Amplified and distinctive genotoxicity of titanium dioxide nanoparticles in transformed yeast reporters with human cytochrome P450 (CYP) genes.

Titanium dioxide nanoparticles (nTiO2) have been considered a possible carcinogen to humans, but most existing studies have overlooked the role of human enzymes in assessing the genotoxicity of nTiO2. Here, a toxicogenomics-based in vitro genotoxicity assay using a GFP-fused yeast reporter library was employed to elucidate the genotoxic potential and mechanisms of nTiO2. Moreover, two new GFP-fused yeast reporter libraries containing either human CYP1A1 or CYP1A2 genes were constructed by transformation to investigate the potential modulation of nTiO2 genotoxicity in the presence of human CYP enzymes. This study found a lack of appreciable nTiO2 genotoxicity as indicated by the yeast reporter library in the absence of CYP expression but a significantly elevated indication of genotoxicity in either CYP1A1- or CYP1A2-expressing yeast. The intracellular reactive oxygen species (ROS) measurement indicated significantly higher ROS in yeast expressing either enzyme. The detected mitochondrial DNA damage suggested mitochondria as one of the target sites for oxidative damage by nTiO2 in the presence of either one of the CYP enzymes. The results thus indicated that the genotoxicity of nTiO2 was enhanced by human CYP1A1 or CYP1A2 enzyme and was associated with elevated oxidative stress, which suggested that the similar mechanisms could occur in human cells.

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh.

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making.

Face mask mandates and risk compensation: an analysis of mobility data during the COVID-19 pandemic in Bangladesh.

INTRODUCTION: Concerns have been raised about the potential for risk compensation in the context of mask mandates for mitigating the spread of COVID-19. However, the debate about the presence or absence of risk compensation for universal mandatory mask-wearing rules-especially in the context of COVID-19-is not settled yet. METHODS: Mobility is used as a proxy for risky behaviour before and after the mask mandates. Two sets of regressions are estimated to decipher (any) risk-compensating effect of mask mandate in Bangladesh. These include: (1) intervention regression analysis of daily activities at six types of locations, using pre-mask-mandate and post-mandate data; and (2) multiple regression analysis of daily new COVID-19 cases on daily mobility (lagged) to establish mobility as a valid proxy. RESULTS: (1) Statistically, mobility increased at all five non-residential locations, while home stays decreased after the mask mandate was issued; (2) daily mobility had a statistically significant association on daily new cases (with around 10 days of lag). Both significances were calculated at 95% confidence level. CONCLUSION: Community mobility had increased (and stay at home decreased) after the mandatory mask-wearing rule, and given mobility is associated with increases in new COVID-19 cases, there is evidence of risk compensation effect of the mask mandate-at least partially-in Bangladesh.

Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.

One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction, using 20 selected chemicals including model genotoxic chemicals and negative controls, respectively. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both Ames genotoxicity endpoints and in-vivo carcinogenicity in rats. A prediction accuracy of 76% with AUC = 0.81 was achieved while predicting in-vivo carcinogenicity with the top-ranked five biomarkers. For Ames genotoxicity prediction, the top-ranked five biomarkers were able to achieve prediction accuracy of 70% with AUC = 0.75. However, the specific biomarkers identified as the top-ranked five biomarkers are different for the two different phenotypic genotoxicity assays. The top-ranked biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications.

Dependence of Graphene Oxide (GO) Toxicity on Oxidation Level, Elemental Composition, and Size.

The mass production of graphene oxide (GO) unavoidably elevates the chance of human exposure, as well as the possibility of release into the environment with high stability, raising public concern as to its potential toxicological risks and the implications for humans and ecosystems. Therefore, a thorough assessment of GO toxicity, including its potential reliance on key physicochemical factors, which is lacking in the literature, is of high significance and importance. In this study, GO toxicity, and its dependence on oxidation level, elemental composition, and size, were comprehensively assessed. A newly established quantitative toxicogenomic-based toxicity testing approach, combined with conventional phenotypic bioassays, were employed. The toxicogenomic assay utilized a GFP-fused yeast reporter library covering key cellular toxicity pathways. The results reveal that, indeed, the elemental composition and size do exert impacts on GO toxicity, while the oxidation level exhibits no significant effects. The UV-treated GO, with significantly higher carbon-carbon groups and carboxyl groups, showed a higher toxicity level, especially in the protein and chemical stress categories. With the decrease in size, the toxicity level of the sonicated GOs tended to increase. It is proposed that the covering and subsequent internalization of GO sheets might be the main mode of action in yeast cells.

Comparative and mechanistic toxicity assessment of structure-dependent toxicity of carbon-based nanomaterials.

