代谢组
化学
代谢组学
工作流程
计算生物学
色谱法
数据库
计算机科学
生物
作者
Carter K. Asef,Samuel G. Moore,C. Austin Pickens,Carlos A. Saavedra‐Matiz,Joseph J. Orsini,Konstantinos Pétritis,David A. Gaul,Facundo M. Fernández
标识
DOI:10.1021/acs.analchem.4c06061
摘要
Newborn screening (NBS) is one of the United States' largest, most successful preventative public health initiatives, improving outcomes for newborns with inborn errors of metabolism. Most disorders on the Recommended Uniform Screening Panel are screened using triple-quadrupole mass spectrometry and flow injection analysis. While these methods are sensitive and well suited for high-throughput quantitative applications, the breadth of measured analytes is limited to a relatively small number of biomarkers, which often have considerable overlaps between healthy and diseased populations. High-resolution liquid chromatography–mass spectrometry (LC–MS)-based metabolomics is now capable of profiling thousands of metabolites, making it well suited for exploratory and biomarker discovery studies. To this end, we developed a robust workflow for performing nontargeted LC–MS analysis on dried bloodspot (DBS) specimens with coverage across many metabolic pathways relevant to NBS. HILIC chromatography enabled quantitation of amino acid and acylcarnitine species while also retaining lipid species, such as lyso-phosphatidylcholines. We analyzed 810 newborn-derived DBS samples across a wide range of newborn birthweights, identifying correlations with metabolites that help to better account for the lower accuracy observed for some NBS markers (e.g., isovalerylcarnitine). Additionally, we leveraged this nontargeted workflow to capture new biomarkers and metabolic phenotypes in newborns associated with parenteral nutrition administration and maternal nicotine exposure. Two critical biomarkers were identified as useful additions to targeted screening panels: N-acetyltyrosine as a qualitative marker for parenteral nutrition administration and N-acetylputrescine as a quantitative marker for controlling birthweight variability.
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