— Victor, how did you come up with the idea of doing neurogenetics? Children, say, fifth graders would hardly, perhaps, dream of such a career…
— Neurogenetics surely did not occur to me in fifth grade, but I already knew I wanted to be a biologist at the time. Like many children my age, I wanted to do traditional zoology, which in my mind's eye was all about riding out on the prairie with a pair of binoculars to observe wildlife. I was inspired by the books of Durrell and Lorenz. I grew up near Lake Baikal in Siberia. We had this eco camp in the wild for 3 to 4 weeks every year. I spent lots of time doing short mini-expeditions in the woods.

— Did you bring your binoculars?
— Yep! I loved animals and enjoyed watching them. I wanted to be like Bernhard Grzimek and spend my whole life on expeditions, studying animals. I was into behavioral study a lot. I devoured books about animal behavior in the wild.
Later, maybe in ninth grade, I discovered organic chemistry. It was fascinating to find out that molecules have three dimensions. In contrast to inorganic chemistry with its formulas, organic chemistry could be visualized in stereo-coordinates. Simultaneously, I developed a taste for genetics. A few years later, in my sophomore year at the university, I found a way to combine genetics and molecular biology with behavioral science. Eventually I decided that I wanted to investigate how behaviors are inherited.

— But you were studying medicine, weren't you?
— That's right. I graduated from medical university in Moscow, but I went to the School of Biomedicine, not general medicine per se. I see no contradiction there with my career choice. The university trained biomedicine researchers.

— How do you identify yourself? A biologist or a medical professional?
— I'd say I'm somewhere in between. I consider myself both a biologist and a medical professional. The classification has been rather nominal in the past 10-15 years. I can see no major difference between medicine and biology, and I view medicine as part of the latter.

— You mentioned you were into genetics and behavior. What's your take on the theory that our genes are the ones to dictate our entire behavior?
— There is decent research out there on how much of the different aspects of behavior and intelligence are determined by genes and how much is determined by environment. A person's IQ (though not all scientists accept it as a credible indicator of human intelligence), for instance, is 70% owed to their genes and only 30% to the environment. This data isn't exactly up to date, it's about 10 years old, but I don't think these numbers have changed much. Our behavior is heavily influenced by our genes, although the upbringing is also an important factor.

— How well do we understand the impact of genes on different aspects of behavior? In my understanding, it rarely happens that you can point a finger at some gene and say it's 100% responsible for this thing or that.
— This is one of the areas that, for the next 15-20 years, I believe, will keep scientists busy trying to figure out the gene combinations that influence behavior. For instance, genes definitely influence aggression levels. The same is true of proneness to depression and personal temperament. In many cases, we do know which genes contribute to shaping certain behaviors. But those are still descriptive research. We have a hard time trying to credibly link certain human behaviors to specific genes. We have identified sets of gene variants that are more commonly found in persons with certain predilections, and less commonly occur in persons without them. We know these gene sets exist, but we don't know how they work exactly or how they interact with each other.

— You perform the majority of your research on mice. Do we know much about some other model organisms whose genomes have been studied well enough?
— There exist many different genetic models. There's one of them the fruit fly, Drosophila melanogaster, for instance. We understand quite a bit about it. For example, we know it has this single gene that defines a specific cell type in the nervous system. This cell type governs the mating behavior of male flies. If you activate this gene in a female, it will exhibit male behavior patterns in courtship. Things get much more complicated with mammals. In this department, we have no real understanding of the causes of specific behaviors. We are aware of numerous genes in mice that, when inactivated, make them more aggressive or alter their social behavior.
But some behavioral aspects are specific to humans or primates. We cannot study those in mice. Although, disorders like autism or aggression, for instance, lend themselves to accurate modeling.

— Are the findings of these experiments in model organisms partially applicable to humans?
— Yes, they are. Almost everything we learn from experiments on mice we can apply to humans, but not the other way around. It is impossible to study all aspects of human brain structure, function, and behavior on mice as humans have too many new genes acquired through evolution.

