Blood gene expression signatures predict exposure levels
Blood gene expression signatures predict exposure levels
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DOI：
10.1073/pnas.0706987104
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发表时间：
2007-11-13
影响因子：
11.1
通讯作者：
Paules, R. S.
中科院分区：
文献类型：
--
作者：
Bushel, P. R.;Heinloth, A. N.;Paules, R. S.
为了正确应对潜在的不利暴露，卫生保健提供者需要准确的暴露水平指标。这些指标在对乙酰氨基酚（APAP）中毒的情况下特别重要，这是美国肝功能衰竭的主要原因。我们假设来自血细胞的基因表达模式将提供急性暴露水平的有用指标。为了验证这一假设，我们使用了暴露于APAP的大鼠的血液基因表达数据集，以两种预测算法训练分类器，并使用分析算法提取预测模式。在盲法、独立大鼠血液测试数据集上测试预测准确度，范围为88.9%至95.8%。基因组标记优于基于传统临床参数的预测。从血液中提取的模式的预测基因的表达谱表现出显着的（97%的准确性）跨组织APAP暴露预测时，肝脏基因表达数据被用作测试集。对人类样本的分析显示，基于大鼠歧视基因的人类直系同源物的血液表达水平，APAP中毒患者与对照个体分离。识别基因中的主要生物信号是暴露于毒性剂量的APAP后炎症反应的激活。这些结果支持了这一假设，即外周血细胞的基因表达数据可以提供有关暴露水平的有价值的信息，早在通过经典参数检测到肝损伤之前。它还支持血液中基因组标记物作为潜在急性肝损伤临床标记物的替代物的潜在用途。
To respond to potential adverse exposures properly, health care providers need accurate indicators of exposure levels. The indicators are particularly important in the case of acetaminophen (APAP) intoxication, the leading cause of liver failure in the U.S. We hypothesized that gene expression patterns derived from blood cells would provide useful indicators of acute exposure levels. To test this hypothesis, we used a blood gene expression data set from rats exposed to APAP to train classifiers in two prediction algorithms and to extract patterns for prediction using a profiling algorithm. Prediction accuracy was tested on a blinded, independent rat blood test data set and ranged from 88.9% to 95.8%. Genomic markers outperformed predictions based on traditional clinical parameters. The expression profiles of the predictor genes from the patterns extracted from the blood exhibited remarkable (97% accuracy) transtissue APAP exposure prediction when liver gene expression data were used as a test set. Analysis of human samples revealed separation of APAP-intoxicated patients from control individuals based on blood expression levels of human orthologs of the rat discriminatory genes. The major biological signal in the discriminating genes was activation of an inflammatory response after exposure to toxic doses of APAP. These results support the hypothesis that gene expression data from peripheral blood cells can provide valuable information about exposure levels, well before liver damage is detected by classical parameters. It also supports the potential use of genomic markers in the blood as surrogates for clinical markers of potential acute liver damage.