当许多高中生正在决定自己的人生道路时，理查德·胡加尼尔(Richard Huganir)博士.D., director of the department of neuroscience, was conducting his first neuroscience experiment, one that would mark the beginning of a decades-long, prolific career.
Years later, Huganir earned his doctorate at Cornell, working in the lab of biochemist Efraim Racker, 他在哪里开始研究被神经递质激活的脑细胞上的受体. As a postdoctoral fellow at Yale, Huganir worked with Nobel laureate Paul Greengard, 继续研究神经递质受体的调节. 他在洛克菲勒大学的第一份教职工作中继续了这一研究.
Then, in 1988, none other than legendary neuroscientist Sol Snyder, 约翰·霍普金斯医学院神经科学系就是以他的名字命名的, 诺贝尔奖得主丹·内森斯邀请胡加尼尔加入约翰·霍普金斯大学医学院. That was more than 30 years ago.
Fast forward to last month, 当时神经科学学会以最高荣誉表彰了Huganir对神经科学领域的贡献, the Ralph W. Gerard Prize in Neuroscience.
我们请Huganir讲述了他职业生涯中的里程碑，并提供了他对神经科学研究未来的看法.
You have been a neuroscientist for more than 40 years. What drew you to this field?
In high school, I was really interested in science. At the same time, I started thinking about what makes me who I am, and a major part of that is my memories. 我意识到记忆一定是由大脑中的生化变化编码的.
So, for my high school senior research project in biology, 我重现了著名神经学家伯纳德·阿格兰诺夫的实验, in which I trained goldfish to learn a task, 然后试图通过使用阻断蛋白质合成的药物来阻止学习. Essentially, by blocking protein synthesis, the fish could not perform the task, suggesting that I was able to block memory formation. 我就是这样开始尝试理解记忆是如何在大脑中编码的, and it’s what I’ve been doing my entire career.
What area of your research has been career-defining?
In my lab, 我们研究学习是如何在大脑中编码并维持多年的, or even decades, and for this, we study all kinds of memory.
Learning is a process called synaptic plasticity, 在这个过程中，你的大脑改变了脑细胞之间的连接, or neurons. The connectivity between neurons is essentially circuitry, 数十亿的神经元和千万亿的突触形成了新的连接, 神经元间的空间，神经递质在附着于受体之前在此来回传递. When you learn something, 你基本上是通过加强一些突触和削弱其他突触来塑造一个新的回路.
As a graduate student, 我对连接神经递质的受体有预感, and it turned out to be true. 它们调节了很多学习，但在疾病中，这种机制就失效了.
For example, in 1998, we identified a protein called SYNGAP, and showed it is involved in normal learning. However, SYNGAP1基因突变的老鼠在学习方面有问题, seizures and hyperactivity.
In 2009, 11 years after our initial discovery, 在智力残疾儿童中发现了SYNGAP1突变, autistic repetitive features and hyperactivity. It’s one of the most common forms of intellectual disability.
我现在致力于开发针对SYNGAP1突变儿童的治疗方法, with the Kennedy Krieger Institute, starting a clinic focusing on this condition.
This is a basic scientist’s dream, to discover the gene, reveal what it does, and then apply that knowledge to develop therapeutics.
What mentors have been most influential to your career?
Paul Greengard was probably the most important mentor to me. When I first arrived as a postdoc in his lab at Yale, he said, “We’re moving to Rockefeller University.” At the time, I really didn’t want to move to New York. But he believed in me and he convinced me to move with him.
When we got to Rockefeller, he promoted me to assistant professor and, amazingly, gave me the penthouse apartment next door to his, but most importantly, he gave me the independence to create my own track.
At Johns Hopkins, 索尔·斯奈德把我收在他的羽翼下，把我培养成神经科学系主任. I helped with major decisions, 领导了十多年的研究生项目，并帮助建立了这个部门, so when he stepped down, he was clear that he prepared me to be chair and, luckily, the director search committee agreed.
另一位重要的导师是丹·内森斯，他在职业发展方面给了我重要的建议.
In my own mentoring, 我努力培养员工，通过给予他们灵活性和鼓励他们发挥创造力，让每个人都发挥出最大的潜能. 这就是索尔和丹为我所做的，这就是为什么我实验室的项目看起来非常多样化.
When you started your career, where did you see the future of neuroscience, and where do you see it now?
当我开始我的职业生涯时，我想当时没有人能想象到我们今天的处境. 当神经科学还不是一个真正的领域时，我被训练成一个生物化学家. Back then it was called bio-psychology. Then, when I started graduate school, 当时全国只有一个神经科学系:哈佛大学的神经生物系. A few years later, Johns Hopkins opened the second department.
然后，在过去的15或20年里，出现了一场新的、关键的技术革命. For example, we can now do human genome sequencing in a day, 我们现在使用的成像技术非常灵敏, 高分辨率的工具可以从大脑内部产生令人难以置信的视觉效果, while an animal is behaving.
We can control the activity of neurons in the brain, 用光刺激特定的神经回路来诱导行为. 这种成像和操纵大脑活动的能力，在30年前是我们无法想象的.
我们现在可以同时想象一百万个突触，并在学习前后观察它们. 问题是，我们可以看到变化，但我们不知道变化发生在哪里. 神经科学部门的研究人员正在与生物医学工程部门的研究人员合作，以识别空间和时间上的突触, using artificial intelligence, machine learning and computational approaches. Ten years ago, that was unfathomable.