— Since you started doing research in this field, have there been any groundbreaking discoveries that made us completely rethink what we know about these processes?
— Yes, there have definitely been breakthroughs. When I first came into this field nearly 25 years ago we knew next to nothing about the molecular basics [of cerebral cortex development], although we knew a lot about brain anatomy on a descriptive level. We had an idea of how stem cells divide and what happens next, but we did not know the number of stem cell types, how they become neurons, and many other things as well. Science has made tremendous steps forward in the past 25 years. We know a whole lot more now. We have identified the key molecular players. We know which genes and proteins can make a cell stop dividing and become a neuron. These advancements were made possible, first of all, by extensive research and, second of all, by the arrival of new technologies.
It all began when we learned to read the human genome and many other genomes. This made it easier for geneticists to study numerous different things underlying cortical development. Then a range of technologies came on the scene enabling quick and targeted genome modification. One of them, CRISPR-Cas, has revolutionized our work over the past decade. With CRISPR-Cas, experiments involving gene activity manipulation take very little time: procedures that used to take 3 to 4 years to complete can now be performed in six months. Another major breakthrough came with the deep sequencing technology. We are now equipped to read the transcriptome, which is the genome part active in a specific cell, and it's quick and affordable.
There's been a breakthrough in neurobiology, specifically in the methods that enable the analysis of interneuronal connections. Now we can, for example, manipulate a mouse brain into such visibility that we can observe the interneuronal connections in 3D. This takes research to an entirely new level. We used to be able to see a two-dimensional image only. We would take the brain, make thin cross sections, and observe what was going on there. Whenever it was possible, it was hard work to process those sequential slices into a three-dimensional image. Now we can take a whole brain, illuminate it, and see all the myriads of interconnected neurons in different regions of the cortex and other parts of the brain.

— Can we now claim that we understand the human brain well, or is our understanding still in its infancy?
— If we take infancy as our starting point, I would say we are at 3rd or 4th grade level of elementary school. We have a good grasp of the basic principles of cortex development, but we don't see the full picture yet. A great deal of effort is required to unravel all the mutual interactions of the genes, or more precisely, interactions of products of genes. Things are further complicated by the fact that, when we study a molecular cascade, the thing at the top is the transcription factor. It is a protein that controls the activity of many genes. It has several hundred target genes whose protein products interact with each other and, in turn, trigger further cascades. We are yet to fully appreciate the complexity of those cascades. Our current methods aren't sophisticated enough to study those cascades in their entirety, rather than their individual components. We are equipped to manipulate 1-3 genes simultaneously, so we can study what happens when those genes are disrupted. But how all those cascades are connected with each other remains a mystery to us. The methods we possess to this day are largely descriptive. I would say our level is arithmetic and basic geometry, but we have yet to master advanced mathematics.

— If I understand correctly, the issue is we don't have the right tools yet?
— That is not always the case. We do have the tools in some areas. But we wish we had massive amounts of data. Let's say, there are dozens of cascades involving hundreds and thousands of molecules. We know the key players. What we don't know is how they interact with each other, which targets they relate to, or how all this brings about the result we observe. That's one thing. The other is that we lack the necessary data processing technology for certain tasks. We have yet to figure out how to analyze the interactions between complex systems.

— Do you think machine learning could help with this? Or is it even applicable to your work?
— Machine learning fits right in. For instance, single cell sequencing would not be possible without machine learning. Without machine learning and artificial intelligence, we would not be able to process the outcomes we get when we sequence the transcriptome — the active part of the genome of several thousand cells.

— Are you saying that if the data processing problem were solved, you would know where to get data?
— Not exactly. We'd be able to figure out which cascades and which sets of molecules are active in specific cells, but we still wouldn't have the tools to manipulate a section of the cascade and see what happens when we alter the ratio of molecules or remove some of them.
The traditional strategy in genetics is to remove a component of the cascade and see what happens. In other words, to break it down and try to build it back. We could break it down at the level of one, two, or three molecules, but no technology available to date that would allow us to either break or gently manipulate the cascade in such a way that the molecules would only lose half of their functionality but retain the other half. When it comes to several hundred genes or proteins, we don't have the technology to manipulate those quantities, and I have no idea how to deal with this problem.

— If this problem were to be solved, would it bring us closer to being able to cure the diseases you mentioned?
— I believe that just about everything we're trying to figure out about the function and development of the brain is bringing us closer to being able to cure people. Here is a simple example. We have this model for mouse autism. In this model, one gene is deactivated – it is the same gene that is found mutated in patients with autism, — resulting in these mice having 30% more synapses. We assumed that this effect can be reversed pharmacologically, meaning that we can prevent the formation of excess synapses with a basic drug, a widely available immunosuppressant.
In mice, we succeeded in offsetting the mutation of the gene with this drug, bringing the number of synapses back to normal. If we find a similar molecule in another similar situation and learn how to use it to block excess synapses in the mouse model, this knowhow might be applicable to humans, because synaptogenesis — the formation of synapses between neurons — is a slow process that starts late.
There are some adverse developments we can't offset: for instance, when the stem cells stop dividing during embryogenesis, leading to what is known as microcephaly — a small and underdeveloped brain. But we can devise new diagnostic methods that will allow us to detect severe microcephaly in the embryo very early in the pregnancy.
For other conditions, such as epilepsy, if we understand which molecular cascade disruptions  cause the disease, we can influence those cascades and develop treatment options.

