Computational prediction of vulnerable points and interventions for dysfunctional synaptic plasticity in neuropsychiatric disorders

自闭症谱系障碍(ASD)和精神分裂症等神经精神障碍很普遍，美国约1.7%的儿童被诊断为自闭症谱系障碍，约0.7%的人在一生中的某个时候被诊断为精神分裂症。心理健康问题一般占英国残疾的20%以上，估计每年要花费700-1000亿英镑的经济。目前对神经精神障碍的行为和药物治疗只对一小部分患者有效，往往会带来不想要的副作用，而且很难预测患者之间的治疗成功。这些缺陷反映了这样一个事实，即几乎所有现有的药物治疗都是偶然发现的，而不是基于对障碍机制的理解而设计的。然而，最近一波与自闭症和精神分裂症各自相关的70-100个基因突变的发现为这些疾病的起源提供了有希望的线索。许多涉及的基因编码对突触很重要，突触是大脑中调节学习和记忆的神经元之间的连接。这意味着许多神经精神障碍实际上可能是突触可塑性障碍。世界各地的学术和公司实验室研究人员现在正试图找出已发现的基因突变导致的大脑变化，通常是通过研究理想地模仿人类患者的基因改变的小鼠。然而，大多数神经科学的研究方法都非常困难，而且吞吐量低，所以进展缓慢。在这个NIRG中，我们将使用数据驱动的神经元和突触的计算模拟，因为它们的工作速度要快得多，并让我们可以执行详细的虚拟实验，研究人员可能想在实验室做这些实验，但做不到。计算机模拟将基于我们在布里斯托尔大学的实验合作者的实验室数据。该项目将入围突触的一些高度脆弱的组件，这些组件可以用于指导未来的湿实验室实验。最后，我将利用这些结果来开发一种新的理论，即神经精神障碍中突触的信息传递功能障碍，可以指导该领域的更广泛研究。

Neuropsychiatric disorders such as Autism Spectrum Disorder (ASD) and Schizophrenia are widespread, with around 1.7% of children in the United States diagnosed with ASD, and around 0.7% of people being diagnosed with Schizophrenia at some point in life. Mental health problems in general account for more than 20% of disabilities in the UK and are estimated to cost the economy between £70-100 billion per year. Current behavioural and pharmaceutical treatments for neuropsychiatric disorders are effective for only a subset of patients, often carry unwanted side-effects, and treatment success is difficult to predict from patient to patient. These shortcomings reflect the fact that almost all existing drug treatments were discovered by chance, rather than being designed based on an understanding of disorder mechanisms.However, a recent wave of discoveries of 70-100 genetic mutations linked to each of ASD and Schizophrenia has given promising clues to the origins of these disorders. Many of the genes code implicated are important for synapses - the connections between neurons that mediate learning and memory in the brain. This implies that many neuropsychiatric disorders may in fact be disorders of synaptic plasticity.Academic and corporate laboratory researchers worldwide are now trying to figure out what brain changes the discovered genetic mutations cause, typically by studying genetically altered mice that should ideally mimic the human patients. However, most neuroscience research methods are painstakingly difficult and low-throughput, so progress is slow. In this NIRG we will instead use data-driven computational simulations of neurons and synapses, because they work much faster, and let us perform detailed virtual experiments that researchers might like to do in the lab, but can't. The computer simulations will be based on data from the lab of our experimental collaborators within the University of Bristol. This project will shortlist a number of highly vulnerable components of synapses, that can be used to direct future wet lab experiments. Finally, I will use the results to develop a new theory of dysfunctional information transmission at synapses in neuropsychiatric disorders, that could guide broader research in the field.