Nahan Keaton S, Walsh Kyle B, Adeoye Opeolu, Landero-Figueroa Julio A
University of Cincinnati/Agilent Technologies Metallomics Center of the Americas, Department of Chemistry, University of Cincinnati, Mail Location 0172, Cincinnati, OH 45221, USA.
University of Cincinnati Neuroscience Institute, Department of Emergency Medicine, University of Cincinnati, College of Medicine, 231 Albert Sabin Way, Cincinnati, OH 45267-0769, USA.
J Trace Elem Med Biol. 2017 Jul;42:81-91. doi: 10.1016/j.jtemb.2017.04.004. Epub 2017 Apr 7.
Stroke, a major cause of disability and mortality, affects someone in the United States every 40s. Stroke biomarkers, including those that could be used as a blood test for diagnosis of stroke, have been particularly elusive. We performed a double blind study to identify human plasma biomarkers for the diagnosis of stroke, including acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH). We utilized a three-track approach based on the total metal profile, the metal cofactor levels among metalloproteins, and the identification of stroke-related metalloproteins. The study included 14 case-control pairs of AIS and 23 case-control pairs of ICH. Controls were matched to cases based on gender, ethnicity, and age (±5 years). AIS cases were statistically higher from their respective controls for protein bound co-factors Se and Cd, while unique correlations of metal cofactor concentrations among metalloproteins were identified between Pb-W, Sr-W, Pb-V, and Cu-V. ICH cases were statistically higher from their respective controls for Se and Co cofactors, whereas Cd and Pb were statistically lower. Unique correlations between metal cofactors for ICH cases were identified between Pb-W, Sr-W, Pb-V, and Cu-V. Stroke-related metalloproteins were identified, including calpain-15, protein-activated inward rectifier potassium channel 1, tau-tubulin kinase 1, and voltage-dependent L-type calcium channel subunit beta-3. Linear discriminant analysis (LDA) was able to classify patients between stroke cases or controls with 93% accuracy as well as classify patients with one of the four stroke groups with 85% accuracy. Additionally, this study found utmost importance in vanadium (V) and tungsten (W) correlations for both bound and total metal concentrations, suggestive of binding to transferrin or inhibition of oxidoreductases. Future work in stroke patients will seek to quantify varying selenoproteins, including selenoprotein P and glutathione peroxidase and identified zinc finger tissue leakage proteins, and further explore the role of trace metal fluctuations with transferrin.
中风是导致残疾和死亡的主要原因,在美国每40秒就有一人受其影响。中风生物标志物,包括那些可用于中风诊断血液检测的标志物,一直特别难以捉摸。我们进行了一项双盲研究,以确定用于中风诊断的人体血浆生物标志物,包括急性缺血性中风(AIS)和脑出血(ICH)。我们采用了一种三轨方法,基于总金属谱、金属蛋白中的金属辅因子水平以及与中风相关的金属蛋白的鉴定。该研究包括14对AIS病例对照和23对ICH病例对照。对照组在性别、种族和年龄(±5岁)方面与病例匹配。AIS病例的蛋白质结合辅因子硒(Se)和镉(Cd)在统计学上高于各自的对照组,而金属蛋白中金属辅因子浓度的独特相关性在铅(Pb)-钨(W)、锶(Sr)-钨、铅-钒(V)和铜(Cu)-钒之间被发现。ICH病例的硒和钴(Co)辅因子在统计学上高于各自的对照组,而镉和铅在统计学上较低。ICH病例的金属辅因子之间的独特相关性在铅-钨、锶-钨、铅-钒和铜-钒之间被发现。鉴定出了与中风相关的金属蛋白,包括钙蛋白酶-15、蛋白激活内向整流钾通道-1、tau-微管蛋白激酶-1和电压依赖性L型钙通道亚基β-3。线性判别分析(LDA)能够以93%的准确率将中风病例或对照患者进行分类,以及以85%的准确率将患者分为四个中风组之一。此外,本研究发现钒和钨的相关性对于结合态和总金属浓度极为重要,提示与转铁蛋白结合或对氧化还原酶的抑制作用。中风患者的未来研究将试图量化不同的硒蛋白,包括硒蛋白P和谷胱甘肽过氧化物酶,并鉴定锌指组织渗漏蛋白,以及进一步探索痕量金属波动与转铁蛋白的作用。