— How far are we from achieving this?
— I'm no futurologist, I can't make predictions. Science progresses in leaps and bounds, not steadily. Some things are unpredictable. Take CRISPR-Cas, for example: only 3-4 years before the first papers no one would have believed you if you'd told them we would soon acquire such a miracle genome editing facility. What I'm saying is that it's hard to predict what might be possible 10 years from now.
Sometimes it seems like we're this close to a solution, but nothing comes of it. Let me give you an example from some other field, let's say, regenerative medicine. 10-15 years ago, I thought, with the apparatus of regenerative medicine, we would soon be growing artificial cartilage to help people with arthrosis. When I read papers where my colleagues reported how they were successfully growing cartilage in test tubes, I thought that in another ten years, we would be growing cartilage in patients' knees and everything would be super. But now we are not that far yet and I do not know when it will happen.

— What major projects are you working on right now?
— All projects we've been working on for the past 20 years can be subsumed under one general topic, which is study of the molecular basis of cerebral cortex development. Specifically, we're interested in those aspects that, when disrupted, cause human pathologies such as autism, mental retardation, dementia, and epilepsy. Disruption at any point in the developmental process, from stem cell to fully mature neuron, can have serious consequences. We're interested in most or all of these aspects. We study how the stem cell behaves, how it divides, how it differentiates into a specific type of neuron and goes on to form connections with other neurons, right up to the formation of a mature nervous system. We strive to understand which genes control these processes.

— Do you have any pressing questions of your own that you'd like to answer?
— I have a few dozen favorite molecules that affect different aspects of cerebral cortex formation. I'd like to piece together a mosaic picture of how these molecules interact and how a stem cell develops into a mature neuron that knows which other neurons to connect with.

— How do you pick your favorite molecules?
— Early in my career, when I worked exclusively with mice, I selected them based on their activity level, expression, and belonging to certain protein families. We were after the genes that are active during the key stages of cortex development. These were primarily transcription factors, the ones at the top of the cascade that controls everything happening inside the cell.
Once we had identified those transcription factors, we slowly figured out how they interact with each other and what this leads to. We used reverse genetics methods: first you identify the gene and you assume it plays an important role in the process you're studying. Then you disrupt it, you try to fix it, you study the processes taking place in the organism after the gene disruption, and make inferences about its functions.
We've been more into direct genetics in recent years. We started with humans. We have this major project we're doing with the Tomsk Research Institute of Medical Genetics, which has a database of patients from over a thousand families with children who are either autistic or have developmental defects like mental retardation or epilepsy. We start by trying to identify mutated genes in these patients and then replicate the "broken gene" effects in mice or at cellular level using induced pluripotent human stem cells. This technology was developed about 15 years ago. Now we can grow mini-organoids, something like "mini-brains", in-vitro, and study the molecular cascades that happen there. In our projects, we create artificial brain organoids from patients, and we investigate the disruption processes and try to figure out if we can fix them.

— Why did you switch over to this approach? Is it more effective?
— I don't think I switched. Rather, we started using this approach in addition to our traditional work exclusively with mouse models. First of all, we did it because now we could. The Research Institute of Medical Genetics didn't even have that patient database 10 years ago, they only have it since recently. Incidentally, this progress was made possible in part by the funding provided by the RSF.

— How do you foresee further development in your field?
— It's going to be something like a Metro map. A person coming to Moscow for the first time will typically move around the city station to station. Leaving the metro at Park Kultury, the visitor will get a sense of what Moscow looks like within a 200-meter radius of the station. Next, the visitor travels to Krasnopresnenskaya and takes a stroll there. While they keep taking the metro, their image of Moscow will remain fragmented, like a quilt: they know what the station neighborhoods look like, but they have no full picture of the city. I would compare the current state of my field of science with that person's idea of Moscow: we know the hubs, but there are plenty of aspects we don't know yet. They may be key, but we don't know that for sure. Science has yet to figure it out.
Now this is what we desire and look forward to in the next 10-20 years: we want to make such a map of Moscow – of the brain – that would show buildings and addresses, not only metro stations.
In other words, we aim to understand all molecular events, how they interrelate, and their outcomes.