Current Opinion in Toxicology最新文献

The gut microbiome, critical to maintaining vertebrate homeostasis, is susceptible to various exposures. In some cases, these exposures induce dysbiosis, wherein the microbiome changes into a state conducive to disease progression. To better prevent, manage, and treat health disorders, we need to define which exposures induce dysbiosis. Contemporary methods face challenges due to the immense diversity of the exposome and the restricted throughput of conventional experimental tools used for dysbiosis evaluation. We propose integrating high-throughput model systems as an augment to traditional techniques for rapid identification of dysbiosis-inducing agents. Although high-throughput screening tools revolutionized areas such as pharmacology and toxicology, their incorporation in gut microbiome research remains limited. One particularly powerful high-throughput model system is the zebrafish, which affords access to scalable in vivo experimentation involving a complex gut microbiome. Numerous studies have employed this model to identify potential dysbiosis triggers. However, its potential could be further harnessed via innovative study designs, such as evaluation of synergistic effects from combined exposures, expansions to the methodological toolkit to discern causal effects of microbiota, and efforts to assess and improve the translational relevance of the model. Ultimately, this burgeoning experimental resource can accelerate the discovery of agents that underlie dysbiotic disorders.

Chemical pollution is characterised by sequential and simultaneous exposure to unintentional complex mixtures. The almost infinite number of real-life mixtures poses major challenges for investigations of all possible exposure scenarios through whole mixture or component-based approaches. As a pragmatic approach in data-poor situations, the application of a Mixture Assessment Factor to single substances assessments under REACH was announced in the European Chemicals Strategy for Sustainability. Current proposals for this factor are based on the assumption that mixtures behave additively, assuming that synergistic interactions are rare. This assumption is based on eight reviews published in the last 30 years. Synergistic deviations from additivity greater than two-fold were reported in roughly 5% of investigated mixtures. This was more, rather than less, frequent in the handful of suitable studies of low-dose mammalian mixture toxicity. This frequency is representative of mixtures toxicology studies in the literature and should not be interpreted as the frequency of synergisms in real-world exposures. Understanding the frequency and likelihood of synergisms would entail detailed understanding of the co-occurrence of groups of substances giving rise to such interactions in relevant environmental media. Assumptions that synergistic interactions in real-life mixtures are rare appear to be premature. While further research is required, potential synergisms should not be omitted from debates on the conservatism or otherwise of mixture allocation factor or other regulatory approaches to protect people and environment from mixture effects.

The composition of the gut microbiome is highly variable and can be altered by external substances including engineered nanomaterials (ENM). Solid particles are abundantly present in our food including intentionally produced particles in the size-range below 100 nm, which are termed nanoparticles. ENM inter alia includes nanoparticles and their agglomerates which occur as food additives, and contaminants in human food and drinking water. In the past five years, more than thirty studies on the effects of ENM on the microbiome were published. These are summarized and reviewed here. Clearly, ENM can affect the gut microbiome in diverse ways. Many studies are exploratory and do not always show unanimous effects; few studies actually demonstrate a link to adverse effects on the host. Based on these data, future studies can be designed to allow for the assessment of hazards and risks of ENM via microbiome changes. We are discussing studies providing such information and needs for future studies to understand and assess ENM's impact on the gut microbiome and then on human health.

Distinguishing between mixtures of substances with similar and dissimilar modes of action is believed to have implications for judgements whether mixture risks might arise when all chemicals comply with their regulatory limits. However, differentiating between similar and dissimilar action unnecessarily complicates mixture risk assessments. Whether substances in a mixture have similar or dissimilar mechanisms is often difficult to decide. Only a few cases show the validity of dissimilar action; concepts based on similar action (dose addition) generally produce good approximations of observed mixture effects. Further, the quantitative differences of mixture effect predictions that follow from assumptions of similar or dissimilar action are rather small. To avoid underestimations of mixture risks, chemicals that produce common adverse outcomes should be assessed together, and this should not be restricted to chemicals with similar mechanisms. Assertions that compliance with Health-Based Guidance Values (HBGVs) protects against mixture risks can be de-constructed to reveal several false assumptions, among them that chemicals generally act according to dissimilar action and that HBGVs are equivalent to “zero-effect levels.” The protection goals enshrined in HBGVs for single chemicals may not be realized when there is co-exposure to chemicals that produce the same effect, regardless of perceived modes of action of the mixture components.

In “traditional toxicity,” a toxic substance reaches the target molecules, such as nucleic acids, proteins including enzymes, membranes, and other components in, on, or around the cell, and induces malfunction. In contrast, in “signal toxicity,” the chemical binds to a receptor and/or modulates the levels of its endogenous ligands. After that, the chemical itself is not important. The aberrant signal from the receptor initiates a cascade of molecular events that leads to various changes in the cells and organs. If the signal is abnormal for them in timing, kind, and amount, it may induce irreversible adverse effects if the organ is under development or maturation. This review summarizes the key characteristics of developmental toxicity referring to the concept of “signal toxicity.”

The environmental exposome impacts the human microbiome, which in turn influences various immune and metabolic functions. Microbiome dysbiosis can be triggered by exposome components and may lead to the development of atopies or exacerbation of existing allergic conditions via the disruption of epithelial barriers and alteration of immune responses. The gut microbiome is a key regulator of the immune system, and immune homeostasis is maintained through bidirectional communication between the gut and distant tissues and organs. The skin microbiome also plays a central role in tuning host immunity and may contribute significantly to the emergence and progression of allergic skin disease. The loss of balance between the host and its microbiota can lead to the breakdown of immune tolerance and may thus contribute to allergy development. In this review, we discuss the current understanding of host-microbe and environmental interactions which have been linked with atopy and allergic diseases.

Gut microbiota plays an important role in host health and disease. They metabolize food-born chemicals, nutrients, drugs and other xenobiotics, while these chemicals and their metabolites can also modulate the gut microbiota. The present review provided an overview of the state-of-the-art in the application of in vitro and in silico based new approach methodologies (NAMs) to study these microbiome–host interactions in a non-invasive way and without the need for the use of animal or human studies.

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine “thresholds of similarity.” Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.