Repeated Exposure of Macrophages to Synthetic Amorphous Silica Induces Adaptive Proteome Changes and a Moderate Cell Activation

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.

Amorphous silica nanoparticles and the human gut microbiota: a relationship with multiple implications

Amorphous silica nanoparticles (ASNP) are among the nanomaterials that are produced in large quantities. ASNP have been present for a long time in several fast-moving consumer products, several of which imply exposure of the gastrointestinal tract, such as toothpastes, food additives, drug excipients, and carriers. Consolidated use and experimental evidence have consistently pointed to the very low acute toxicity and limited absorption of ASNP. However, slow absorption implies prolonged exposure of the intestinal epithelium to ASNP, with documented effects on intestinal permeability and immune gut homeostasis. These effects could explain the hepatic toxicity observed after oral administration of ASNP in animals. More recently, the role of microbiota in these and other ASNP effects has attracted increasing interest in parallel with the recognition of the role of microbiota in a variety of conditions. Although evidence for nanomaterial effects on microbiota is particularly abundant for materials endowed with bactericidal activities, a growing body of recent experimental data indicates that ASNPs also modify microbiota. The implications of these effects are recounted in this contribution, along with a discussion of the more important open issues and recommendations for future research.

The internal dose makes the poison: higher internalization of polystyrene particles induce increased perturbation of macrophages

Plastics are emerging pollutants of great concern. Macroplastics released in the environment degrade into microplastics and nanoplastics. Because of their small size, these micro and nano plastic particles can enter the food chain and contaminate humans with still unknown biological effects. Plastics being particulate pollutants, they are handled in the human body by scavenger cells such as macrophages, which are important players in the innate immune system. Using polystyrene as a model of micro and nanoplastics, with size ranging from under 100 nm to 6 microns, we have showed that although non-toxic, polystyrene nano and microbeads alter the normal functioning of macrophages in a size and dose-dependent manner. Alterations in the oxidative stress, lysosomal and mitochondrial functions were detected, as well as changes in the expression of various surface markers involved in the immune response such as CD11a/b, CD18, CD86, PD-L1, or CD204. For each beads size tested, the alterations were more pronounced for the cell subpopulation that had internalized the highest number of beads. Across beads sizes, the alterations were more pronounced for beads in the supra-micron range than for beads in the sub-micron range. Overall, this means that internalization of high doses of polystyrene favors the emergence of subpopulations of macrophages with an altered phenotype, which may not only be less efficient in their functions but also alter the fine balance of the innate immune system.

About the Transient Effects of Synthetic Amorphous Silica: An In Vitro Study on Macrophages

Silica (either crystalline or amorphous) is widely used for different applications and its toxicological assessment depends on its characteristics and intended use. As sustained inflammation induced by crystalline silica is at the root of silicosis, investigating the inflammatory effects induced by amorphous silicas and their persistence is needed. For the development of new grades of synthetic amorphous silicas, it is also desirable to be able to understand better the factors underlying potential adverse effects. Therefore, we used an optimized in vitro macrophage system to investigate the effects of amorphous silicas, and their persistence. By using different amorphous silicas, we demonstrated that the main driver for the adverse effects is a low size of the overall particle/agglomerate; the second driver being a low size of the primary particle. We also demonstrated that the effects were transient. By using silicon dosage in cells, we showed that the transient effects are coupled with a decrease of intracellular silicon levels over time after exposure. To further investigate this phenomenon, a mild enzymatic cell lysis allowed us to show that amorphous silicas are degraded in macrophages over time, explaining the decrease in silicon content and thus the transiency of the effects of amorphous silicas on macrophages.