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1.
Front Immunol ; 13: 1019365, 2022.
Article in English | MEDLINE | ID: mdl-36311752

ABSTRACT

The inflammasome has been linked to diverse inflammatory and metabolic diseases, and tight control of inflammasome activation is necessary to avoid excessive inflammation. Kynurenic acid (KA) is a tryptophan metabolite in the kynurenine pathway. However, the roles and mechanisms of the regulation of inflammasome activation by KA have not yet been fully elucidated. Here, we found that KA suppressed caspase-1 activation and IL-1ß production in macrophages by specifically inhibiting canonical and noncanonical activation of the NLRP3 inflammasome. Mechanistically, KA reduced calcium mobilization through G-protein receptor 35 (GPR35), resulting in reduced mitochondrial damage and decreased mtROS production, thus blocking NLRP3 inflammasome assembly and activation. Importantly, KA prevented lipopolysaccharide-induced systemic inflammation, monosodium urate-induced peritoneal inflammation, and high-fat diet-induced metabolic disorder. Thus, KA ameliorated inflammation and metabolic disorders by blocking calcium mobilization-mediated NLRP3 inflammasome activation via GPR35. Our data reveal a novel mechanism for KA in the modulation of inflammasome activation and suggest that GPR35 might be a promising target for improving NLRP3 inflammasome-associated diseases by regulating calcium mobilization.


Subject(s)
Inflammasomes , NLR Family, Pyrin Domain-Containing 3 Protein , Humans , Inflammasomes/metabolism , NLR Family, Pyrin Domain-Containing 3 Protein/metabolism , Kynurenic Acid/pharmacology , Caspase 1/metabolism , Calcium/metabolism , Interleukin-1beta/metabolism , Carrier Proteins/metabolism , Inflammation/metabolism , GTP-Binding Proteins/metabolism , Receptors, G-Protein-Coupled/metabolism
2.
Sci Total Environ ; 545-546: 67-76, 2016 Mar 01.
Article in English | MEDLINE | ID: mdl-26745294

ABSTRACT

Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12 µg/l in the EU (0.053-3.3 µg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43 mg/kg · y in the EU (0.19-12 mg/kg · y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data.


Subject(s)
Environmental Monitoring/methods , Environmental Pollutants/analysis , Environmental Pollution/statistics & numerical data , Models, Statistical , Nanostructures/analysis , Silicon Dioxide/analysis , Risk Assessment , Switzerland , Waste Products/statistics & numerical data
3.
Nanotoxicology ; 10(4): 436-44, 2016.
Article in English | MEDLINE | ID: mdl-26554717

ABSTRACT

The environmental risks of five engineered nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes) were quantified in water, soils, and sediments using probabilistic Species Sensitivity Distributions (pSSDs) and probabilistic predicted environmental concentrations (PECs). For water and soil, enough ecotoxicological endpoints were found for a full risk characterization (between 17 and 73 data points per nanomaterial for water and between 4 and 20 for soil) whereas for sediments, the data availability was not sufficient. Predicted No Effect Concentrations (PNECs) were obtained from the pSSD and used to calculate risk characterization ratios (PEC/PNEC). For most materials and environmental compartments, exposure and effect concentrations were separated by several orders of magnitude. Nano-ZnO in freshwaters and nano-TiO2 in soils were the combinations where the risk characterization ratio was closest to one, meaning that these are compartment/ENM combinations to be studied in more depth with the highest priority. The probabilistic risk quantification allows us to consider the large variability of observed effects in different ecotoxicological studies and the uncertainty in modeled exposure concentrations. The risk characterization results presented in this work allows for a more focused investigation of environmental risks of nanomaterials by consideration of material/compartment combinations where the highest probability for effects with predicted environmental concentrations is likely.


Subject(s)
Ecotoxicology , Environmental Pollutants/analysis , Fullerenes/analysis , Nanostructures/analysis , Probability , Silver/analysis , Titanium/analysis , Zinc Oxide/analysis , Environmental Pollutants/toxicity , Fresh Water/chemistry , Fullerenes/toxicity , Geologic Sediments/chemistry , Models, Statistical , Nanostructures/toxicity , Nanotubes, Carbon/analysis , Nanotubes, Carbon/toxicity , Risk Assessment , Silver/toxicity , Soil/chemistry , Titanium/toxicity , Zinc Oxide/toxicity
4.
Waste Manag ; 36: 33-43, 2015 Feb.
Article in English | MEDLINE | ID: mdl-25524750

ABSTRACT

The use of engineered nanomaterials (ENMs) in diverse applications has increased during the last years and this will likely continue in the near future. As the number of applications increase, more and more waste with nanomaterials will be generated. A portion of this waste will enter the recycling system, for example, in electronic products, textiles and construction materials. The fate of these materials during and after the waste management and recycling operations is poorly understood. The aim of this work is to model the flows of nano-TiO2, nano-ZnO, nano-Ag and CNT in the recycling system in Switzerland. The basis for this study is published information on the ENMs flows on the Swiss system. We developed a method to assess their flow after recycling. To incorporate the uncertainties inherent to the limited information available, we applied a probabilistic material flow analysis approach. The results show that the recycling processes does not result in significant further propagation of nanomaterials into new products. Instead, the largest proportion will flow as waste that can subsequently be properly handled in incineration plants or landfills. Smaller fractions of ENMs will be eliminated or end up in materials that are sent abroad to undergo further recovery processes. Only a reduced amount of ENMs will flow back to the productive process of the economy in a limited number of sectors. Overall, the results suggest that risk assessment during recycling should focus on occupational exposure, release of ENMs in landfills and incineration plants, and toxicity assessment in a small number of recycled inputs.


Subject(s)
Environmental Pollutants/analysis , Metal Nanoparticles/analysis , Nanotubes, Carbon/analysis , Occupational Exposure , Recycling , Waste Management , Environmental Monitoring , Humans , Incineration , Risk Assessment , Switzerland , Waste Disposal Facilities
5.
Environ Pollut ; 181: 287-300, 2013 Oct.
Article in English | MEDLINE | ID: mdl-23856352

ABSTRACT

Scientific consensus predicts that the worldwide use of engineered nanomaterials (ENM) leads to their release into the environment. We reviewed the available literature concerning environmental concentrations of six ENMs (TiO2, ZnO, Ag, fullerenes, CNT and CeO2) in surface waters, wastewater treatment plant effluents, biosolids, sediments, soils and air. Presently, a dozen modeling studies provide environmental concentrations for ENM and a handful of analytical works can be used as basis for a preliminary validation. There are still major knowledge gaps (e.g. on ENM production, application and release) that affect the modeled values, but over all an agreement on the order of magnitude of the environmental concentrations can be reached. True validation of the modeled values is difficult because trace analytical methods that are specific for ENM detection and quantification are not available. The modeled and measured results are not always comparable due to the different forms and sizes of particles that these two approaches target.


Subject(s)
Environmental Pollutants/analysis , Models, Chemical , Nanostructures/analysis , Environment , Environmental Monitoring , Environmental Pollution/statistics & numerical data , Fullerenes/analysis , Soil/chemistry , Wastewater/analysis , Wastewater/statistics & numerical data , Zinc Oxide/analysis
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