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

帕金森氏症（PD）是世界上第二常见的与年龄相关的疾病，也是增长最快的神经系统疾病，目前尚无治愈方法。PD的特征是黑质致密部多巴胺能神经元的逐渐丧失，导致运动受损，如震颤、僵硬和运动缓慢。迫切需要新的治疗方案，以解决潜在的疾病机制，并改进目前的治疗方法管理症状。最近我们对帕金森病病因的理解取得了进展，强调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，2019）。该博士学位的目的是在溶酶体和线粒体生物学的背景下，通过人工智能（AI）的假设生成，识别和验证与PD进展相关的基因。作为有丝分裂后的细胞，溶酶体和线粒体的功能对神经元的生存至关重要，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，帕金森病的特征是蛋白质聚集体的积累，突出了清除途径的缺陷。遗传学研究已经确立了溶酶体和线粒体功能障碍与帕金森病发病之间的重要联系。大量常染色体显性和隐性基因与帕金森病以及一些遗传风险因素相关，这些遗传风险因素编码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，受损的线粒体通过线粒体自噬从细胞中移除，即自噬体捕获受损的线粒体，随后将其传递给溶酶体。在许多形式的帕金森病中，溶酶体对受损线粒体的这一过程和成功降解是有缺陷的。这个项目的主要目的是确定可以被操纵来增强有丝分裂和溶酶体功能的基因，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，该团队将功能基因组学和基于成像的筛查（Ketteler实验室）的优势与人工智能的目标识别和神经退行性疾病的验证（BenevolentAI）相结合。该项目将分两个不同的阶段进行：首先，由BenevolentAI驱动的目标识别（这项工作将在学生开始学习日期之前完成），其次是基于siRNA和CRISPR/ cas9的功能性有丝分裂测定中筛选这些目标（在UCL）。其次，从诱导多能干细胞（与benevolentai和UCL合作开发）中获得的疾病相关神经细胞的机制筛选中确定的hit的验证。我们预计这项工作将在BenevolentAI的研究实验室中进行12个月，从而使学生能够在行业环境中获得重要的工作经验。在这个跨学科项目中，学生将获得定量生物学、功能基因组学、细胞生物学和早期药物发现方面的技能，并将能够深入了解人工智能和机器学习的当前方法。我们相信，BenevolentAI对新靶点的计算预测与Ketteler在线粒体自噬和神经变性系统分析方面的专业知识相结合，将使我们在识别PD发病机制的新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.