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

神经精神障碍，如自闭症谱系障碍（ASD）和精神分裂症是广泛存在的，在美国约有1.7%的儿童被诊断患有ASD，约有0.7%的人在生活中的某个时候被诊断患有精神分裂症。心理健康问题一般占英国残疾人数的20%以上，估计每年经济损失700 - 1000亿英镑。目前神经精神障碍的行为和药物治疗仅对一部分患者有效，通常会产生不必要的副作用，并且很难从患者到患者预测治疗成功率。这些缺陷反映了一个事实，即几乎所有现有的药物治疗都是偶然发现的，而不是基于对疾病机制的理解而设计的。然而，最近一波发现与ASD和精神分裂症相关的70-100个基因突变的浪潮为这些疾病的起源提供了有希望的线索。许多相关的基因编码对突触很重要--突触是神经元之间的连接，在大脑中介导学习和记忆。这意味着许多神经精神疾病实际上可能是突触可塑性障碍。世界各地的学术和企业实验室研究人员现在正试图弄清楚发现的基因突变会导致大脑发生什么变化，通常是通过研究基因改变的小鼠，理想情况下应该模仿人类患者。然而，大多数神经科学研究方法都非常困难且通量低，因此进展缓慢。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，并且让我们执行研究人员可能喜欢在实验室中做但不能做的详细虚拟实验。计算机模拟将基于我们在布里斯托大学的实验合作者的实验室数据。该项目将筛选出一些突触中高度脆弱的成分，这些成分可用于指导未来的湿实验室实验。最后，我将利用这些结果来发展一个新的神经精神疾病突触信息传递功能障碍的理论，这可以指导该领域更广泛的研究。

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.