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Emerging research supports that triclosan (TCS), an antimicrobial agent found in thousands of consumer products, exacerbates colitis and colitis-associated colorectal tumorigenesis in animal models. While the intestinal toxicities of TCS require the presence of gut microbiota, the molecular mechanisms involved have not been defined. Here we show that intestinal commensal microbes mediate metabolic activation of TCS in the colon and drive its gut toxicology. Using a range of in vitro, ex vivo, and in vivo approaches, we identify specific microbial β-glucuronidase (GUS) enzymes involved and pinpoint molecular motifs required to metabolically activate TCS in the gut. Finally, we show that targeted inhibition of bacterial GUS enzymes abolishes the colitis-promoting effects of TCS, supporting an essential role of specific microbial proteins in TCS toxicity. Together, our results define a mechanism by which intestinal microbes contribute to the metabolic activation and gut toxicity of TCS, and highlight the importance of considering the contributions of the gut microbiota in evaluating the toxic potential of environmental chemicals. Triclosan (TCS), an antimicrobial agent commonly found in consumer products, has been reported to exacerbates colitis in animal models. Here, using in vitro and in vivo approaches, the authors show that gut bacterial enzymes can drive the metabolic activation and gut toxicity of TCS, highlighting an important role of intestinal microbial factors in the complex etiology of colitis.

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non‐carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross‐validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver‐operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

Per- and polyfluoroalkyl substances (PFAS) constitute a large class of environmentally persistent chemicals used in industrial and consumer products. Human exposure to PFAS is extensive, and PFAS contamination has been reported in drinking water and food supplies as well as in the serum of nearly all people. The most well-studied member of the PFAS class, perfluorooctanoic acid (PFOA), induces tumors in animal bioassays and has been associated with elevated risk of cancer in human populations. GenX, one of the PFOA replacement chemicals, induces tumors in animal bioassays as well. Using the Key Characteristics of Carcinogens framework for cancer hazard identification, we considered the existing epidemiological, toxicological and mechanistic data for 26 different PFAS. We found strong evidence that multiple PFAS induce oxidative stress, are immunosuppressive, and modulate receptor-mediated effects. We also found suggestive evidence indicating that some PFAS can induce epigenetic alterations and influence cell proliferation. Experimental data indicate that PFAS are not genotoxic and generally do not undergo metabolic activation. Data are currently insufficient to assess whether any PFAS promote chronic inflammation, cellular immortalization or alter DNA repair. While more research is needed to address data gaps, evidence exists that several PFAS exhibit one or more of the key characteristics of carcinogens.

The incidence of adverse reactions in food is very low, however, some food products contain toxins formed naturally due to their handling, processing and storage conditions. 5-(Hydroxymethyl)-2-furfural (HMF) can be formed by hydrogenation of sugar substances in some of manufactured foodstuffs and honey under elevated temperatures and reduced pH conditions following Maillard reactions. In previous studies, it was found that HMF was responsible for harmful (mutagenic, genotoxic, cytotoxic and enzyme inhibitory) effects on human health. HMF occurs in a wide variety of food products like dried fruit, juice, caramel products, coffee, bakery, malt and vinegar. The formation of HMF is not only an indicator of food storage conditions and quality, but HMF could also be used as an indicator of the potential occurrence of contamination during heat-processing of some food products such as coffee, milk, honey and processed fruits. This review focuses on HMF formation and summarizes the adverse effects of HMF on human health.

Advances in the biological sciences have led to an ongoing paradigm shift in toxicity testing based on expanded application of high-throughput in vitro screening and in silico methods to assess potential health risks of environmental agents. This review examines progress on the vision for toxicity testing elaborated by the US National Research Council (NRC) during the decade that has passed since the 2007 NRC report on Toxicity Testing in the 21st Century (TT21C). Concomitant advances in exposure assessment, including computational approaches and high-throughput exposomics, are also documented. A vision for the next generation of risk science, incorporating risk assessment methodologies suitable for the analysis of new toxicological and exposure data, resulting in human exposure guidelines is described. Case study prototypes indicating how these new approaches to toxicity testing, exposure measurement, and risk assessment are beginning to be applied in practice are presented. Overall, progress on the 20-year transition plan laid out by the US NRC in 2007 has been substantial. Importantly, government agencies within the United States and internationally are beginning to incorporate the new approach methodologies envisaged in the original TT21C vision into regulatory practice. Future perspectives on the continued evolution of toxicity testing to strengthen regulatory risk assessment are provided.

