Adverse outcome pathway for benzene induced toxicity through reverse causal reasoning and network perturbation analysis

Abstract

The increase and improvement in molecular profiling technologies have enabled the acquisition of large datasets consisting of measurements for many molecular entities. These datasets allow an understanding of molecular profiles of, for example, a disease, drug and compounds action, or toxicity. Furthermore, gene expression profiling experiments usually produce extensive lists of differential expressed genes that characterize the comparison between the two states in the study, such as disease versus healthy or treatment versus control. In this study two approaches are used to interpret these lists, take out relevant and reliable hypotheses and quantify biological network perturbations: Reverse Causal Reasoning (RCR) and Network Perturbation Analysis (NPA); towards exploring the full potential of these datasets. The RCR and NPA methods are implemented and tested on the transcriptome of benzene-exposed individuals to propose a hypothesis of biological processes alterations. Several proposed altered biological mechanisms are in agreement with literature evidence, meaning that this approach can be a valuable tool for understanding mechanisms associated with benzene exposure. While some of them have not been studied and false positives are a possibility, this approach indicates possible candidates, that have not been verified by the literature as potential future directions in research.

The increase and improvement in molecular profiling technologies have enabled the acquisition of large datasets consisting of measurements for many molecular entities. These datasets allow an understanding of molecular profiles of, for example, a disease, drug and compounds action, or toxicity. Furthermore, gene expression profiling experiments usually produce extensive lists of differential expressed genes that characterize the comparison between the two states in the study, such as disease versus healthy or treatment versus control.In this study two approaches are used to interpret these lists, take out relevant and reliable hypotheses and quantify biological network perturbations: Reverse Causal Reasoning (RCR) and Network Perturbation Analysis (NPA); towards exploring the full potential of this datasets. The RCR and NPA methods are implemented and tested on the transcriptome of benzene-exposed individuals to propose a hypothesis of biological processes alterations.Several proposed altered biological mechanisms are in agreement with literature evidence, meaning that this approach can be a valuable tool for understanding mechanisms associated with benzene exposure. While some of them have not been studied and false positives are a possibility, this approach indicates possible candidates, that have not been verified by the literature as potential future directions in research.