Validation of novel Parkinson's Disease target genes by Artificial Intelligence-based predictions

帕金森氏病(PD)是世界上第二常见的年龄相关疾病，也是发展最快的神经系统疾病，目前尚无治愈方法。帕金森病的特征是黑质致密部多巴胺神经元的进行性丢失，导致运动受损，如震颤、僵硬和运动缓慢。迫切需要新的治疗选择，以解决潜在的疾病机制，并改进目前管理症状的治疗方法。最近，我们对帕金森病病因的理解取得了进展，突显了线粒体和溶酶体功能障碍在疾病发展中的核心作用(Wallings等人，2019年)。这个博士学位的目的是识别和验证参与PD进展的基因，这些基因来自人工智能(AI)在溶酶体和线粒体生物学背景下的假说生成。作为有丝分裂后合适的细胞，溶酶体和线粒体的功能对神经元的存活至关重要，因为它们参与了废物的回收和能量的产生。与其他神经退行性疾病一样，帕金森病的特点是蛋白聚集物的积累突出了清除途径的缺陷。遗传学研究已经建立了溶酶体和线粒体功能障碍与帕金森病发病机制之间的重要联系。大量的常染色体显性和隐性基因与帕金森病以及几个遗传风险因素有关，这些基因编码了参与线粒体质量控制和溶酶体活性的关键蛋白，包括PINK1或GBA1。在健康细胞中，受损的线粒体通过有丝分裂从细胞中移除，即自噬小体捕获受损的线粒体，然后将其输送到溶酶体。这一过程和溶酶体对受损线粒体的成功降解在许多形式的帕金森病中是有缺陷的。这个项目的主要目的是识别可以改变的基因，以增强有丝分裂和溶酶体功能，从而提高多巴胺能神经元的存活率。成功的学生将得到一个多学科团队的支持，该团队将结合功能基因组学和基于成像的筛查(Ketteler Lab)的优势，以及人工智能的目标识别和神经退行性疾病(慈善AI)的验证。该项目将分两个不同的阶段进行：第一，由慈善人工智能驱动的靶标识别(这项工作将在学生开始日期之前完成)，随后在功能性有丝分裂噬菌体分析中(在伦敦大学学院)基于siRNA和CRISPR/Cas9对这些靶标进行筛选。其次，对从诱导多能干细胞(与慈善AI和UCL共同开发)中获得的与疾病相关的神经细胞进行机械筛选，以确认确认的命中结果。我们预计12个月的这项工作将在仁慈人工智能的研究实验室进行，从而使学生能够在行业环境中获得丰富的工作经验。在这个跨学科的项目中，学生将获得数量生物学、功能基因组学、细胞生物学和早期药物发现方面的技能，并将能够洞察人工智能和机器学习的当前方法。我们相信，慈善人工智能对新靶点的计算预测与Ketteler在有丝分裂和神经变性的系统分析方面的专业知识相结合，使我们在识别PD发病机制的新方面方面处于独特的地位。

Parkinson's disease (PD) is the second most common age related disorder and the fastest growingneurological condition in the world, with no cure. PD is characterised by a progressive loss of dopaminergicneurons in the substantia nigra pars compacta, leading to impaired movement such as tremors, stiffnessand slowness of movements. There is an urgent need for new treatment options that address the underlyingdisease mechanisms and improve upon the current therapeutic approach of managing symptoms. Recentadvances in our understanding of the causes of PD have highlighted a central role for mitochondrial andlysosomal dysfunction in the development of the disease (Wallings et al, 2019). The aim of this PhDstudentship is to identify and validate genes involved in PD progression derived from hypothesis generationby Artificial Intelligence (AI) in the context of lysosomal and mitochondrial biology. As post-mitotic cellsappropriate, lysosomal and mitochondrial function is vital to neuron viability because of their involvement inrecycling of waste material and energy production. PD, like other neurodegenerative diseases, ischaracterised by an accumulation of protein aggregates highlighting deficiencies in the clearance pathways.Genetic studies have established an important link between lysosomal and mitochondrial dysfunction andthe pathogenesis of PD. A large number of autosomal dominant and recessive genes are associated withPD as well as several genetic risk factors which encode for key proteins involved in mitochondrial qualitycontrol and lysosomal activity, including PINK1 or GBA1. In healthy cells, damaged mitochondria areremoved from cells by mitophagy, i.e., the capture of damaged mitochondria by autophagosomes withsubsequent delivery to the lysosome. This process and successful degradation of damaged mitochondria bythe lysosome is defective in many forms of PD. The main aim of this project is to identify genes that can bemanipulated to enhance mitophagy and lysosomal function and thus improve dopaminergic neuron survival.The successful student will be supported by a multidisciplinary team, combining the strengths in functionalgenomics and imaging-based screening (Ketteler lab) with target identification from AI and validation inneurodegenerative diseases (BenevolentAI). The project will proceed in two distinct phases:First, AI-driven identification of targets by BenevolentAI (this work will be completed before the studentshipstart date), followed by siRNA- and CRISPR/Cas9-based screening of these targets in functional mitophagyassays (at UCL). Secondly, the validation of identified hits from the mechanistic screening indisease-relevant neuronal cells derived from induced pluripotent stem cells (developed with BenevolentAIand UCL). We anticipate that 12 months of this work will take place in research labs at BenevolentAI, thusenabling the student to gain significant experience in working in an industry environment.During this interdisciplinary project the student will acquire skills in quantitative biology, functional genomics,cell biology and early stage drug discovery and will be able to get insight into current approaches in AI andmachine learning. We believe that the computational prediction of novel targets by BenevolentAI combinedwith Ketteler's expertise in systematic analysis of mitophagy and neurodegeneration places us in a uniqueposition to identify novel aspects of PD pathogenesis.