of indolent, smoldering process, if you like, that happens in the background. And we’re sort of just getting to understand that process a bit better, which I’ll sort of encapsulate with the word progression. So, this idea that people can get worse over time in the absence of having new attacks, but the prior damage that was done never quite went away. And there’s kind of two bits to that. We think partly it’s a neurodegenerative disease. So, once you have kind of ropey myelin and these denuded axons that don’t really like sitting there trying to conduct electricity with no plastic sheath around them anymore, then that can make them die kind of quicker.
And on the other hand, we think-- and this is really the focus of my research-- that when the immune system comes in, so during those first inflammatory attacks, it doesn’t actually leave. It kind of sets up shop and causes this kind of CNS compartmentalized inflammation, if you like, [pickle juice 08:24], that even within a contained central nervous compartment can cause ongoing damage independent of those focal inflammatory attacks that set the whole process off at the beginning. We diagnose it based on clinical features and MRI features. We use something called the McDonald criteria. And it’s important to note that we don’t actually fully understand the whole disease. It’s kind of more of a syndrome, right? So, we make a diagnosis based on a constellation of features, which an international group of experts has come together and said that, “This is MS.” And that definition is actually a moving target. We just had our newest revision of the McDonald criteria, and we’re sort of constantly aiming to implement new and emerging biomarkers so that we can make the diagnosis earlier, so that we can institute treatment earlier and minimize the sort of irreparable damage that happens if we don’t stop that focal relapse biology and progressive biology as early as we can.
Fiona 09:20
So, like you just said, your goal is to develop biomarkers that can be used in the clinic or the diagnostic lab to detect and inform treatment of MS, something that may be important for your own patients as a neurologist. But can you tell us what is a biomarker? What makes a biomarker good or usable or informative?
Dr. Simon Thebault 09:39
Yes, I mean, the biomarker is a bit of a buzzword. It means lots of different things in different contexts. So, you can have biomarkers of disease risks. That might be a genetic test, for instance. We have biomarkers of diagnosis. So, in the context of MS, for instance, we do lumbar punctures to look for evidence of inflammation. We do MRIs, as I mentioned. So, they’re diagnostic tools. And we also have kind of monitoring biomarkers that we can follow over time once somebody has a diagnosis or not and tells us how a patient is responding to treatment or not. And finally, I guess you have prognostic biomarkers. So, we have these things you can deploy hopefully early on in the disease that may detect whether my patient is on a good or a bad trajectory, and that might inform, again, the intensity of the treatments. And it can mean lots of different things as well. It could be an imaging test. It could be a blood test. It could be a spinal fluid test. It could be a digital eye movement tracking device. It’s a very broad term. It just simply means a useful disease surrogate that we can use to hopefully better detect, predict, and monitor a disease.
Dr. Shreejoy Tripathy 10:39
Simon, may I ask, like, is it somewhat well-established now that MS is caused by a prior incident of Epstein-Barr virus, like basically getting mono?
Dr. Simon Thebault 10:49
Yeah.
Dr. Shreejoy Tripathy 10:49
Would you agree with that?
Dr. Simon Thebault 10:50
Yeah, I do, and that’s a major focus of my lab, actually. So, I mean, there’s been more than 40 years of research on the role of Epstein-Barr virus in multiple sclerosis. I think it’s safe to say at this point that EBV, or Epstein-Barr virus, is a necessary but insufficient risk factor for the subsequent development of MS. I personally think it’s the first kind of nudge of the snowball, and then other things have to happen in the wrongly predisposed individual and in the wrong sequence to ultimately lead to MS. What I think is going on fundamentally is it’s now known that EBV does two kind of unfortunate things, I guess. Number one, it’s a molecular mimic. So, bits of EBV look very much like bits of myelin, and so that’s enough for an initial trigger. But then on the second hand, EBV is a herpes virus, so you have lifelong latency. And guess what? It happens to infect and reside within B cells, so one of the key cells of the immune system. And what is EBV trying to do once it’s infected a B cell? Well, evolutionarily it’s supposed to survive. So, now you’ve got this kind of latent infection within a B cell, and at the same time bits of this latent infection look like the bits of myelin, and I think that setup is probably what ultimately leads to MS. I mean the big question remains, why do most people get infected with EBV, 95% of the normal population, but only a very small percentage end up with multiple sclerosis? And I think that’s probably down to sort of things like genetics and bad luck, quite frankly. So, it’s not just about getting EBV, for instance. It’s about when you got EBV.
