The role of DNA methylation in stratifying and understanding paediatric nephrotic syndrome - a combined bioinformatics and CRISPR-cas9 approach

肾病综合征：肾病综合征（NS）是一种以肾脏将大量蛋白质泄漏到尿液中，显著液体潴留和血液中蛋白质水平低为特征的疾病。患有NS的儿童可能会发展为肾衰竭，需要终身治疗;因此，这种情况对他们的生活产生了巨大的影响。我们对NS的了解正在增加，我们现在认识到它不是一种单一的疾病，这解释了为什么NS患者对相同的治疗可以表现出明显不同的反应，以及为什么有些人的NS在肾移植后会恢复，但对于其他人来说不会。研究结果表明，NS由4种条件（亚组）组成，并且该疾病可能由每个亚组中的不同机制驱动。遗传学的最新进展已经确定，对肾功能重要的基因中的异常DNA序列是其中一个亚组中NS的原因。然而，NS的原因在其余3个亚组是未知的，也没有临床上有用的方法来确定哪个亚组的患者属于。DNA甲基化：DNA甲基化（DNAm）是一个身体的分子机制开关基因。一个人的DNA图谱可以在一生中根据其行为和环境而变化。因此，DNAm可能在疾病发展中起重要作用。DNAm图谱在区分不同疾病亚组方面显示出很大的前景，例如，DNAm图谱可用于对患有不同类型脑肿瘤的患者进行准确分类。我研究了105名NS患者的血液DNAm谱，以探讨这种机制是否在NS中很重要。我很着迷地发现，在4个NS亚组中的人有不同的DNAm模式;而且，这些差异指向可能在NS中很重要但以前没有被检查过的特定基因。我将通过研究来自280名NS患者的DNA图谱和遗传数据，并利用尖端的计算机科学和实验室技术，以这些发现为基础。本研究的目的和意义：本研究的目的是利用NS患者的血液DNAm谱，将NS患者分为4个NS亚组。可以识别患者属于哪个亚组的血液测试是个性化NS治疗的第一步;它将使NS儿童避免无效和潜在危险的药物治疗方案，并专注于有益的治疗。它也可以用来预测哪些患者可能在肾移植后发展为NS，哪些不会。通过研究NS患者的分子特征，我还将对每个亚组的不同疾病途径有新的了解，这可能导致新的靶向NS治疗。我的研究有三个阶段：1。使用血液DNAm图谱分离4个NS亚组。我将使用复杂的机器学习技术来确定血液DNAm图谱是否可以一致且准确地将患者分为4个亚组。2.检查是否在DNA的差异可能会导致NS。我将比较血液DNA在4个NS亚组的人之间的档案。通过分析DNAm图谱和遗传数据，我将梳理出哪些DNAm差异更有可能导致NS;这对于优先考虑不同DNAm的哪些特定位点最有可能成为治疗靶点至关重要。在血细胞系中以靶向方式改变DNAm。我将在第2阶段确定的5个最有希望的DNAm位点改变血细胞中的DNAm，并研究这对基因调控和蛋白质产生的影响。为此，我将学习利用基因编辑技术的高度专业化的实验室技术。最终，这项工作是创造靶向DNAm的新NS治疗的第一步。

Nephrotic syndrome: Nephrotic syndrome (NS) is a condition characterised by the kidneys leaking huge amounts of protein into the urine, significant fluid retention and low protein levels in the blood. Children who suffer from NS can develop kidney failure and require lifelong treatment; therefore, this condition has a drastic impact on their lives. Our knowledge of NS is increasing and we now recognise that it is not one single disease, which explains why people with NS can exhibit markedly different responses to the same treatment and why some people's NS will return after they have had a kidney transplant, but for others it will not. Findings from research studies suggest that NS consists of 4 conditions (subgroups) and that the disease may be driven by a different mechanism in each subgroup. Recent advances in genetics have identified that abnormal DNA sequences in genes which are important for kidney function are the cause of NS in one of the subgroups. However, the causes of NS in the 3 remaining subgroups are unknown and there is no clinically useful method to identify which subgroup a patient belongs to.DNA methylation: DNA methylation (DNAm) is one of the body's molecular mechanisms for switching genes on and off. A person's DNAm profile can change throughout their lifetime in response to their behaviour and their environment. Therefore, DNAm may play an important role in disease development. DNAm profiles have shown great promise in distinguishing between different disease subgroups, for example, DNAm profiles can be used to accurately classify patients with different types of brain tumours. I studied blood DNAm profiles from 105 people with NS to explore whether this mechanism may be important in NS. I was fascinated to find that people in the 4 NS subgroups had different DNAm patterns; also, the differences pointed to specific genes which could be important in NS but have not previously been examined. I will build on these findings by studying DNAm profiles and genetic data from 280 NS patients and by using cutting-edge computer science and laboratory techniques. The aim and importance of my research: My work aims to classify NS patients into the 4 NS subgroups by using their blood DNAm profiles. A blood test that can identify which subgroup a patient belongs to is the first step towards personalised NS treatment; it would allow children with NS to avoid futile and potentially dangerous medication regimes and focus on beneficial treatments. It could also be used to predict which patients may develop NS after kidney transplantation and which would not. By studying the molecular profiles of people with NS, I will also gain a new understanding of the separate disease pathways of each subgroup, which could lead to novel targeted NS treatments. There are 3 stages to my research:1. Use blood DNAm profiles to segregate the 4 NS subgroups.I will use sophisticated machine learning techniques to determine whether blood DNAm patterns can consistently and accurately segregate patients into the 4 subgroups. 2. Examine whether differences in DNAm are likely to cause NS.I will compare blood DNAm profiles between people in the 4 NS subgroups. By analysing DNAm profiles alongside genetic data I will tease out which DNAm differences are more likely to be causing NS; this is crucial for prioritising which specific sites of differing DNAm have the most potential to become treatment targets.3. Alter DNAm in a targeted manner in blood cell lines.I will alter DNAm in blood cells at the 5 most promising DNAm sites identified in stage 2 and study the effect that this has on gene regulation and protein production. To do this, I will learn highly specialised laboratory techniques which utilise gene editing technology. Ultimately, this work is the first step towards creating new NS treatments which target DNAm.