Toxicology is the scientific pursuit of identifying and classifying the toxic effect of a substance, as well as exploration and understanding of the adverse effects due to toxic exposure. The modern toxicological efforts have been driven by the human industrial exploits in the production of engineered substances with advanced interdisciplinary scientific collaborations. These engineered substances must be carefully tested to ensure public safety. This task is now more challenging than ever with the employment of new classes of chemical compounds, such as the engineered nanomaterials. Toxicological paradigms have been redefined over the decades to be more agile, versatile, and sensitive. On the other hand, the design of toxicological studies has become more complex, and the interpretation of the results is more challenging. Toxicogenomics offers a wealth of data to estimate the gene regulation by inspection of the alterations of many biomolecules (such as DNA, RNA, proteins, and metabolites). The response of functional genes can be used to infer the toxic effects on the biological system resulting in acute or chronic adverse effects. However, the dense data from toxicogenomics studies is difficult to analyze, and the results are difficult to interpret. Toxicogenomic evidence is still not completely integrated into the regulatory framework due to these drawbacks. Nanomaterial properties such as particle size, shape, and structure increase complexity and unique challenges to Nanotoxicology.
This thesis presents the efforts in the standardization of toxicogenomics data by showcasing the potential of omics in nanotoxicology and providing easy to use tools for the analysis, and interpretation of omics data. This work explores two main themes: i) omics experimentation in nanotoxicology and investigation of nanomaterial effect by analysis of the omics data, and ii) the development of analysis pipelines as easy to use tools that bring advanced analytical methods to general users. In this work, I explored a potential solution that can ensure effective interpretability and reproducibility of omics data and related experimentation such that an independent researcher can interpret it thoroughly. DNA microarray technology is a well-established research tool to estimate the dynamics of biological molecules with high throughput. The analysis of data from these assays presents many challenges as the study designs are quite complex. I explored the challenges of omics data processing and provided bioinformatics solutions to standardize this process. The responses of individual molecules to a given exposure is only partially informative and more sophisticated models, disentangling the complex networks of dynamic molecular interactions, need to be explored. An analytical solution is presented in this thesis to tackle down the challenge of producing robust interpretations of molecular dynamics in biological systems. It allows exploring the substructures in molecular networks underlying mechanisms of molecular adaptation to exposures. I also present here a multi-omics approach to defining the mechanism of action for human cell lines exposed to nanomaterials. All the methodologies developed in this project for omics data processing and network analysis are implemented as software solutions that are designed to be easily accessible also by users with no expertise in bioinformatics. Our strategies are also developed in an effort to standardize omics data processing and analysis and to promote the use of omics-based evidence in chemical risk assessment.Toxicology is the scientific pursuit of identifying and classifying the toxic effect of a substance, as well as exploration and understanding of the adverse effects due to toxic exposure. The modern toxicological efforts have been driven by the human industrial exploits in the production of engineered substances with advanced interdisciplinary scientific collaborations. These engineered substances must be carefully tested to ensure public safety. This task is now more challenging than ever with the employment of new classes of chemical compounds, such as the engineered nanomaterials. Toxicological paradigms have been redefined over the decades to be more agile, versatile, and sensitive. On the other hand, the design of toxicological studies has become more complex, and the interpretation of the results is more challenging. Toxicogenomics offers a wealth of data to estimate the gene regulation by inspection of the alterations of many biomolecules (such as DNA, RNA, proteins, and metabolites). The response of functional genes can be used to infer the toxic effects on the biological system resulting in acute or chronic adverse effects. However, the dense data from toxicogenomics studies is difficult to analyze, and the results are difficult to interpret. Toxicogenomic evidence is still not completely integrated into the regulatory framework due to these drawbacks. Nanomaterial properties such as particle size, shape, and structure increase complexity and unique challenges to Nanotoxicology.
This thesis presents the efforts in the standardization of toxicogenomics data by showcasing the potential of omics in nanotoxicology and providing easy to use tools for the analysis, and interpretation of omics data. This work explores two main themes: i) omics experimentation in nanotoxicology and investigation of nanomaterial effect by analysis of the omics data, and ii) the development of analysis pipelines as easy to use tools that bring advanced analytical methods to general users. In this work, I explored a potential solution that can ensure effective interpretability and reproducibility of omics data and related experimentation such that an independent researcher can interpret it thoroughly. DNA microarray technology is a well-established research tool to estimate the dynamics of biological molecules with high throughput. The analysis of data from these assays presents many challenges as the study designs are quite complex. I explored the challenges of omics data processing and provided bioinformatics solutions to standardize this process. The responses of individual molecules to a given exposure is only partially informative and more sophisticated models, disentangling the complex networks of dynamic molecular interactions, need to be explored. An analytical solution is presented in this thesis to tackle down the challenge of producing robust interpretations of molecular dynamics in biological systems. It allows exploring the substructures in molecular networks underlying mechanisms of molecular adaptation to exposures. I also present here a multi-omics approach to defining the mechanism of action for human cell lines exposed to nanomaterials. All the methodologies developed in this project for omics data processing and network analysis are implemented as software solutions that are designed to be easily accessible also by users with no expertise in bioinformatics. Our strategies are also developed in an effort to standardize omics data processing and analysis and to promote the use of omics-based evidence in chemical risk assessment