So, we know that people are supposed to get EBV on average before the age of 10, but if you get EBV later in the shape of infectious mononucleosis, so these are teenagers that end up getting it-- or 20-year-olds that end up getting EBV and they end up in bed for two weeks, that is the-- sort of the bad version of EBV that seems to be the setup and predictive of future MS. So, it’s not just the virus itself. It’s the wrongly predisposed individual plus the sort of wrong time of life when they acquired the infection.
Dr. Paul Sabatini 12:55
[In 12:55] MS, you know, do you see defects in both oligodendrocytes and immune function? Do you think it takes kind of [two partners 13:02] to make this disease progress or one-- can you drive it just with one cell type defect?
Dr. Simon Thebault 13:08
I think it’s a very heterogeneous disease that we’re just understanding sort of the heterogeneity of. As we mentioned before, it has diagnostic criteria, which is kind of a binary 1-0 and not really based on much in terms of biology. It’s an imaging end result that we’re seeing on an MRI scan, and it’s a clinical attack which again is an end result. One thing I do observe as a clinician quite frequently, and I don’t know what it means, you can have two patients with very similar looking lesions on the MRI, very similar sort of attacks of baseline, who have very different results. Some people, they sort of rapidly get back to normal and everything’s fine, and other people are in a wheelchair. So, clearly there’s a great difference in host response to injury that I think may well play a large part in progression.
And one of my colleagues, Dr. Adil Harroud, who’s also funded by Brain Canada, does research on just this. In his studies, he’s been able to show that actually some of the best prognosticators of the genetic level for people having not MS versus non-MS, but people that have MS having worse versus better MS, has actually got more to do with genetics of CNS, innate and repair capabilities rather than anything to do with the immune system, which I think is fascinating.
Fiona 14:25
That is fascinating. So, you all work on conditions with lots of different symptoms, different areas of the body affected, no specific causes really nailed down, and lots of people affected. So, autism, obesity, MS, these are some of the most common conditions. And each of you are working on extremely specific aspects of these conditions that have massive system-wide impacts. So, in essence, you’re zooming in in order to zoom out. So, how do you put the puzzle pieces together? How do you go from the effect of a genetic variation or a subset of a cell population or a circulating biomarker to what it’s doing in the context of complex outcomes?
Dr. Shreejoy Tripathy 15:05
A paradigm I often use is, like, maybe-- the idea of maybe the first thing you do is you build a reference atlas. And so, reference atlases kind of work in many ways, like you have a map of the world or a map of a city, and once you have that map you kind of decide where you want to go. So, in my project that’s funded by Brain Canada, we’re trying to make a reference atlas of different mRNAs expressed by neurons as they develop. So, we’re working with really talented experimental colleagues at University of Toronto using a bunch of latest edge, cutting-edge tools that we can basically learn a lot of stuff about tiny things made by neurons that change as these neurons are growing up. And so, then we now have this really great resource, so we know, “Okay. Well, this part of the genome is being transcribed into RNA, and that seems important as neurons undergo this transition from baby neurons all the way to grown-up neurons in a dish.” And then when we have that reference resource, that we can go use that resource to re-look at genetic information from individuals with autism disorder. We can say, “Well, do those people have genetic mutations in these new regions of the genome that we’ve said are important for neuron development?” So, that enables us to then link, you know, a mutation in this part of the genome to the response or importance of that part of the genome for normal neuron development.
Dr. Paul Sabatini 16:22
Yeah. I like that answer. You know, this is really scaffolding up. We’re starting, you know, really small, but the idea is, you know, this is step one in the process. And unless you kind of understand the fundamentals of the system, it’s really difficult to understand how the system breaks in pathology.
Dr. Simon Thebault 16:39
I see things a little bit differently where, as a clinician, understanding biology is obviously the end goal, and at the end of the day the perfect biomarker is going to be the thing that explains the biology, and that’s probably the perfect treatment target often as well. But sometimes that level of understanding is maybe unrealistic, and I think at least in my field, where we’ve had some great advancements over the last couple of decades, many of those things have kind of happened by accident. We then realize by reverse translation what is actually going on. So, for instance, in MS, the most efficacious treatments right now happen to work against CD20, which is a B cell targeting monoclonal antibody. We used to think it was a T cell disease, but only when we started doing trials on B cells did we realize how important they were. So, if we can then take a bigger picture approach and then, “Okay, what’s working? What’s not working? What can we first hypothesize and then reverse engineer, if you like, from those data sets that are often collected in the context of a clinical trial to inform on the biology?” And maybe stumble upon a useful biomarker or treatment target as we go.