The research employed network toxicology and molecular docking techniques to systematically examine the potential carcinogenic effects and mechanisms of aspartame ( l -α-aspartyl- l -phenylalanine methyl ester). Aspartame, a commonly used synthetic sweetener, is widely applied in foods and beverages globally. In recent years, its safety issues, particularly the potential carcinogenic risk, have garnered widespread attention. The study first constructed an interaction network map of aspartame with gastric cancer targets using network toxicology methods and identified key targets and pathways. Preliminary validation was conducted through microarray data analysis and survival analysis, and molecular docking techniques were employed to further examine the binding affinity and modes of action of aspartame with key proteins. The findings suggest that aspartame has the potential to impact various cancer-related proteins, potentially raising the likelihood of cellular carcinogenesis by interfering with biomolecular function. Furthermore, the study found that the action patterns and pathways of aspartame-related targets are like the mechanisms of known carcinogenic pathways, further supporting the scientific hypothesis of its potential carcinogenicity. However, given the complexity of the in vivo environment, we also emphasize the necessity of validating these molecular-level findings in actual biological systems. The study introduces a fresh scientific method for evaluating the safety of food enhancers and provides a theoretical foundation for shaping public health regulations.

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

Mechanisms of carcinogenicity are discussed for metals and their compounds, classified as carcinogenic to humans or considered to be carcinogenic to humans: arsenic, antimony, beryllium, cadmium, chromium, cobalt, lead, nickel and vanadium. Physicochemical properties govern uptake, intracellular distribution and binding of metal compounds. Interactions with proteins (e.g., with zinc finger structures) appear to be more relevant for metal carcinogenicity than binding to DNA. In general, metal genotoxicity is caused by indirect mechanisms. In spite of diverse physicochemical properties of metal compounds, three predominant mechanisms emerge: (1) interference with cellular redox regulation and induction of oxidative stress, which may cause oxidative DNA damage or trigger signaling cascades leading to stimulation of cell growth; (2) inhibition of major DNA repair systems resulting in genomic instability and accumulation of critical mutations; (3) deregulation of cell proliferation by induction of signaling pathways or inactivation of growth controls such as tumor suppressor genes. In addition, specific metal compounds exhibit unique mechanisms such as interruption of cell–cell adhesion by cadmium, direct DNA binding of trivalent chromium, and interaction of vanadate with phosphate binding sites of protein phosphatases.

Polycyclic aromatic hydrocarbons (PAHs) have toxic impacts on humans and ecosystems. One of the most carcinogenic PAHs, benzo(a)pyrene (BaP), is efficiently bound to and transported with atmospheric particles. Laboratory measurements show that particle-bound BaP degrades in a few hours by heterogeneous reaction with ozone, yet field observations indicate BaP persists much longer in the atmosphere, and some previous chemical transport modeling studies have ignored heterogeneous oxidation of BaP to bring model predictions into better agreement with field observations. We attribute this unexplained discrepancy to the shielding of BaP from oxidation by coatings of viscous organic aerosol (OA). Accounting for this OA viscosity-dependent shielding, which varies with temperature and humidity, in a global climate/chemistry model brings model predictions into much better agreement with BaP measurements, and demonstrates stronger long-range transport, greater deposition fluxes, and substantially elevated lung cancer risk from PAHs. Model results indicate that the OA coating is more effective in shielding BaP in the middle/high latitudes compared with the tropics because of differences in OA properties (semisolid when cool/dry vs. liquid-like when warm/humid). Faster chemical degradation of BaP in the tropics leads to higher concentrations of BaP oxidation products over the tropics compared with higher latitudes. This study has profound implications demonstrating that OA strongly modulates the atmospheric persistence of PAHs and their cancer risks.