Lin Liang-Yu, Chun Chang Sunny, O'Hearn Jim, Hui Simon T, Seldin Marcus, Gupta Pritha, Bondar Galyna, Deng Mario, Jauhiainen Raimo, Kuusisto Johanna, Laakso Markku, Sinsheimer Janet S, Deb Arjun, Rau Christoph, Ren Shuxun, Wang Yibin, Lusis Aldons J, Wang Jessica J, Huertas-Vazquez Adriana
Department of Medicine, Division of Cardiology.
Division of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
G3 (Bethesda). 2018 Nov 6;8(11):3499-3506. doi: 10.1534/g3.118.200655.
We describe a simple bioinformatics method for biomarker discovery that is based on the analysis of global transcript levels in a population of inbred mouse strains showing variation for disease-related traits. This method has advantages such as controlled environment and accessibility to heart and plasma tissue in the preclinical selection stage. We illustrate the approach by identifying candidate heart failure (HF) biomarkers by overlaying mouse transcriptome and clinical traits from 91 Hybrid Mouse Diversity Panel (HMDP) inbred strains and human HF transcriptome from the Myocardial Applied Genomics Network (MAGNet) consortium. We found that some of the top differentially expressed genes correlated with known human HF biomarkers, such as galectin-3 and tissue inhibitor of metalloproteinase 1. Using ELISA assays, we investigated one novel candidate, Glycoprotein NMB, in a mouse model of chronic β-adrenergic stimulation by isoproterenol (ISO) induced HF. We observed significantly lower GPNMB plasma levels in the ISO model compared to the control group (p-value = 0.007). In addition, we assessed GPNMB plasma levels among 389 HF cases and controls from the METabolic Syndrome In Men (METSIM) study. Lower levels of GPNMB were also observed in patients with HF from the METSIM study compared to non-HF controls (p-value < 0.0001). In summary, we have identified several candidate biomarkers for HF using the cardiac transcriptome data in a population of mice that may be directly relevant and applicable to human populations.
我们描述了一种用于生物标志物发现的简单生物信息学方法，该方法基于对一群在疾病相关性状上表现出变异的近交系小鼠品系的整体转录水平进行分析。这种方法具有诸如环境可控以及在临床前选择阶段能够获取心脏和血浆组织等优势。我们通过将91个杂交小鼠多样性面板（HMDP）近交系小鼠的转录组和临床特征与心肌应用基因组学网络（MAGNet）联盟的人类心力衰竭（HF）转录组进行叠加，来举例说明该方法。我们发现一些差异表达最显著的基因与已知的人类HF生物标志物相关，如半乳糖凝集素-3和金属蛋白酶组织抑制剂1。使用酶联免疫吸附测定（ELISA）法，我们在异丙肾上腺素（ISO）诱导的慢性β-肾上腺素能刺激导致HF的小鼠模型中研究了一个新的候选物，糖蛋白NMB。我们观察到与对照组相比，ISO模型中的GPNMB血浆水平显著降低（p值 = 0.007）。此外，我们评估了来自男性代谢综合征（METSIM）研究的389例HF病例和对照中的GPNMB血浆水平。与非HF对照相比，METSIM研究中的HF患者的GPNMB水平也较低（p值 < 0.0001）。总之，我们利用小鼠群体中的心脏转录组数据鉴定了几种HF候选生物标志物，这些标志物可能与人类群体直接相关且适用。
