Suppr超能文献

代谢组学分析在西非冈比亚严重儿童肺炎中的应用:一项初步研究的结果。

Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study.

机构信息

Department of Biochemistry and Molecular and Cellular Biology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, United States of America.

出版信息

PLoS One. 2010 Sep 9;5(9):e12655. doi: 10.1371/journal.pone.0012655.

Abstract

BACKGROUND

Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.

METHODOLOGY/PRINCIPAL FINDINGS: Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). 'Unsupervised' (blinded) data were analyzed by Principal Component Analysis (PCA), while 'supervised' (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5'-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.

CONCLUSIONS/SIGNIFICANCE: Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.

摘要

背景

肺炎仍然是全球导致儿童死亡的主要原因,需要改进诊断方法以更好地识别病例并降低病死率。代谢组学是一个快速发展的领域,旨在描述生物体液中的代谢物,有可能改善一系列疾病的诊断。本研究旨在应用代谢组学分析方法研究儿童肺炎,探索其在高负担环境下改善肺炎诊断的潜力。

方法/主要发现:在西非冈比亚,我们选择了 11 名患有世界卫生组织(WHO)定义的非同质严重肺炎的儿童病例和社区对照者。使用超高效液相色谱(UPLC)与飞行时间质谱(TOFMS)联用对匹配的血浆和尿液样本进行代谢组学分析。采用 SIMCA-P+和随机森林(RF)进行生物标志物提取。“无监督”(盲法)数据分析采用主成分分析(PCA),“有监督”(非盲法)分析采用偏最小二乘判别分析(PLS-DA)和正交投影到潜在结构(OPLS)。从 OPLS 分析后构建的 S-图中提取潜在标志物,并根据其对数据集内变异和相关性的贡献选择标志物。此外,还使用机器学习算法 RF 对数据集进行了分析,以解决模型过度拟合的问题,并根据其变量重要性排名选择标志物。无监督 PCA 分析显示肺炎组和对照组之间有很好的分离,而 PLS-DA 和 OPLS 分析则显示出更清晰的分离。与对照组相比,病例组血浆中的尿酸、次黄嘌呤和谷氨酸水平较高,而 L-色氨酸和二磷酸腺苷(ADP)水平较低;病例组尿液中的尿酸和 L-组氨酸水平较低。本研究的主要局限性是样本量小。

结论

代谢组学分析能清楚地区分严重肺炎患者和社区对照者。鉴定出的代谢物通过抗氧化、炎症和抗菌途径以及能量代谢对宿主的感染反应很重要。需要进行更大规模的研究以确定这些发现是否是肺炎特异性的,并区分特定病原体的反应。代谢组学在改善儿童肺炎的诊断方面具有很大的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47bd/2936566/3bd5a34df596/pone.0012655.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验