Systems toxicology meta-analysis—From aerosol exposure to nanotoxicology

Abstract Systems toxicology marks an important stage in the evolution of toxicology. It combines the insights from traditional toxicology end points, high-throughput data, and quantitative analysis of large cause-and-effect molecular network models that provide the most mechanistic information in the interpretation of high-throughput data. Here, we show an example on how pulmonary causal biological network models can be used in a meta-analysis of independent studies on engineered nanomaterials to gain mechanistic insight into the similarities and differences of the ways the engineered nanomaterials impact biological processes in the mouse lung. Meta-analyses using the lung network models could be used in various toxicological applications to find underlying trends in response to exposures, derive compound-specific mechanistic signatures, and translate between species.

[1]  Ashraf Elamin,et al.  Evaluation of the Tobacco Heating System 2.2. Part 7: Systems toxicological assessment of a mentholated version revealed reduced cellular and molecular exposure effects compared with mentholated and non-mentholated cigarette smoke. , 2016, Regulatory toxicology and pharmacology : RTP.

[2]  Elena Scotti,et al.  Comparative systems toxicology analysis of cigarette smoke and aerosol from a candidate modified risk tobacco product in organotypic human gingival epithelial cultures: A 3-day repeated exposure study. , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[3]  B. Nowack,et al.  Exposure modeling of engineered nanoparticles in the environment. , 2008, Environmental science & technology.

[4]  Yang Xiang,et al.  Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models , 2014, BMC Bioinformatics.

[5]  Raffaella Corvi,et al.  3S – Systematic, Systemic, and Systems Biology and Toxicology , 2019, ALTEX.

[6]  Andrew Williams,et al.  MWCNTs of different physicochemical properties cause similar inflammatory responses, but differences in transcriptional and histological markers of fibrosis in mouse lungs. , 2015, Toxicology and applied pharmacology.

[7]  Jennifer Park,et al.  A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue , 2011, BMC Systems Biology.

[8]  Nicklas Raun Jacobsen,et al.  Transcriptional profiling identifies physicochemical properties of nanomaterials that are determinants of the in vivo pulmonary response , 2015, Environmental and molecular mutagenesis.

[9]  Ashraf Elamin,et al.  A systems toxicology approach for comparative assessment: Biological impact of an aerosol from a candidate modified-risk tobacco product and cigarette smoke on human organotypic bronchial epithelial cultures. , 2017, Toxicology in vitro : an international journal published in association with BIBRA.

[10]  M C Peitsch,et al.  Systems toxicology meta-analysis of in vitro assessment studies: biological impact of a candidate modified-risk tobacco product aerosol compared with cigarette smoke on human organotypic cultures of the aerodigestive tract† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7tx , 2017, Toxicology research.

[11]  Hyosil Kim,et al.  Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity , 2015, PloS one.

[12]  Manuel C. Peitsch,et al.  Systems Approaches Evaluating the Perturbation of Xenobiotic Metabolism in Response to Cigarette Smoke Exposure in Nasal and Bronchial Tissues , 2013, BioMed research international.

[13]  Jennifer Park,et al.  Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems , 2015, Database J. Biol. Databases Curation.

[14]  Douglas G Altman,et al.  Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets , 2008, PLoS medicine.

[15]  Martin Hofmann-Apitius,et al.  Novel approaches to develop community-built biological network models for potential drug discovery , 2017, Expert opinion on drug discovery.

[16]  Ann McNeill,et al.  Reducing harm from tobacco use , 2013, Journal of psychopharmacology.

[17]  M. Agrawal,et al.  A Global Perspective of Fine Particulate Matter Pollution and Its Health Effects. , 2017, Reviews of environmental contamination and toxicology.

[18]  Elena Scotti,et al.  Assessment of the impact of aerosol from a potential modified risk tobacco product compared with cigarette smoke on human organotypic oral epithelial cultures under different exposure regimens. , 2018, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[19]  Yong Qian,et al.  Multiwalled Carbon Nanotube-Induced Gene Signatures in the Mouse Lung: Potential Predictive Value for Human Lung Cancer Risk and Prognosis , 2012, Journal of toxicology and environmental health. Part A.

[20]  Manuel C. Peitsch,et al.  An 8-Month Systems Toxicology Inhalation/Cessation Study in Apoe−/− Mice to Investigate Cardiovascular and Respiratory Exposure Effects of a Candidate Modified Risk Tobacco Product, THS 2.2, Compared With Conventional Cigarettes , 2015, Toxicological sciences : an official journal of the Society of Toxicology.

[21]  Ivan Rusyn,et al.  Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses. , 2007, Toxicological sciences : an official journal of the Society of Toxicology.

