mRNA and miRNA regulatory networks reflective of multi-walled carbon nanotube-induced lung inflammatory and fibrotic pathologies in mice

Multi-walled carbon nanotubes (MWCNTs) are known for their transient inflammatory and progressive fibrotic pulmonary effects; however, the mechanisms underlying these pathologies are unknown. In this study, we used time-series microarray data of global lung mRNA and miRNA expression isolated from C57BL/6J mice exposed by pharyngeal aspiration to vehicle or 10, 20, 40, or 80 µg MWCNT at 1, 7, 28, or 56 days post-exposure to determine miRNA and mRNA regulatory networks that are potentially involved in MWCNT-induced inflammatory and fibrotic lung etiology. Using a non-negative matrix factorization method, we determined mRNAs and miRNAs with expression profiles associated with pathology patterns of MWCNT-induced inflammation (based on bronchoalveolar lavage score) and fibrosis (based on Sirius Red staining measured with quantitative morphometric analysis). Potential binding targets between pathology-related mRNAs and miRNAs were identified using Ingenuity Pathway Analysis and the miRTarBase, miRecords, and TargetScan databases. Using these experimentally validated and predicted binding targets, we were able to build molecular signaling networks that are potentially reflective of and play a role in MWCNT-induced lung inflammatory and fibrotic pathology. As understanding the regulatory networks between mRNAs and miRNAs in different disease states would be beneficial for understanding the complex mechanisms of pathogenesis, these identified genes and pathways may be useful for determining biomarkers of MWCNT-induced lung inflammation and fibrosis for early detection of disease.

Multi-walled carbon nanotube-induced gene expression in vitro: concordance with in vivo studies

There is a current interest in reducing the in vivo toxicity testing of nanomaterials in animals by increasing toxicity testing using in vitro cellular assays; however, toxicological results are seldom concordant between in vivo and in vitro models. This study compared global multi-walled carbon nanotube (MWCNT)-induced gene expression from human lung epithelial and microvascular endothelial cells in monoculture and coculture with gene expression from mouse lungs exposed to MWCNT. Using a cutoff of 10% false discovery rate and 1.5 fold change, we determined that there were more concordant genes (gene expression both up- or downregulated in vivo and in vitro) expressed in both cell types in coculture than in monoculture. When reduced to only those genes involved in inflammation and fibrosis, known outcomes of in vivo MWCNT exposure, there were more disease-related concordant genes expressed in coculture than monoculture. Additionally, different cellular signaling pathways are activated in response to MWCNT dependent upon culturing conditions. As coculture gene expression better correlated with in vivo gene expression, we suggest that cellular cocultures may offer enhanced in vitro models for nanoparticle risk assessment and the reduction of in vivo toxicological testing.

Integrated miRNA and mRNA Analysis of Time Series Microarray Data

The dynamic temporal regulatory effects of microRNA are not well known. We introduce a technique for integrating miRNA and mRNA time series microarray data with known disease pathology. The integrated analysis includes identifying both mRNA and miRNA that are signi cantly similar to the quantitative pathology. Potential regulatory miRNA/mRNA target pairs are identi ed through databases of both predicted and validated pairs. Finally, potential target pairs are ltered by examining the second derivatives of the fold changes over time. Our system was used on genome-wide microarray expression data of mouse lungs (n = 160) following aspiration of multi-walled carbon nanotubes. This system shows promise of readily identifying miRNA for further study as potential biomarker use.

Systems Approach to Identifying Relevant Pathways from Phenotype Information in Dose-Dependent Time Series Microarray Data

This study presents a novel computational approach to find relevant pathways from dose-dependent time series gene expression data which are significantly associated with a phenotype pattern pathological patterns in the comprehensive evaluation of database of pathways. Our system uses four steps: 1) identify a set of genes which change significantly in dose or time; 2) find phenotype patterns and gene coefficients for the genes found in step 1; 3) expand to genome-wide coefficients, and 4) identify pathways which are significantly relevant to a phenotype pattern. Our technique finds biologically relevant pathways with and without phenotype-constraints. Our system has been used on genome-wide expression profiles of mouse lungs (n=160) following aspiration of well dispersed multi-walled carbon nanotubes (MWCNT), in order to detect MWCNT-induced lung inflammation and related pathways. The identified significant pathways are supported by evidence in the literature and biological validation.