Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles.
Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles.
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DOI：
10.1021/es103606x
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发表时间：
2011-02-15
影响因子：
11.4
通讯作者：
Cohen, Yoram
中科院分区：
文献类型：
--
作者：
Rallo, Robert;France, Bryan;Liu, Rong;Nair, Sumitra;George, Saji;Damoiseaux, Robert;Giralt, Francesc;Nel, Andre;Bradley, Kenneth;Cohen, Yoram
通过使用荧光素酶记者的高通量筛选(HTS)试验，评估了小鼠巨噬细胞系对七种金属和金属氧化物纳米颗粒文库的反应，以寻找10条独立的毒性相关信号通路。通过自组织映射(SOM)分析，确定了不同纳米颗粒之间毒性反应的相似性。这一分析应用于HTS数据，使用严格标准化平均差(SSMD)量化了暴露于纳米材料的细胞群体相对于未处理细胞群体的信号通路反应(SPR)的重要性。考虑到数据的高维度和相对较小的数据集，SOM聚类的有效性是通过共识聚类技术建立的。对SPR特征的分析揭示了对应于(I)可能与ROS产生有关的对Al_2O_3、Au、Ag、SiO_2纳米颗粒的亚致死性促炎反应和(Ii)暴露于浓度范围为25μg/m L-100μg/m L的纳米氧化锌和铂的致死性遗传毒性反应的两个群组。除了识别和可视化集群并量化相似性度量之外，SOM方法还可以帮助建立预测性的定量-结构关系；然而，这将需要从工程纳米颗粒的组合库中产生更大的数据集。
The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small dataset the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sub-lethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25 μg/mL-100 μg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger datasets generated from combinatorial libraries of engineered nanoparticles.
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影响因子：
17.1
作者：
Fourches D;Pu D;Tassa C;Weissleder R;Shaw SY;Mumper RJ;Tropsha A
通讯作者：
Tropsha A
DOI：
10.1080/10937404.2010.483176
发表时间：
2010-02
期刊：
Journal of toxicology and environmental health. Part B, Critical reviews
影响因子：
--
作者：
Krewski D;Acosta D Jr;Andersen M;Anderson H;Bailar JC 3rd;Boekelheide K;Brent R;Charnley G;Cheung VG;Green S Jr;Kelsey KT;Kerkvliet NI;Li AA;McCray L;Meyer O;Patterson RD;Pennie W;Scala RA;Solomon GM;Stephens M;Yager J;Zeise L
通讯作者：
Zeise L
影响因子：
17.1
作者：
George, Saji;Pokhrel, Suman;Xia, Tian;Gilbert, Benjamin;Ji, Zhaoxia;Schowalter, Marco;Rosenauer, Andreas;Damoiseaux, Robert;Bradley, Kenneth A.;Maedler, Lutz;Nel, Andre E.
通讯作者：
Nel, Andre E.
影响因子：
6.4
作者：
Mangiameli, P;Chen, SK;West, D
通讯作者：
West, D
DOI：
10.1111/j.1432-1033.1991.tb16261.x
发表时间：
1991-10-01
期刊：
EUROPEAN JOURNAL OF BIOCHEMISTRY
影响因子：
--
作者：
NOSE, K;SHIBANUMA, M;KUROKI, T
通讯作者：
KUROKI, T