Defining lipidomic biomarkers of the interactions between Western diet and liver health using mass spectrometry imaging

生活方式因素影响健康老龄化。饮食选择改变了对许多疾病的易感性。低脂肪的地中海饮食是抗氧化的，被认为对健康有益，而高脂肪的“西方饮食”则与负面的健康结果有关。体重指数在全球范围内呈上升趋势，这与糖尿病和非酒精性脂肪性肝病（NAFLD）等许多疾病的风险增加有关，但个体风险是可变的。当受到高脂肪饮食的挑战时，身体的反应是调整脂肪储存量，并将脂肪“异位”储存在肝脏中。这种健康的适应性反应最初是无害的。然而，在某些个体中，超过一定水平，代谢灵活性就会被超越，过多的异位脂肪会引发进行性肝纤维化、肝硬化和肝细胞癌。需要生物标志物来区分健康与不健康的饮食适应，以改善高危人群的健康状况。代谢组学提供了个人生化状态的指纹；脂质组学是一个子领域，对调节代谢和炎症的内源性脂质进行分类。脂质组学最常在血液中进行，易于取样，可用于健康筛查。然而，循环脂质组可能不能反映组织。当肝脏健康状况下降时，健康组织和危险组织共存，发生区域性变化。因此，肝脏脂质组学特征必须在空间上描述。这个学生将发展质谱成像（MSI）来空间描绘肝脏中的脂质，并定义它们对饮食的反应。特别是，将详细研究溶血磷脂酰胆碱过度转化为其酸性代谢物的酶autotaxin。假设：不同脂肪含量的饮食对肝脏脂质组的影响不同。MSI揭示的脂质组学特征将确定评估肝脏健康和疾病的途径，并确定可通过生活方式或药物改变治疗的标志物。首先，学生将建立MSI，从小鼠和人的肝脏切片中取样脂质。LC-MS/MS也将用于测量血浆中的关键脂质。通过与脂质组学数据库（如Lipidmaps）的比对，将描述脂质概况并鉴定种类。接下来，我们将研究具有代表性的健康小鼠模型的肝脏脂质组，并通过改变脂肪/碳水化合物含量来研究致肥性饮食的影响。将开发用于共同定位组织学和脂质组学特征的图像分析方法，旨在开发预测模型以识别有风险的区域。脂质组随饮食持续时间的变化将被比较，并与疾病分期（健康与脂肪肝/炎症/纤维化肝病）相关联，以对生物标志物进行分层。小鼠模型的研究结果将转化为来自NHS洛锡安生物资源的人类肝脏样本，并证明在小鼠模型中使用自噬素抑制剂建立的可逆性原理。研究培训学生将接受广泛方法的跨学科培训，包括：a)使用动物模型和人类肝脏组织的体内和体外研究；b)生物分析质谱脂质组学；C)脂质组学数据集的多元统计分析，包括与在线数据库的接口；(d)组织特征的共配准和图像分析。研究将在女王医学研究所内资金充足的实验室进行，这些实验室为拟议的实验提供了充分的设备。可汗，安德鲁（2019）脂质MSI，当前和新兴技术在脂质组学，英国皇家化学学会，伦敦，在出版社。Iredale, Pellicoro, Fallowfield（2017）肝纤维化：了解双向伤口修复的动态，为标记物和治疗方法的设计提供信息。植物学报，2017;35:310-313。coice et al . (2017) 11β - hsd1抑制剂在br中的动力学和药效学效应的定量研究。

Lifestyle factors influence healthy ageing. Dietary choices modify susceptibility to many diseases. The low-fat Mediterranean diet is anti-oxidant and believed beneficial for health, whereas high-fat "Western diet" associates with negative health outcomes. Body mass index is increasing worldwide, linked to increased risk of many diseases, including diabetes and non-alcoholic fatty liver disease (NAFLD), but individual risk is variable. When challenged with a fatty diet, the body responds by adjusting adipose depot volume and by storing fat "ectopically" e.g. in liver. This healthy, adaptive response is initially not harmful. However, beyond a certain level and in some individuals, metabolic flexibility is exceeded and too much ectopic fat triggers progressive liver fibrosis, cirrhosis and hepatocellular carcinoma. Biomarkers are needed to differentiate healthy vs unhealthy dietary adaptation to improve the healthspan of at-risk individuals. Metabolomics provides a fingerprint of personal biochemical status; lipidomics is a sub-field, categorising endogenous lipids regulating metabolism and inflammation. Lipidomics is most commonly conducted in blood, easy to sample and useful for health screening. However, the circulating lipidome may not reflect tissues. When liver health declines, zonal changes occur with healthy and at-risk tissue co-existing. Therefore, the hepatic lipidomic signature must be described spatially. This studentship will develop mass spectrometry imaging (MSI) to spatially profile lipids in liver and define their response to diet. In particular, excessive conversion of lysophosphatidyl cholines to their acidic metabolites by the enzyme autotaxin will be studied in detail.HypothesisDiets varying in fat content differentially affect the hepatic lipidome. The lipidomic signature revealed by MSI will identify pathways to assess liver health and disease and identify markers amenable to therapeutic modification either by lifestyle or drugs.Project PlanInitially, the student will establish MSI to sample lipids from liver sections of mouse and human. LC-MS/MS will also be deployed to measure key lipids in plasma. The profile of lipids will be described and species identified through alignment with lipidomic databases e.g. Lipidmaps.Next, we will study the hepatic lipidome of mouse models representative of healthy animals and study the effect of an obesogenic diet by varying the fat/carbohydrate content. Image analysis methods for co-localising histological and lipidomic features will be developed, aiming to develop predictive models to identify regions at risk. The changes in the lipidome with the duration of diet will be compared and correlated with stages of disease (healthy versus fatty/inflamed/fibrotic liver disease) to stratify biomarkers. Findings in mouse models will be translated to human liver samples from the NHS Lothian BioResource and proof of principle of reversibility established using inhibitors of autotaxin in murine modelsResearch TrainingThe student will receive interdisciplinary training in a broad range of methods including: a) in vivo and in vitro studies using liver tissue from animal models and humans; b) lipidomics by bioanalytical MS; c) multivariate statistical analysis of lipidomic datasets, involving interfacing with online databases; (d) co-registration and image analysis of histological features. Studies will be conducted in well-funded laboratories within the Queen's Medical Research Institute which are fully equipped for the proposed experiments.Khan, Andrew (2019) MSI of lipids, Current and emerging technologies in lipidomics, Royal Society of Chemistry, London, In Press.Iredale, Pellicoro, Fallowfield (2017) Liver Fibrosis: Understanding the dynamics of bidirectional wound repair to inform the design of markers and therapies. Dig Dis 2017;35:310-313.Cobice et al (2017) Quantification of 11beta-HSD1 kinetics and pharmacodynamic effects of inhibitors in br...