Enriching Nanomaterials Omics Data: An Integration Technique to Generate Biological Descriptors

Abstract

produced, and one that has introduced integration methodologies for all available information to draw a more complete view of the biological mechanisms of the disease. Along these lines, a recently published review presents a cancer research-inspired pipeline that applies high-throughput and high-content profiling technologies integrated with omics profiling for assessing toxicity of chemicals and engineered nanomaterials. [6] The authors emphasize how, in analogy to biomarker discovery in cancer research, toxicology's aim is to identify genes or pathways from the "toxome" that could predict specific toxicity effects. Indeed, the search for new approach methodologies is largely focused on "big data" such as omics, but to this date there are relatively few concrete examples of how to utilize this type of data within the currently available risk assessment frameworks. Quantitative structure-activity relationship (QSAR) modeling remains a key method in toxicology, aiming mainly to predict the dependent variable of interest ("end-point") using structural and physicochemical data, although there are recent paradigms of successful prediction with omics data. [7,8] Grouping and read across is one of the most commonly used alternative approaches for risk assessment of novel substances and materials of industrial relevance submitted under the registration, evaluation, authorization and restriction of chemicals. Particularly, the read across method has not yet gained full regulatory acceptance, however recent efforts by the European chemicals agency (ECHA) are contributing toward its consistent and transparent evaluation.Methods for integrating biological information relative to genes' functions have been developed for omics data, [9][10][11][12] but have rarely been applied to QSAR modeling or grouping. For example, pathway analysis via gene set enrichment analysis (GSEA) methods [13] or the so-called connectivity mapping models have been applied to toxicity data to identify similar modes of action. [14,15] Smalley et al. [15] presented a connectivity mapping model to compare a query gene set microarray profile from a biological system exposed to a test substance, to those in a reference compounds database. In another approach, Williams and Halappanavar [16] identified pulmonary disease-related biclusters of genes from gene expression profiles derived from mouse disease models, which were then used to conduct GSEA The interest toward omics data is growing in the field of toxicology owing to the diverse knowledge they generate, which can improve prediction and dosage profiling for more accurate safety assessment. An integration methodology is presented where high-throughput omics data are enriched with biological-pathway information to produce a novel set of biological (BIO) descriptors by decomposing omics data to meaningful clusters in terms of both their mechanistic interpretation and correlation affinity. A generalized simulated annealing algorithm is employed to estimate the optimal partition of the enriched data and ...

A Data Fusion Pipeline for Generating and Enriching Adverse Outcome Pathway Descriptions

“…A number of current initiatives are strongly focused on the development and use of highthroughput data generation and omics methodology (Collins et al, 2017;Merrick et al, 2015). Such data are directly applicable to AOP-targeted pathway analyses, including, (1) disease-linked overrepresentation analysis, (2) disease-linked enrichment analysis among differentially expressed genes, (3) integration into AOP-based testing strategies as multivariate biomarkers (Tollefsen et al, 2014), (4) application as disease-linked descriptors in (quantitative) structure-activity relationships ((Q)SAR) approaches (Tsiliki et al 2017), and finally, based on all the above, enable (5) disease-linked grouping and read across among toxic agents through implementation into omics-based toxicity predictive tools. In relation to this, our laboratory recently described a Predictive Toxicogenomics Space (PTGS) scoring concept that gives any omics analysis a predictive toxicity effect (Kohonen et al, 2017).…”

Non‐Animal Strategies for Toxicity Assessment of Nanoscale Materials: Role of Adverse Outcome Pathways in the Selection of Endpoints

“…In addition, the molecular descriptors can be used to support mechanism‐aware QSAR approaches. [ 63,112 ] This type of molecular descriptors have also been used to establish dose‐response relationships and derive points of departures, [ 35 ] useful for ranking of NM potency to induce inflammation, [ 113 ] identification properties responsible for the effects [ 63 ] and for prediction of AOs such as lung fibrosis. [ 95 ] The resulting big data and comprehensive knowledge further supports AOP‐driven hazard characterization, including grouping and read across among substances, through implementation of omics‐based toxicity prediction tools (Figure 6, right panel).…”

Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale

“…Lastly, owing to their comprehensiveness and sensitivity in differentiating subtle toxicity response induced by two individual nanomaterials of different properties [40,42], high content toxicogenomic data can help define the structural properties of nanomaterials that are responsible for triggering an MIE and thus, an AO. For example, an approach that uses proteomic data to profile the protein corona of 84 gold nanoparticles as a basis for developing biological descriptors as input into quantitative structue activity relationship (Q) SAR modelling was recently described [156]. The integration of gene ontologies describing molecular function of the protein sets in the corona and using those to predict cellular uptake allowed the authors to draw conclusions on the molecular functions, i.e.…”