[22]  Rolf Altenburger,et al.  The Transcriptome of the Zebrafish Embryo After Chemical Exposure: A Meta-Analysis , 2017, Toxicological sciences : an official journal of the Society of Toxicology.

[23]  Manuel C. Peitsch,et al.  Construction of a computable cell proliferation network focused on non-diseased lung cells , 2011, BMC Systems Biology.

[24]  Julia Hoeng,et al.  Evaluation of the Tobacco Heating System 2.2. Part 4: 90-day OECD 413 rat inhalation study with systems toxicology endpoints demonstrates reduced exposure effects compared with cigarette smoke. , 2016, Regulatory toxicology and pharmacology : RTP.

[25]  Andreas Bender,et al.  Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data , 2018, Molecular omics.

[26]  Michael D Hays,et al.  Source apportionment of fine (PM1.8) and ultrafine (PM0.1) airborne particulate matter during a severe winter pollution episode. , 2009, Environmental science & technology.

[27]  Julia Hoeng,et al.  Case study: the role of mechanistic network models in systems toxicology. , 2014, Drug discovery today.

[28]  Manuel C. Peitsch,et al.  A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue , 2013, Bioinformatics and biology insights.

[29]  Julia Hoeng,et al.  Comparative effects of a candidate modified-risk tobacco product Aerosol and cigarette smoke on human organotypic small airway cultures: a systems toxicology approach† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7tx00152e , 2017, Toxicology research.

[30]  Julia Hoeng,et al.  3-D nasal cultures: Systems toxicological assessment of a candidate modified-risk tobacco product. , 2016, ALTEX.

[31]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[32]  Osamu Morita,et al.  Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics. , 2017, The Journal of toxicological sciences.

[33]  Kurt A. Gust,et al.  Transcriptomics provides mechanistic indicators of mixture toxicology for IMX-101 and IMX-104 formulations in fathead minnows (Pimephales promelas). , 2018, Aquatic toxicology.

[34]  Alexander Amberg,et al.  Cross-study and cross-omics comparisons of three nephrotoxic compounds reveal mechanistic insights and new candidate biomarkers. , 2011, Toxicology and applied pharmacology.

[35]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2017 , 2016, Nucleic Acids Res..

[36]  Hiroshi Yamada,et al.  Open TG-GATEs: a large-scale toxicogenomics database , 2014, Nucleic Acids Res..

[37]  Manuel C. Peitsch,et al.  Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence , 2013, Bioinformatics and biology insights.

[38]  Bengt Fadeel,et al.  Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk. , 2016, Toxicology and applied pharmacology.

[39]  Wonhee Jang,et al.  Meta-analysis of microarray and RNA-Seq gene expression datasets for carcinogenic risk: An assessment of Bisphenol A , 2017, Molecular & Cellular Toxicology.

[40]  Ashraf Elamin,et al.  Systems Toxicology Assessment of the Biological Impact of a Candidate Modified Risk Tobacco Product on Human Organotypic Oral Epithelial Cultures. , 2016, Chemical research in toxicology.

[41]  R. Snyder,et al.  Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. , 2006, Pharmacogenomics.

[42]  Igor Nabiev,et al.  Dependence of Nanoparticle Toxicity on Their Physical and Chemical Properties , 2018, Nanoscale Research Letters.

[43]  Manuel C. Peitsch,et al.  Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung , 2013 .

[44]  Andrew Williams,et al.  Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials , 2015, Particle and Fibre Toxicology.

[45]  Julia Hoeng,et al.  Evaluation of the Tobacco Heating System 2.2. Part 6: 90-day OECD 413 rat inhalation study with systems toxicology endpoints demonstrates reduced exposure effects of a mentholated version compared with mentholated and non-mentholated cigarette smoke. , 2016, Regulatory toxicology and pharmacology : RTP.

[46]  Natalie L. Catlett,et al.  Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data , 2013, BMC Bioinformatics.

[47]  Joakim Lundeberg,et al.  Using Whole-Exome Sequencing to Identify Genetic Markers for Carboplatin and Gemcitabine-Induced Toxicities , 2015, Clinical Cancer Research.

[48]  G. Oberdörster,et al.  Nanotoxicology: An Emerging Discipline Evolving from Studies of Ultrafine Particles , 2005, Environmental health perspectives.

[49]  Purvesh Khatri,et al.  Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury , 2017, American journal of respiratory cell and molecular biology.

[50]  Andrew Williams,et al.  Hepatic and Pulmonary Toxicogenomic Profiles in Mice Intratracheally Instilled With Carbon Black Nanoparticles Reveal Pulmonary Inflammation, Acute Phase Response, and Alterations in Lipid Homeostasis , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[51]  Sabrina Gioria,et al.  A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro , 2016, Nanotoxicology.