Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

Abstract

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Figure 1. Flow of data and bioinformatics analyses.

Further information on the bioinformatics tools employed is given in Methods.

Figure 2. STRING-generated interaction network among the hub genes; the intensity of the edges reflects the strength of evidence; prediction methods: co-expression, experimentally observed interactions and curated databases; confidence score “high” (>70% probability of terms being found together in a metabolic map in the KEGG database).

Figure 3. Interactions between DEGs, DMGs and miRNAs related to lung cancer as derived using ConsensusPathDB and Cytoscape; node shapes: diamonds = DEGs, rectangles = DMGs, triangles = miRNAs; node colours: red = down-regulated (the darker the more down-regulation), green = up-regulated (the darker the more up-regulation); the colours of the node borders indicate the number of connecting edges (the darker the more connecting edges).

Figure 4. Comparison of smoking effects on the methylation of 49 AHRR CpG sites in blood leukocytes (this study) and lung alveolar macrophages at which the effects of smoking are significant (FDR < 0.05) in either tissue.