Nanosafety-data-reusability-34-datasets

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics (2019)

Original Study Abstract

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs.

The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Data Sample

Nanoparticles Core size (nm) Shape Surface coatings Zeta Potential (mV) Specific surface area (m2/g) Exposure dose (ug/ml) Exposure duration (h) Tissue Cell type Cell line Microarray Method_Transcriptomics Cell cycle and Proliferation responses Cell death and Apoptosis responses DNA damage and Repair responses Cell adhesion and Extracellular matrix responses Inflammation and Immune responses Unfolded protein responses (UPR) and Endoplasmatic reticulum (ER) stress Metal Ion Responses Angiogenenesis responses Cytoskeleton organization responses
CoFe2O4 50 ? Silica ? ? 100 12 Kidney Cancer 293T Genechip_Affymetrix no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect
CoFe2O4 50 ? Silica ? ? 1000 12 Kidney Cancer 293T Genechip_Affymetrix triggered no_effect triggered triggered no_effect no_effect no_effect no_effect triggered
CuO_NPs 36 Spherical none 21 13,30 75 24 Brain Cancer SH-SY5Y Genechip_Affymetrix triggered triggered triggered triggered triggered no_effect triggered no_effect triggered
CuO_NPs 50 Unknown none -24 ? 25 24 Lung Cancer A549 WHG_Agilent triggered triggered no_effect no_effect triggered no_effect triggered no_effect no_effect
Au_NPs 5 Spherical Citrate -14 62,11 60 72 Intestinal Cancer CACO-2 WHG_Agilent no_effect triggered triggered no_effect triggered no_effect triggered no_effect no_effect
Au_NPs 5 Spherical Citrate -14 62,11 20 72 Intestinal Cancer CACO-2 WHG_Agilent no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect
Au_NPs 5 Spherical Citrate -14 62,11 60 24 Intestinal Cancer CACO-2 WHG_Agilent no_effect triggered triggered no_effect triggered no_effect triggered no_effect no_effect
Au_NPs 5 Spherical Citrate -14 62,11 20 24 Intestinal Cancer CACO-2 WHG_Agilent no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect
Au_NPs 32 Spherical Citrate -14 10,35 20 72 Intestinal Cancer CACO-2 WHG_Agilent no_effect no_effect no_effect no_effect no_effect no_effect no_effect no_effect triggered

Data Summary

Group Count
# of Toxicity Endpoints 247
# of Nanomaterial types 10
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