In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach

Abstract

Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the ‘independent action’ generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through ‘independent action’ to induce cytotoxicity to E. coli instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.

Graphical Abstract

Keywords:

• E. coli
• in silico
• in vitro
• mixtures
• nanoparticles
• toxicity

Acknowledgments

The authors are thankful to the National Science Foundation grant No. NSF/CREST HRD-1547754), and to the National Institutes of Health grant No. U54MD015929.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Author contributions

SK: In silico computation, validation, writing, editing, conceptualization; KP: In vitro experiments, writing, editing, conceptualization; DL: writing and editing; PBT: writing and editing; JL: conceptualization, writing, editing, supervision.