The scale of neurology is larger than the observable universe

This week, I am happy to report that I am pretty much back on track in terms of my work routine, and productivity is once again through the roof. Spelling out and publicizing my exact strategy for not wasting time on the computer definitely motivated me to adhere to it- which is the entire point of writing a blog! But I also have another motivating factor working in my favor this week- a new undergraduate just joined the lab! When I teach, I feel like I learn more than when I'm just trying to learn. Like writing, teaching someone forces me to spell out my logic as clearly as possible, which both clarifies my thinking and uncovers hidden assumptions I had been making that may be wrong. Furthermore, the best way to teach is to ask the student questions that stimulate the student's thought and lets the student figure out the answer. That means I need to put myself in the position of a beginner, so I learn more about my own field and gain insights that I might have missed. It's true that until you can teach a subject, you haven't mastered it. But what is often overlooked is that the very act of teaching something is the METHOD by which one masters something.

Yesterday, I attended the Life Sciences Institute symposium, and this year's focus was on Neuroscience. The speakers were amazing (most were HHMI), and it reminded me why I want to go into neurology. Let's just examine some of the highly attractive intellectual aspects of neurosciences

Brain-specific systems: Molecular and cellular mechanisms that are unique to the brain and, in many cases, unique to the human brain. Non-coding DNA seems to be one of the biggest things that separates us from chimpanzees- why? A lot of it might be transposons that are specifically activated in the brain to jump around and disrupt genes, so that every brain cell has different DNA- thus creating a diversity not seen in any other organ system, other than the immune system. Another thing, which I just learned from Robert Darnell, MD, PhD (Rockefeller) is that the brain has its own splicing system (Nova proteins, etc) allowing the generation of novel isoforms not seen anywhere else in the body. 20,000 genes becomes 100 or 1000 times that number because the brain can generate far more unique proteins than the rest of the body due to novel mechanisms of splicing. Furthermore, different parts of the brain have different splicing systems, and in fact different parts of the same brain cell have different splicing machinery- possibly explaining some aspects of memory assuming these are stable states. And so it also makes sense that certain cancers would co-opt the Nova system to drastically change their gene expression profiles and give them a proliferative advantage, despite the immunological risk it puts the cancer at (spontaneous regression of Nova+ cancers have been observed due to the immune response).

Combinatorial complexity: This point can be best illustrated using one of the simplest examples in neuroscience (even though really it's not simple at all). The problem is recognizing self vs. non-self. Neurons don't want to synapse onto themselves because otherwise they inhibit themselves and become useless, or they hyper-activate themselves and end up killing themselves. But how does a highly branching neuron figure out that the neuron it has reached is another neuron or another part of itself? Larry Zipursky, PhD (UCLA) has discovered how this is accomplished in the Drosophila fruit fly. The Dscam class of molecules have alternative exons at four positions. Combinatorial complexity means that 12 x 48 x 33 x 2 = 38,000 unique Dscam molecules. Furthermore, each neuron expresses a random combination of about 50 different Dscam molecules. How many different profiles thus are possible in the brain? 

38,000^50 = 10^229. That is far greater than the number of particles in the entire universe. In fact, if every particle in the universe had an entire universe inside of it, and every particle in that universe had a entire universe inside of it, 10^229 is still far larger. Now, when a Dscam group on one neuron encounters a Dscam group on either the same or another neuron, it only binds if they match sufficiently. If it binds, they inhibit each other and cause the synapse to fail. So essentially, the likelihood that two different neurons will have profiles similar enough to inhibit each other is essentially non-existent. Thus, every neuron has a unique barcode that allows its dendrites to recognize other dendrites on itself. Even cooler, the Zipursky lab systematically deleted alternative exons until they figured out how many unique Dscam molecules are required to prevent inappropriate self-synapsing and inappropriate avoidance of non-self.

Region-specific features: Let's not forget that the brain is huge. Really huge. The human brain should not be thought of as one organ system. A single brain's complexity is more on the order of the entire rest of Earth's biosphere. So one part of the brain might act under totally differently principles than its neighboring part, even though the majority of proteins are the same. So when you treat the brain with a single simple drug, it may have really awesome effects in one part, but it's going to affect everything else too, possibly adversely. Let's take dopamine as an extremely simple example. Insufficient dopamine is a cause of some Parkinson's symptoms, so dopamine therapy can have massive benefit in terms of quality of life for Parkinson's patients. But dopamine is also inappropriately elevated in an entirely different part of the brain in schizophrenia, so a potential side effect is schizophrenic-like symptoms. Conversely, treating schizophrenic patients with dopamine antagonists can have Parkinsonian side effects. 

Another example: yesterday Luis Parada, PhD (MIT) discussed his work on SSRI anti-depressant therapy. He found that the reason why SSRIs take months to work even though they cause immediate serotonin changes is that SSRIs enhance hippocampal neurogenesis over time. New neurons need to form for SSRIs to work. More interestingly, exercise seems to have the same effect- explaining why I'm always happier after exercising consistently. Furthermore, activating hippocampal neurogenesis is sufficient to reverse depression and anxiety-like symptoms in mice, and blocking neurogenesis can block the positive effects of anti-depressants and exercise. This has major implications for depression therapy, since SSRIs currently affect serotonin all of the brain, resulting in all sorts of changes that may have all sorts of adverse effects. So if we develop a drug that specifically activates hippocampal neurogenesis, we can treat depression without the side effects. Exercise should also be incorporated as a mainstay of depression therapy. Lastly, I'd like to point out that this strategy can be used for cognitive enhancement in healthy people. Meanwhile, I'm going to keep on exercising.

One reason I want to go into neurology is that there are few good therapies for any of the major neurological disorders. But I have little doubt, based on what I've heard at research talks, that major neurological therapies will reach the clinic right around the time that I start residency. Right now there are some crazy flowcharts for figuring out which patients receive which therapies. But the brain is an entirely different animal. Figuring out which patients will benefit from neurological therapies (and cognitive enhancement) will require a fundamental understanding of these intellectually challenging topics such as combinatorial complexity. Thus neurology will provide me with intellectual challenge for my entire life. My prediction that most of the diseases of other organ systems will be cured within 100 years, and medicine will become tediously boring and trivial. I doubt that neurology will be solved for another 500.