Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

Abstract

Relationships among fourteen different biological responses (including ten signaling pathway activities and four cytotoxicity effects) of murine macrophage (RAW264.7) and bronchial epithelial (BEAS-2B) cells exposed to six metal and metal oxide nanoparticles (NPs) were analyzed using both statistical and data mining approaches. Both the pathway activities and cytotoxicity effects were assessed using high-throughput screening (HTS) over an exposure period of up to 24 h and concentration range of 0.39-200 mg L(-1). HTS data were processed by outlier removal, normalization, and hit-identification (for significantly regulated cellular responses) to arrive at reliable multiparametric bioactivity profiles for the NPs. Association rule mining was then applied to the bioactivity profiles followed by a pruning process to remove redundant rules. The non-redundant association rules indicated that "significant regulation" of one or more cellular responses implies regulation of other (associated) cellular response types. Pairwise correlation analysis (via Pearson's χ(2) test) and self-organizing map clustering of the different cellular response types indicated consistency with the identified non-redundant association rules. Furthermore, in order to explore the potential use of association rules as a tool for data-driven hypothesis generation, specific pathway activity experiments were carried out for ZnO NPs. The experimental results confirmed the association rule identified for the p53 pathway and mitochondrial superoxide levels (via MitoSox reagent) and further revealed that blocking of the transcriptional activity of p53 lowered the MitoSox signal. The present approach of using association rule mining for data-driven hypothesis generation has important implications for streamlining multi-parameter HTS assays, improving the understanding of NP toxicity mechanisms, and selection of endpoints for the development of nanomaterial structure-activity relationships.

Frameworks and tools for risk assessment of manufactured nanomaterials

“…Quantitative Nano-structure Activity Relationships (QNAR) (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, Puzyn et al, 2009b, Toropov et al, 2007, Toropov and Leszczynski, 2006, Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013 In silico tools for hazard assessment (Liu et al, 2014, Liu et al, 2013a, Liu et al, 2013b In silico tools for hazard assessment Some of these tools are capable of assessing uncertainties. The Precautionary Matrix for Synthetic Nanomaterials uses a "specific framework conditions" criterion that represents uncertainties resulting from knowledge gaps with respect to the origin of the MNs, their characteristics and uses.…”

“…principal component analysis, neural networks) have also been applied to model the properties and effects of MNs (Puzyn et al, 2009b, Puzyn et al, 2009a, Toropov et al, 2007, Toropov and Leszczynski, 2006, Sayes et al, 2013, Lynch et al, 2014. Such methods were used for example in the EU-funded MODERN project with the aim to establish in silico modelling of the effects of metal and metal oxide MNs (Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013.…”

Analysis of cellular responses of macrophages to zinc ions and zinc oxide nanoparticles: a combined targeted and proteomic approach

“…RAW264 cells are one of the classical macrophage models and have been often used for studying the effects of nanoparticles in macrophages (e.g. in 26,[28][29][30][31][32][33] . They are also one of the few cell lines able to produce NO upon LPS stimulation 34 , a feature absent from the other classical human macrophage model THP-1.…”

The role of the food matrix and gastrointestinal tract in the assessment of biological properties of ingested engineered nanomaterials (iENMs): State of the science and knowledge gaps

“…Several studies have attempted to correlate various metal oxide ENM physiochemical descriptors with measurements of in vitro toxicity in order to develop structure-activity relationships (SARs). (Liu et al , 2014; Liu et al , 2013). Molecular SARs have been extensively and successfully used in the field of drug discovery and development (Akhtar et al , 2016; Cordomi et al , 2016; Monteiro et al , 2016; Putz et al , 2016; Sharma et al , 2016).…”