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Predicting the clinical outcome of infectious and non-infectious disease patients using whole blood transcriptomics

使用全血转录组学预测传染病和非传染病患者的临床结果

基本信息

批准号：
2366926
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2366926
关键词：
Predicting clinical outcome infectious non

项目摘要

High clinical variability between individuals with the same (infectious or non-infectious) disease is the medical reality of the vast majority of diseases. These clinical manifestations can range from asymptomatic to lethal with a range of clinical complications in between. For example, malaria infected patients are generally divided into those with either uncomplicated or severe infections. The severe and deadlier form of the disease is almost exclusively caused by Plasmodium falciparum and is characterised by the presence of one or more clinical complications. These can include: cerebral malaria, severe anaemia, kidney failure, hypoglycaemia, lactic acidosis, pulmonary edema, respiratory distress syndrome and liver dysfunction. Consequently, there is high variability in the severity of this disease between individuals, but >70% of malarial deaths occur in children under 5. Hence, transcriptomic, primarily differential expression-based, methods have been utilised in the study of this and other diseases to identify transcriptomic prognostic biomarkers. This has resulted in the identification of a few pathways, including toll-like receptor pathways, and genes that are associated with malarial severity but the progression from an uncomplicated to severe infection remains poorly understood. Hence, we cannot predict the clinical complications a patient is likely to develop, as the result of a P. falciparum infection, based on what we can measure at clinical presentation. Thus, new approaches may be required to model the natural history of a patient's disease. One such approach that could be adapted for this purpose is fate mapping. Fate-mapping, which is also called cell lineage tracing when performed at the single cell level, uses a cell's transcriptomic profile to trace its development through a process, commonly differentiation. This has recently been combined with a new method, RNA velocity, to place a directionality onto the transcriptome of each cell within the map (the velocyto algorithm). It is this method I am using in this study.

患有相同(传染性或非传染性)疾病的个体之间的高度临床变异性是绝大多数疾病的医学现实。这些临床表现从无症状到致命性不等，中间有一系列临床并发症。例如，感染疟疾的患者通常被分为无并发症或严重感染的患者。这种严重和致命的疾病几乎完全是由恶性疟原虫引起的，其特点是存在一种或多种临床并发症。这些疾病可能包括：脑型疟疾、严重贫血、肾功能衰竭、低血糖、乳酸酸中毒、肺水肿、呼吸窘迫综合征和肝功能障碍。因此，这种疾病的严重程度在个体之间有很高的差异性，但70%的疟疾死亡发生在5岁以下的儿童中。因此，转录法，主要基于差异表达，已经被用于研究这种疾病和其他疾病来识别转录法的预后生物标记物。这导致确定了一些途径，包括Toll样受体途径，以及与疟疾严重程度相关的基因，但从不复杂的感染到严重感染的进展仍然知之甚少。因此，根据我们在临床表现时所能测量的结果，我们不能预测患者可能由于恶性疟原虫感染而发生的临床并发症。因此，可能需要新的方法来模拟患者疾病的自然历史。一种可以用于这一目的的方法是命运映射。命运图谱，当在单细胞水平上进行时，也被称为细胞谱系追踪，使用细胞的转录图谱来追踪其通过一个过程的发展，通常是分化。这最近与一种新的方法--RNA速度相结合，在MAP内每个细胞的转录组上放置一个方向性(速度细胞算法)。这就是我在这项研究中使用的方法。