Gestational age-dependent development of the neonatal metabolome
Gestational age-dependent development of the neonatal metabolome
复制标题
DOI：
10.1038/s41390-020-01149-z
复制
发表时间：
2020-09-17
影响因子：
3.6
通讯作者：
Cohen, Arieh S.
中科院分区：
文献类型：
--
作者：
Ernst, Madeleine;Rogers, Simon;Cohen, Arieh S.
背景技术背景：早产是一种严重的病理生理条件，然而，鲜为人知的是胎龄依赖性发展的新生儿metabolome.METHODS：使用非靶向液相色谱-串联质谱代谢组学协议，我们测量了超过9000代谢物在298个新生儿残余足跟刺干血点检索丹麦新生儿筛查生物库。通过结合多种最先进的代谢组挖掘工具，我们检索了超过5000种（60%）代谢物的广泛水平的化学结构信息，并评估了它们与胎龄的关系。共1459个（相似于16%）代谢产物与胎龄显著相关（错误发现率调整的P < 0.05），而83种代谢物平均解释了胎龄方差的48%。使用基于超几何检验的自定义算法，我们确定了化合物类别（617种代谢物），其中代谢物与胎龄相关（P < 0.05）。与胎龄显著相关的代谢物包括胆汁酸、肉毒碱、多胺、氨基酸衍生化合物、核苷酸、磷脂酰胆碱和二肽，以及治疗相关的代谢物，如抗生素和咖啡因。我们的发现阐明了胎龄-新生儿血液代谢组的依赖性发展，并表明代谢组学工具的应用具有很大的潜力，以揭示新的生化基础提高我们对早产相关疾病复杂病理生理机制的理解。
BACKGROUND: Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome.METHODS: Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age.RESULTS: A total of 1459 (similar to 16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine.CONCLUSIONS: Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders.