Understanding the effect of anti-psychotic medication on cellular metabolism.

抗精神病药物是治疗精神病的主要药物。这些药物的主要副作用之一是代谢障碍，这增加了心脏代谢性疾病的风险。与普通人群相比，精神病患者死于心脏代谢性疾病的可能性是普通人的两倍。抗精神病药物引起代谢紊乱的倾向不同：氯氮平和奥氮平(高风险)，利培酮和奎硫平(中风险)，以及氟哌啶醇和卢拉西酮(低风险)。患者对不同抗精神病药物的代谢副作用的易感性也不同。只有在患者接受抗精神病药物治疗后，通常是几周或几个月，这些副作用才会出现问题，并制定出改变生活方式和二甲双胍等药物来缓解这些副作用的策略。除了增加心脏代谢性疾病的风险外，这些副作用还会对治疗关系产生不利影响，导致治疗一致性差，并增加复发的风险。迫切需要更好地了解这些药物对细胞的反应，这将导致制定策略，告知治疗方案，将心脏代谢风险降至最低，并减少相关的发病率和死亡率。线粒体功能障碍是解释抗精神病药物引起的代谢紊乱的关键过程。线粒体代谢可以使用高分辨率呼吸测量(HRR)技术准确地描绘出来。学生将使用HRR来准确定义不同的抗精神病药物在细胞系、原代细胞以及人类治疗和未治疗(对照)血细胞中引发的代谢特征，以对发生代谢综合征的风险进行分层，并确定哪些治疗方案，如二甲双胍，可以缓解或逆转这些变化。使用详细的代谢图谱，他们将能够准确地指定哪些分子和途径指示代谢综合征的开始和改善。学生有可能致力于开发一套生物标记物，以表征目前临床上使用的每种药物的效果。呼吸测量信息将通过对处理细胞和未处理细胞的蛋白质组学分析来丰富，以确定与代谢扰动相关的分子途径。表观遗传学分析将确定在控制副作用方面可能重要的调控机制。

Antipsychotic medications are the mainstay of treatment of psychosis. One of the major side-effects of these medications is metabolic impairment, which increases the risk of cardiometabolic diseases. Patients with psychosis are twice as likely, compared to general populations, to die of cardiometabolic diseases. Antipsychotics differ in their propensity to cause metabolic derangements: clozapine and olanzapine (high risk), risperidone and quetiapine (moderate risk), and haloperidol and lurasidone (low risk). Patients also differ in their susceptibility to develop metabolic side-effects with different antipsychotics. It is only after patients are on antipsychotic treatment, often for weeks and months, before these side-effects become problematic and strategies, like lifestyle changes and medications like metformin, to mitigate these are instituted. Besides increasing the risk of cardiometabolic diseases, these side-effects also affect the therapeutic relationship adversely, contribute to poor treatment concordance and increase the risk of relapse. There is a critical need for a a better understanding of the cellular response to these medications which will lead to strategies to inform treatment options, which minimise cardiometabolic risks and reduce associated morbidity and mortality.Mitochondrial dysfunction is a crucial process that can explain antipsychotic induced metabolic derangements. Mitochondrial metabolism can be accurately profiled using high-resolution respirometry (HRR) techniques. The student will use HRR to accurately define the metabolic signatures that different antipsychotics elicit in cell lines, primary cells and human treated and untreated (control) blood cells, to stratify the risk of developing metabolic syndrome and identify which among the treatment options, like metformin, mitigate or reverse those changes. Using detailed metabolic profiling, they will be able to specify precisely which molecules and pathways are indicative of onset and amelioration of metabolic syndromes. There is potential for the student to work towards development of a set of biomarkers that characterise the effects of each of the medications currently used in the clinic. Respirometry information will be enriched with proteomics analysis of treated and untreated cells to define the molecular pathways associated with metabolic perturbations. Epigenetic analysis will define regulatory mechanisms that may be important in controlling the side effects.