长期接触某些金属在疾病发展中起作用。将非靶向代谢组学与尿金属组数据整合可能有助于更好地理解疾病的病理生理学和与环境金属暴露相关的复杂分子相互作用。为了发现尿金属生物标志物和代谢网络之间的新关联,我们使用一组尿中金属和来自强心脏家族研究(SHFS)的非靶向血液代谢组数据进行了综合金属-代谢组分析.
SHFS是一项基于家庭的前瞻性队列研究,由2001-2003年招募的美国印第安人男性和女性组成。这项巢式病例对照分析对145名参与者进行了分析,其中50名参与者在2006-2009年的随访中发生了糖尿病,其中包括具有尿金属和非目标代谢组学数据的参与者。8种肌酐调节的尿液金属/类金属的浓度[锑(Sb),镉(Cd),铅(Pb),钼(Mo),硒(Se),钨(W),铀(U)和锌(Zn)],和4种砷物种[无机砷(iAs),单甲基arsonate(MMA),二甲基氨酸盐(DMA),和砷甜菜碱(AsB)]进行测量。使用高分辨率Orbitrap质谱对血浆样品进行全局代谢组学。我们使用xMWAS进行了综合网络分析,并使用Mummichog进行了代谢途径分析。
8,810种代谢特征和12种金属被包括在综合网络分析中。大多数金属物种与代谢物的不同子集有关,形成单金属多代谢产物簇(|r|>0.28,p值<0.001)。DMA(使用W进行聚类),iAs(用U聚类),与Mo和Se一起通过与常见代谢物的关联显示出适度的相互作用。相关代谢物的途径富集分析(|r|>0.17,p值<0.1)显示了对氨基酸代谢的影响(AsB,Sb,Se和U),脂肪酸和脂质代谢(iAs,Mo,W,Sb,Pb,Cd和Zn)。在继续发展为糖尿病的参与者的分层分析中,iAs和U通过共享代谢物聚集在一起,并且两者都与磷脂酰肌醇磷酸代谢途径相关;金属也与能量代谢中的代谢物相关(iAs,MMA,DMA,U,W)和异源生物降解和代谢(DMA,Pb)途径。
在对多种金属和非靶向代谢组学的综合分析中,结果显示与脂肪酸的共同关联,能量和氨基酸代谢途径。不同金属的个体代谢物关联的结果不同,表明需要更多的人口来确认这里检测到的金属-金属相互作用,如铀和无机砷的强相互作用。了解代谢稳态的生化网络及其与多种金属接触的关系可能有助于识别新的生物标志物。疾病的路径,环境金属暴露的潜在特征。
Chronic exposure to certain metals plays a role in disease development. Integrating untargeted metabolomics with urinary metallome data may contribute to better understanding the pathophysiology of diseases and complex molecular interactions related to environmental metal exposures. To discover novel associations between urinary metal biomarkers and metabolism networks, we conducted an integrative metallome-metabolome analysis using a panel of urinary metals and untargeted blood metabolomic data from the Strong Heart Family
Study (SHFS).
The SHFS is a prospective family-based cohort
study comprised of American Indian men and women recruited in 2001-2003. This nested case-control analysis of 145 participants of which 50 developed incident diabetes at follow up in 2006-2009, included participants with urinary metal and untargeted metabolomic data. Concentrations of 8 creatinine-adjusted urine metals/metalloids [antimony (Sb), cadmium (Cd), lead (Pb), molybdenum (Mo), selenium (Se), tungsten (W), uranium (U) and zinc (Zn)], and 4 arsenic species [inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (AsB)] were measured. Global metabolomics was performed on plasma samples using high-resolution Orbitrap mass spectrometry. We performed an integrative network analysis using xMWAS and a metabolic pathway analysis using Mummichog.
8,810 metabolic features and 12 metal species were included in the integrative network analysis. Most metal species were associated with distinct subsets of metabolites, forming single-metal-multiple-metabolite clusters (|r|>0.28, p-value < 0.001). DMA (clustering with W), iAs (clustering with U), together with Mo and Se showed modest interactions through associations with common metabolites. Pathway enrichment analysis of associated metabolites (|r|>0.17, p-value < 0.1) showed effects in amino acid metabolism (AsB, Sb, Se and U), fatty acid and lipid metabolism (iAs, Mo, W, Sb, Pb, Cd and Zn). In stratified analyses among participants who went on to develop diabetes, iAs and U clustered together through shared metabolites, and both were associated with the phosphatidylinositol phosphate metabolism pathway; metals were also associated with metabolites in energy metabolism (iAs, MMA, DMA, U, W) and xenobiotic degradation and metabolism (DMA, Pb) pathways.
In this integrative analysis of multiple metals and untargeted metabolomics, results show common associations with fatty acid, energy and amino acid metabolism pathways. Results for individual metabolite associations differed for different metals, indicating that larger populations will be needed to confirm the metal-metal interactions detected here, such as the strong interaction of uranium and inorganic arsenic. Understanding the biochemical networks underlying metabolic homeostasis and their association with exposure to multiple metals may help identify novel biomarkers, pathways of disease, potential signatures of environmental metal exposure.