Blood gene expression signatures predict exposure levels

Abstract

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.

Fig. 1.

Workflow to predict the exposure level of the samples. The steps in the classifier-based and pattern-based approaches are shown.

Fig. 2.

Differentially expressed genes in blood that discriminate between exposure to subtoxic/nontoxic or toxic dose of APAP. Pictured is a subgroup of genes involved in immune response and inflammation. Gray filling of circles beside genes indicates identification of genes in the list of discriminatory genes (top circle, EPIG; middle circle, k-NN, bottom circle, ANOVA DME and DCE), and coloring of squares signals direction of change (red, up-regulation; green, down-regulation in comparison with mock-treated control).