Mathematical Nanotoxicoproteomics: Quantitative Characterization of Effects of Multi-walled Carbon Nanotubes (MWCNT) and TiO2 Nanobelts (TiO2-NB) on Protein Expression Patterns in Human Intestinal Cells

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Human and environmental impacts of nanoparticles: a scoping review of the current literature

Use of nanoparticles have established benefits in a wide range of applications, however, the effects of exposure to nanoparticles on health and the environmental risks associated with the production and use of nanoparticles are less well-established. The present study addresses this gap in knowledge by examining, through a scoping review of the current literature, the effects of nanoparticles on human health and the environment. We searched relevant databases including Medline, Web of Science, ScienceDirect, Scopus, CINAHL, Embase, and SAGE journals, as well as Google, Google Scholar, and grey literature from June 2021 to July 2021. After removing duplicate articles, the title and abstracts of 1495 articles were first screened followed by the full-texts of 249 studies, and this resulted in the inclusion of 117 studies in the presented review.In this contribution we conclude that while nanoparticles offer distinct benefits in a range of applications, they pose significant threats to humans and the environment. Using several biological models and biomarkers, the included studies revealed the toxic effects of nanoparticles (mainly zinc oxide, silicon dioxide, titanium dioxide, silver, and carbon nanotubes) to include cell death, production of oxidative stress, DNA damage, apoptosis, and induction of inflammatory responses. Most of the included studies (65.81%) investigated inorganic-based nanoparticles. In terms of biomarkers, most studies (76.9%) used immortalised cell lines, whiles 18.8% used primary cells as the biomarker for assessing human health effect of nanoparticles. Biomarkers that were used for assessing environmental impact of nanoparticles included soil samples and soybean seeds, zebrafish larvae, fish, and Daphnia magna neonates.From the studies included in this work the United States recorded the highest number of publications (n = 30, 25.64%), followed by China, India, and Saudi Arabia recording the same number of publications (n = 8 each), with 95.75% of the studies published from the year 2009. The majority of the included studies (93.16%) assessed impact of nanoparticles on human health, and 95.7% used experimental study design. This shows a clear gap exists in examining the impact of nanoparticles on the environment.

Changes of physico-chemical properties of nano-biomaterials by digestion fluids affect the physiological properties of epithelial intestinal cells and barrier models

Background

The widespread use of nano-biomaterials (NBMs) has increased the chance of human exposure. Although ingestion is one of the major routes of exposure to NBMs, it is not thoroughly studied to date. NBMs are expected to be dramatically modified following the transit into the oral-gastric-intestinal (OGI) tract. How these transformations affect their interaction with intestinal cells is still poorly understood. NBMs of different chemical nature—lipid-surfactant nanoparticles (LSNPs), carbon nanoparticles (CNPs), surface modified Fe₃O₄ nanoparticles (FNPs) and hydroxyapatite nanoparticles (HNPs)—were treated in a simulated human digestive system (SHDS) and then characterised. The biological effects of SHDS-treated and untreated NBMs were evaluated on primary (HCoEpiC) and immortalised (Caco-2, HCT116) epithelial intestinal cells and on an intestinal barrier model.

Results

The application of the in vitro SDHS modified the biocompatibility of NBMs on gastrointestinal cells. The differences between SHDS-treated and untreated NBMs could be attributed to the irreversible modification of the NBMs in the SHDS. Aggregation was detected for all NBMs regardless of their chemical nature, while pH- or enzyme-mediated partial degradation was detected for hydroxyapatite or polymer-coated iron oxide nanoparticles and lipid nanoparticles, respectively. The formation of a bio-corona, which contains proteases, was also demonstrated on all the analysed NBMs. In viability assays, undifferentiated primary cells were more sensitive than immortalised cells to digested NBMs, but neither pristine nor treated NBMs affected the intestinal barrier viability and permeability. SHDS-treated NBMs up-regulated the tight junction genes (claudin 3 and 5, occludin, zonula occludens 1) in intestinal barrier, with different patterns between each NBM, and increase the expression of both pro- and anti-inflammatory cytokines (IL-1β, TNF-α, IL-22, IL-10). Notably, none of these NBMs showed any significant genotoxic effect.

Conclusions

Overall, the results add a piece of evidence on the importance of applying validated in vitro SHDS models for the assessment of NBM intestinal toxicity/biocompatibility. We propose the association of chemical and microscopic characterization, SHDS and in vitro tests on both immortalised and primary cells as a robust screening pipeline useful to monitor the changes in the physico-chemical properties of ingested NBMs and their effects on intestinal cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00491-w.

Chemometrical analysis of proteomics data obtained from three cell types treated with multi-walled carbon nanotubes and TiO2 nanobelts$

Applications of nanomaterials in biomedical, industrial, and consumer goods areas are expanding rapidly because of their unique physicochemical properties. Hazard assessment of nanosubstances is necessary for the protection of human and ecological health. We studied the proteomics patterns of three cell lines: co-culture of Caco-2 and HT29-MTX cells, primary small airway epithelial cells, and THP-1macrophage-like cells. The cells were exposed at 10 μg and 100 μg concentrations for 3 and 24 hours to multi-walled carbon nanotubes and TiO₂ nanobelts (TiO₂-NB). The data were analysed with the hierarchical clustering method and principal components analysis. In all cases, time of exposure is the most important factor in separation and clustering of proteomics patterns. Furthermore, the sets of proteins, which are specific for long (24 hours) exposure, are identified.