So, we can really drill into what this given patient in front of me is, and I think that’s really the dream, at least for me. Is to have tools available that don’t rely on this one receptor or this one marker or this one cell type in particular, but synthesize that data, perhaps in a data-driven manner, to provide the best synopsis at the level of the biology, which is informed by the outcomes we’re able to measure in the context of clinical care.
Dr. Paul Sabatini 18:13
Yeah, complex diseases, we just need more study. I don’t think it can all be argued. I mean, MS, obesity, autism, without further investigation, whether it’s, you know, human clinical trials or basic research, we can’t make progress without research.
Dr. Shreejoy Tripathy 18:27
Yeah. One thing kind of maybe that was-- maybe Fiona mentioned that’s shared among our-- you know, our projects is maybe the use of somewhat unbiased techniques. Like in my case, studying all of the genes in the genome as opposed to having to pre-decide which one are you going to study and kind of investing in that. It seems like we probably all use high-throughput methods to some degree in our work, I would probably guess. The low-throughput method is you measure one thing, you get one result. A high-throughput method enables you to measure many, many things at once. That enables you to study a lot of things at once and then focus in on the things that probably matter. And I think that’s really important and valuable and I think that’s very much a theme of this era of science.
Fiona 19:11
And so, we know that often a myopic view of a complex disease is needed in order to unravel that complexity. Maybe that’s coming from top-down or bottom-up. But as we talk about how you go from that zoomed-in view to understanding the bigger picture, now I want to go kind of in the reverse direction and ask you, what led you down this path or towards these specific approaches that you’re now using?
Dr. Shreejoy Tripathy 19:37
For me, it’s, like, maybe a love of data and a love of using computers and computational automation to answer important questions. And also wanting to do a lot of different things, I think that that’s part of how I ended up here. And I think we’re very much in an era of big data and that suits my personality really well. I love having massive new datasets out of latest, greatest technologies, and I love exploring them. AI is this really amazing tool. I think it’s this really amazing tool that just accelerates that process, like, you know, 10 or 100-fold.
Fiona 20:14
Paul, what do you think?
Dr. Paul Sabatini 20:16
So, I agree. I mean, that drive for discovery, it’s, like, an animating thing for me. Right now, the-- you know, this project we’re really excited about, it’s kind of a two-pronged approach of both new biology but also tool development. And we’re hoping that the tool development will facilitate this new biological insight, but we also hope the tools will be useful for other neuroscientists doing basic research. This is kind of what’s really driving me right now.
Dr. Simon Thebault 20:40
From the very beginning, I knew I was always interested in neuroscience. On the one hand, in immunology. I even sort of remember the first few days of medical school, those being my favorite things, which is a bit weird because most people hate those topics. So, there was interest from the beginning. And then when I actually became a clinician and started focusing on neuroimmunology, realized how much sort of hope there is. Like, so, we made a lot of progress, actually, in my field in the last couple of decades, but still how much there is still to go. And you look at what is possible in the lab and then what is actually done in the clinic, and it is a huge gulf. Even if in my career I could bring 10% of that magic that we’re already able to do into the lab and make it clinically applicable, that would be a career success. And I think the final thing for me has actually been opportunity and mentorship. I’ve had some kind of great people along the way that I definitely wouldn’t have been able to get here without. So, I think it’s a combination, yeah. So, interest, having a good question to ask, and then having some people that we’re able to stand on the shoulders of to make it happen.
Fiona 21:40
So, you all work on understanding the key processes happening within the cells of our brain that have downstream, multi-symptom, multi-system effects, and the ultimate goal is really to help people, right? To help the people living with these conditions live better, healthier, longer, happier lives. And I mentioned earlier that these are some of, you know, the most common conditions, so what does it mean to you to work on something where the long-term effects of your findings could really make an actual difference in many lives?
