Integration of metabolomics, lipidomics and clinical data using a machine learning method.
Integration of metabolomics, lipidomics and clinical data using a machine learning method.
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DOI：
10.1186/s12859-016-1292-2
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发表时间：
2016-11-22
影响因子：
3
通讯作者：
Griffin JL
中科院分区：
文献类型：
--
作者：
Acharjee A;Ament Z;West JA;Stanley E;Griffin JL
最近肥胖和代谢综合征(METS)的流行导致人们意识到，需要新的药物靶点来减少肥胖或随后与超重增加相关的病理生理后果。某些核激素受体(NRs)在脂肪和碳水化合物代谢中起着关键作用，已被强调为潜在的肥胖治疗方法。这一认识开启了对NR激动剂的研究，以了解并成功治疗METS及其相关疾病，如胰岛素抵抗、血脂异常、高血压、高甘油三酯血症、肥胖和心血管疾病。研究最多的用于治疗代谢性疾病的NRs是过氧化物酶体增殖物激活受体(PPAR)、PPAR-α、PPAR-γ和PPAR-δ。然而，在动物模型中长期治疗PPAR会导致不良副作用，包括增加患癌症的风险，但这些受体如何从病理学角度长期改变新陈代谢，尽管短期内有许多有益的影响，尚不完全清楚。在目前的研究中，用经典毒理学(临床化学)和高通量代谢组学和脂类组学方法用质谱学方法分析了用PPAR-PAN(PPAR-α，−γ和-δ)激动剂饮食治疗雄性SD大鼠肝脏的变化。为了将9个不同的多变量代谢和脂质组学数据集与经典毒理学参数集成在一起，我们开发了一种无假设、数据驱动的机器学习方法。从数据分析中，我们检查了九个数据集如何能够对剂量和临床化学结果进行建模，不同的数据集具有非常不同的信息内容。我们发现脂质组学(直接输注-质谱学)数据对不同剂量的反应最具预测性。此外，还建立了代谢和脂组数据与天冬氨酸氨基转移酶(AST)和白蛋白(指示肝脏合成功能改变)的关系。AST是一种肝脏渗漏酶，用于评估器官损伤。此外，通过建立二十烷类、磷脂和三酰甘油之间的相关性和网络联系，我们提供了证据，表明这些脂类在炎症过程和中间代谢之间起关键作用。本文的在线版本(doi：10.1186/s12859-016-1292-2)包含补充材料，授权用户可以使用。
The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, −γ, and –δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism. The online version of this article (doi:10.1186/s12859-016-1292-2) contains supplementary material, which is available to authorized users.
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DOI：
10.3390/molecules20022425
发表时间：
2015-01-30
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Jenkins B;West JA;Koulman A
通讯作者：
Koulman A
影响因子：
3
作者：
Díaz-Uriarte R;Alvarez de Andrés S
通讯作者：
Alvarez de Andrés S
影响因子：
10.8
作者：
Garcia, Giani Martins;Oliveira, Liliam Teixeira;Furtado Mosqueira, Vanessa Carla
通讯作者：
Furtado Mosqueira, Vanessa Carla
影响因子：
7.7
作者：
Huang W;Metlakunta A;Dedousis N;Zhang P;Sipula I;Dube JJ;Scott DK;O'Doherty RM
通讯作者：
O'Doherty RM
影响因子：
7.4
作者：
Ament Z;West JA;Stanley E;Ashmore T;Roberts LD;Wright J;Nicholls AW;Griffin JL
通讯作者：
Griffin JL