The wide application of carbon-based nanomaterials (CNMs) has resulted in the ubiquity of CNMs in the natural environment and they potentially impose adverse consequences on ecosystems and human health. In this study, we comprehensively evaluated and compared potential toxicological effects and mechanisms of seven CNMs in three representative types (carbon blacks, graphene nanoplatelets, and fullerenes), to elucidate the correlation between their physicochemical/structural properties and toxicity. We employed a recently-developed quantitative toxicogenomics-based toxicity testing system with GFP-fused yeast reporter library targeting main cellular stress response pathways, as well as conventional phenotype-based bioassays. The results revealed that DNA damage, oxidative stress, and protein stress were the major mechanisms of action for all the CNMs at sub-cytotoxic concentration levels. The molecular toxicity nature were concentration-dependent, and they exhibited both similarity within the same structural group and distinctiveness among different CNMs, evidencing the structure-driven toxicity of CNMs. The toxic potential based on toxicogenomics molecular endpoints revealed the remarkable impact of size and structure on the toxicity. Furthermore, the phenotypic endpoints derived from conventional phenotype-based bioassays correlated with quantitative molecular endpoints derived from the toxicogenomics assay, suggesting that the selected protein biomarkers captured the main cellular effects that are associated with phenotypic adverse outcomes.

Impact of solid residence time (SRT) on functionally relevant microbial populations and performance in full-scale enhanced biological phosphorus removal (EBPR) systems.

Investigations of the impact of solid residence time (SRT) on microbial ecology and performance of enhanced biological phosphorus removal (EBPR) process in full-scale systems have been scarce due to the challenges in isolating and examining the SRT from other complex plant-specific factors. This study performed a comprehensive evaluation of the influence of SRT on polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs) dynamics and on P removal performance at Clark County Water Reclamation District Facility in Las Vegas, USA. Five parallel treatment trains with separated clarifiers were operated with five different SRTs ranging from 6 to 40 days. Microbial community analysis using multiple molecular and Raman techniques suggested that the relative abundances and diversity of PAOs and GAOs in EBPR systems are highly affected by the SRT. The resultant EBPR system stability and performance can be potentially controlled and optimized by manipulating the system SRT, and shorter SRT (<10 days) seems to be preferred. PRACTITIONER POINTS: Phosphorus removal performance and kinetics are highly affected by the operational solid residence time (SRT), with lower and more stable effluent P level achieved at SRT < 10 days. Excessive long SRTs above that needed for nitrification may harm EBPR performance; additionally, excessive long SRT may favor GAOs to dominate over PAOs and thus further reducing efficient use of rbCOD for EBPR. Microbial population abundance and diversity, especially those functionally relevant PAOs and GAOs, can impact the P removal performances, and they are highly dependent on the operational solid residence time. EBPR performance can be potentially controlled and optimized by manipulating the system SRT, and shorter SRT (≤10 days) seems to be preferred at the influent rbCOD/P ratio and environmental conditions as in the plant studied.

The Composition and Implications of Polyphosphate-Metal in Enhanced Biological Phosphorus Removal Systems.

The individual cellular level and quantitative Polyphosphate (PolyP)-metal compositions in EBPR (enhanced biological phosphorus removal) systems have hardly been investigated and its potential link to EBPR performance therefore remain largely unknown. In this study, we applied scanning electron microscopy combined with energy dispersive X-ray spectroscopy (SEM/EDX) method that enabled detection and semiquantification of metal elemental compositions in intact intracellular PolyP granules in individual PAO (polyphosphate accumulating organism) cells. We, for the first time, revealed diverse and dynamic distributions of different metals ions in the PolyP-metal granules in different EBPR systems operated with the same influent metal composition but varying SRT of 5-30 days. We further demonstrated that the PolyP-metal composition diversity correlated with 16S rRNA gene based PAO phylogenetic diversity, suggesting the possible phylogeny-dependent PolyP-metal composition variation. The impact of PolyP metal composition in EBPR system, especially the Mg content in PolyP granules, was evidenced by the significant and strong positive correlation between PolyP-Mg content and the long-term stability of the four EBPR systems with varying SRTs. The PolyP-Mg content can therefore possibly serve as an indicator for EBPR performance monitoring. The results demonstrated that phenotyping techniques, such as PolyP-metal-based profiling, in compliment, or combined with genotyping techniques such as phylogenetic and functional gene sequencing, can provide more insights into the mechanisms and performance prediction of this important microbial ecosystem.

Genotoxicity Assessment of Drinking Water Disinfection Byproducts by DNA Damage and Repair Pathway Profiling Analysis.

Genotoxicity is considered a major concern for drinking water disinfection byproducts (DBPs). Of over 700 DBPs identified to date, only a small number has been assessed with limited information for DBP genotoxicity mechanism(s). In this study, we evaluated genotoxicity of 20 regulated and unregulated DBPs applying a quantitative toxicogenomics approach. We used GFP-fused yeast strains that examine protein expression profiling of 38 proteins indicative of all known DNA damage and repair pathways. The toxicogenomics assay detected genotoxicity potential of these DBPs that is consistent with conventional genotoxicity assays end points. Furthermore, the high-resolution, real-time pathway activation and protein expression profiling, in combination with clustering analysis, revealed molecular level details in the genotoxicity mechanisms among different DBPs and enabled classification of DBPs based on their distinct DNA damage effects and repair mechanisms. Oxidative DNA damage and base alkylation were confirmed to be the main molecular mechanisms of DBP genotoxicity. Initial exploration of QSAR modeling using moleular genotoxicity end points (PELI) suggested that genotoxicity of DBPs in this study was correlated with topological and quantum chemical descriptors. This study presents a toxicogenomics-based assay for fast and efficient mechanistic genotoxicity screening and assessment of a large number of DBPs. The results help to fill in the knowledge gap in the understanding of the molecular mechanisms of DBP genotoxicity.