Abstract Preeclampsia (PE) affects 5–8% of all pregnancies and is associated with significant maternal and fetal morbidity and mortality. Placental mitochondrial dysfunction has been reported in PE. ...MicroRNAs (miRNA) are small non-coding RNAs that regulate gene expression through mRNA degradation and translational repression. MiR-210 has been previously shown to be upregulated in placentas from pregnancies complicated by PE. We hypothesized that placental mitochondrial dysfunction during PE can be mediated by miR-210. Placentas were collected at term from normotensive pregnancies (CTRL) and those complicated by severe PE ( n = 6 each) following c-section (no labor). Villous tissue from PE showed significantly increased levels of HIF-1α compared to CTRL with no change in corresponding mRNA expression but with reduced DNA-binding activity. Mitochondrial complex III was significantly decreased in PE along with significantly reduced protein expression in complex I and IV during PE. Among the four miRNAs tested, miR-210 showed significant up regulation in PE and significant downregulation of its target, ISCU mRNA. To understand the role of miR-210 in PE, loss- and gain-of-function studies were performed using primary trophoblasts. Trophoblasts were transfected with miR-210 inhibitor or pre-miR-210 and mitochondrial function was measured using Seahorse Extracellular Flux Analyzer. Cells transfected with pre-miR-210 showed significant reduction in oxygen consumption. In contrast, transfection of trophoblast with AntagomiR-210 was sufficient to prevent the DFO-mediated respiratory deficiency. These data collectively suggest that miR-210 overexpression during PE could be responsible for placental mitochondria dysfunction.
Early life exposure to fine particulate matter (PM) in air is associated with infant respiratory disease and childhood asthma, but limited epidemiological data exist concerning the impacts of ...ultrafine particles (UFPs) on the etiology of childhood respiratory disease. Specifically, the role of UFPs in amplifying Th2- and/or Th17-driven inflammation (asthma promotion) or suppressing effector T cells (increased susceptibility to respiratory infection) remains unclear. Using a mouse model of in utero UFP exposure, we determined early immunological responses to house dust mite (HDM) allergen in offspring challenged from 0 to 4 wk of age. Two mice strains were exposed throughout gestation: C57BL/6 (sensitive to oxidative stress) and BALB/C (sensitive to allergen exposure). Offspring exposed to UFPs in utero exhibited reduced inflammatory response to HDM. Compared with filtered air (FA)-exposed/HDM-challenged mice, UFP-exposed offspring had lower white blood cell counts in bronchoalveolar lavage fluid and less pronounced peribronchiolar inflammation in both strains, albeit more apparent in C57BL/6 mice. In the C57BL/6 strain, offspring exposed in utero to FA and challenged with HDM exhibited a robust response in inflammatory cytokines IL-13 and Il-17. In contrast, this response was lost in offspring exposed in utero to UFPs. Circulating IL-10 was significantly up-regulated in C57BL/6 offspring exposed to UFPs, suggesting increased regulatory T cell expression and suppressed Th2/Th17 response. Our results reveal that in utero UFP exposure at a level close to the WHO recommended PM guideline suppresses an early immune response to HDM allergen, likely predisposing neonates to respiratory infection and altering long-term pulmonary health.
Quantitative structure−activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine ...using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020–2022) as a successor to the First Project (2014–2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.
The aim of the SEURAT‐1 (Safety Evaluation Ultimately Replacing Animal Testing‐1) research cluster, comprised of seven EU FP7 Health projects co‐financed by Cosmetics Europe, is to generate a ...proof‐of‐concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT‐1 strategy is to adopt a mode‐of‐action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross‐cluster infrastructure project whose activities include the development of a data warehouse to provide a web‐accessible shared repository of research data and protocols, a physical compounds repository, reference or “gold compounds” for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA‐Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds.
The occurrence of mutagenicity in primary aromatic amines has been investigated using conformal prediction. The results of the investigation show that it is possible to develop mathematically proven
...models using conformal prediction and that the existence of uncertain classes of prediction, such as
(both classes assigned to a compound) and
(no class assigned to a compound), provides the user with additional information on how to use, further develop, and possibly improve future models. The study also indicates that the use of different sets of fingerprints results in models, for which the ability to discriminate varies with respect to the set level of acceptable errors.
Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the ...fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics‐based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics‐aided, cell‐based, high‐throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer‐driven evaluation of toxicity data into meaningful results. Future ‘cancer research‐inspired knowledge management’ of toxicological data will aid the translation of basic discovery results and chemicals‐ and materials‐testing data to information relevant to human health and environmental safety.
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