Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology

Abstract

MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.

Figure 1. Diagram of the computational systems toxicology framework.

(a) The original data were collected from the Human MiRNA Disease Database and miREnvironment Database, and used to construct three bipartite networks: the EF-miRNA association (EMA), EF-disease association (EDA), and miRNA-disease association (MDA) networks. (b) Three methods, network-based inference (NBI), EF structure similarity-based inference (ES-SBI) and disease phenotypic similarity-based inference (DP-SBI), were developed to build the predictive model designated the predictive EF-miRNA-disease association model (PEMDAM). (c) The PEMDAM was built using the intersection of both of the prioritized lists from NBI and SBI. (d) Network visualization and analysis. EF: the environmental factor; S_T: the Tanimoto similarity between two EFs; S_S: the phenotypic similarity between two diseases.

Figure 2. Modules obtained from the miRNA-disease association (MDA) network.

The first number behind a module code denotes the node number in that module, while the latter number denotes the edge number, for example, there are 24 nodes and 46 edges in Module 1.

Figure 3. The receiver operating characteristic (ROC) curves of NBI and SBI.

ROC curves were generated by 100 simulations of 10-fold cross validation. miR2Dis is the abbreviation for the predicting putative miRNAs to diseases, and the other abbreviations can be deduced similarly. NBI: network-based inference; SBI: similarity-based inference, including ES-SBI (miR2EF and Dis2EF) and DP-SBI (miR2Dis and EF2Dis).

Figure 4. The discovered EF-miRNA-disease association network for breast cancer.

Breast cancer is shown as a hexagon. The network includes the associations between breast cancer and 287 known miRNAs, 26 known EFs, 40 predicted miRNAs and 6 predicted EFs as well as the known associations between these miRNAs and EFs.

Figure 5. The discovered EF-miRNA-disease association network for Tobacco, nicotine and benzo(a)pyrene (BaP).

Tobacco, nicotine and BaP are shown as magenta hexagons. The network contains 58 known miRNAs, 7 known diseases, 12 predicted miRNAs, 8 predicted diseases, and the known associations between these miRNAs and diseases.