Dr. Simon Thebault 22:10
I mean, I’ve been told I should do a podcast on being a clinician scientist, because, I mean, it sounds really cool, and it is in many, many ways. Like I get to wear multiple hats and do different things, and one time I’m at a conference, and the next minute I’m in a clinic, and the next minute I’m down at the lab, and now I’m pitching something to the government to try and get a test implemented, and that’s really cool. But at other times, it also feels like having multiple jobs at once, which is harder, and certainly harder in the early career side. Especially if you’re a clinician scientist, it’s not a path to be taken lightly. You definitely got to have a lot of-- as one of my mentors would say, a lot of fire in the belly to push it through and succeed. And I think I’m lucky enough to really work on that divide, where on the one hand, I work on patient material, and my actual patients in my clinic become my research participants. And we’ve already had a few kind of success stories of, say, biomarkers we’ve been able to innovate, or at least contribute to the innovation of, and that are now being routinely used in clinical care. So, it’s kind of really neat to be able to see things that you feel like you contributed to, even in a small way, and then see that actually play out amongst patients, and that can even happen in a fairly short-term basis. So that’s probably what I love about my job the most.
Dr. Shreejoy Tripathy 23:21
One thing I really appreciate getting with my role at CAMH is, like, I’m often involved to sort of speak to the public and advocate for research happening at the hospital being important. And one thing I’ve learned from that is that just doing research on the brain and doing research on mental health is important because it illustrates that mental health, it’s not just in your head, it’s in your brain, and mental health and problems of the mind are brain problems, they’re real. And I think that’s very validating, and it’s very-- it can be very powerful for people, families whose lives have been impacted by mental health.
Dr. Paul Sabatini 23:56
I mean, I was thinking along those similar lines. That having interacted with people who live with obesity, there’s still an enormous stigma around living in a larger body. And this idea that obesity is a chronic disease is not-- is-- maybe it’s starting to change, but still not widely accepted by many people or even governments. Just showing that there’s basic biology behind obesity and body weight, and this is not an issue of willpower or lack of effort, I hope to be one of my contributions as well as many other people doing the same work.
Fiona 24:30
You guys all kind of touched on this, and I don’t have to tell you, but research is hard. So, science by its very nature is asking questions that you don’t yet have answers to. Sometimes your hypothesis is right, it’s wrong, it leads you to other questions. Sometimes your methods work, sometimes they don’t. Sometimes you don’t even have the tools you need, and you have to build them from scratch. So, what fuels you? What drives you to keep going even when you hit roadblocks?
Dr. Shreejoy Tripathy 24:57
Like, for me, it’s the possibility of discovery. The possibility of discovering something new that I didn’t know yesterday.
Dr. Paul Sabatini 25:03
Yeah. In the past, we once referred to scientists as, you know, “the last of the explorers”. We’ve mapped out at least the surface of the Earth, really diving down into biology, or one of the last pursuits where you can truly discover something new. It’s really cool. I mean, I don’t know if there’s any feeling like when you get new data, especially if you’re proven right. Not always. But when there’s times when you had a hypothesis and the data comes in and it looks like you’re right, I think that’s-- across all the defeats and challenges that science faces, that for me is what keeps me going during those leaner times.
Dr. Simon Thebault 25:33
Yeah, I think you-- I don’t know, I’m going to attempt to quote Winston Churchill. He said something along the lines of-- what? Success is going from failure to failure and keep getting up again, and that’s certainly been my experience. That there’s definitely a lot of failure and you’ve got to be okay to fail fast and move on.
For me, I have to be sustained by the successes when they do happen. So, it’s about getting energy from the moments when things do work or that grant does come or the paper finally gets published. And in the bigger picture, I guess, in the kind of the grand scale of the satisfaction, is knowing that-- or hoping that what you’re doing is directly relevant to the people that we’re trying to treat and make the lives better for.
Fiona 26:13
I think that’s just very well said and an incredible note to leave off on. So, thank you so much for joining us. Shreejoy, Simon, Paul, thank you for chatting with me today. And to all of our listeners, thank you for joining us on Bold Minds.
[theme music]
Dr. Paul Sabatini 26:26
Thank you.
Fiona 26:29
Bold Minds is a Brain Canada production with support from the Azrieli Foundation. Our executive producers are Jillian Donnelly and Kate Shingler. Our lead producer is Jess Schmidt, with editing by Morgane Chambrin. Thanks for listening.
If you enjoyed this episode, we’d appreciate it if you could send it to a friend. If you want to learn more about Brain Canada and our Future Leaders program, please visit our website at braincanada.ca. [music ends]