In vivo epigenetic effects induced by engineered nanomaterials: A case study of copper oxide and laser printer-emitted engineered nanoparticles

Evidence continues to grow on potential environmental health hazards associated with engineered nanomaterials (ENMs). While the geno- and cytotoxic effects of ENMs have been investigated, their potential to target the epigenome remains largely unknown. The aim of this study is two-fold: 1) determining whether or not industry relevant ENMs can affect the epigenome in vivo and 2) validating a recently developed in vitro epigenetic screening platform for inhaled ENMs. Laser printer-emitted engineered nanoparticles (PEPs) released from nano-enabled toners during consumer use and copper oxide (CuO) were chosen since these particles induced significant epigenetic changes in a recent in vitro companion study. In this study, the epigenetic alterations in lung tissue, alveolar macrophages and peripheral blood from intratracheally instilled mice were evaluated. The methylation of global DNA and transposable elements (TEs), the expression of the DNA methylation machinery and TEs, in addition to general toxicological effects in the lung were assessed. CuO exhibited higher cell-damaging potential to the lung, while PEPs showed a greater ability to target the epigenome. Alterations in the methylation status of global DNA and TEs, and expression of TEs and DNA machinery in mouse lung were observed after exposure to CuO and PEPs. Additionally, epigenetic changes were detected in the peripheral blood after PEPs exposure. Altogether, CuO and PEPs can induce epigenetic alterations in a mouse experimental model, which in turn confirms that the recently developed in vitro epigenetic platform using macrophage and epithelial cell lines can be successfully utilized in the epigenetic screening of ENMs.     

Novel natural product-based oral topical rinses and toothpastes to prevent periodontal diseases

There is increasing public interest in natural or herbal-based healthcare products. This trend is not only visible in supermarkets and dental practices, but also in the scientific world. An improving number of clinical trials are being conducted to validate the claims made about these products in regards to periodontal health. Among single component preparations, Aloe vera and green tea are the most studied natural ingredients. Concerning polyherbal mixtures, triphala has garnered great interest. The effects of these natural products on periodontal health is encouraging, with almost all studies showing an inhibitory effect on plaque accumulation and an improvement in gingival health. However, more studies are needed to be able to design clinical guidelines to guide the use of these natural products in periodontal practice. For most of these products, few studies are available and, moreover, the available studies are limited in duration, the number of participants, and the specific composition of the natural product is often not described in detail.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.