Assessment of pulmonary fibrosis using weighted gene co-expression network analysis

For many industrial chemicals toxicological data is sparse regarding several regulatory endpoints, so there is a high and often unmet demand for NAMs that allow for screening and prioritization of these chemicals. In this proof of concept case study we propose multi-gene biomarkers of compounds' ability to induce lung fibrosis and demonstrate their application in vitro. For deriving these biomarkers we used weighted gene co-expression network analysis to reanalyze a study where the time-dependent pulmonary gene-expression in mice treated with bleomycin had been documented. We identified eight modules of 58 to 273 genes each which were particularly activated during the different phases (inflammatory; acute and late fibrotic) of the developing fibrosis. The modules' relation to lung fibrosis was substantiated by comparison to known markers of lung fibrosis from DisGenet. Finally, we show the modules' application as biomarkers of chemical inducers of lung fibrosis based on an in vitro study of four diketones. Clear differences could be found between the lung fibrosis inducing diketones and other compounds with regard to their tendency to induce dose-dependent increases of module activation as determined using a previously proposed differential activation score and the fraction of differentially expressed genes in the modules. Accordingly, this study highlights the potential use of composite biomarkers mechanistic screening for compound-induced lung fibrosis.