Feature-Based Molecular Networking Analysis of the Metabolites Produced by In Vitro Solid-State Fermentation Reveals Pathways for the Bioconversion of Epigallocatechin Gallate.

Dark teas are prepared by a microbial fermentation process. Flavan-3-ol B-ring fission analogues (FBRFAs) are some of the key bioactive constituents that characterize dark teas. The precursors and the synthetic mechanism involved in the formation of FBRFAs are not known. Using a unique solid-state fermentation system with ß-cyclodextrin inclusion complexation as well as targeted chromatographic isolation, spectroscopic identification, and Feature-based Molecular Networking on the Global Natural Products Social Molecular Networking web platform, we reveal that dihydromyricetin and the FBRFAs, including teadenol A and fuzhuanin A, are derived from epigallocatechin gallate upon exposure to fungal strains isolated from Fuzhuan brick tea. In particular, the strains from subphylum Pezizomycotina were key drivers for these B-/C-ring oxidation transformations. These are the same transformations seen during the fermentation process of dark teas. These discoveries set the stage to enrich dark teas and other food products for these health-promoting constituents.

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models.

The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 µm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 µg/m3 (range: 7.00-73.40 µg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.

Biomagnetic monitoring combined with support vector machine: a new opportunity for predicting particle-bound-heavy metals.

Biomagnetic monitoring includes fast and simple methods to estimate airborne heavy metals. Leaves of Osmanthus fragrans Lour and Ligustrum lucidum Ait were collected simultaneously with PM10 from a mega-city of China during one year. Magnetic properties of leaves and metal concentrations in PM10 were analyzed. Metal concentrations were estimated using leaf magnetic properties and meteorological factors as input variables in support vector machine (SVM) models. The mean concentrations of many metals were highest in winter and lowest in summer. Hazard index for potentially toxic metals was 5.77, a level considered unsafe. The combined carcinogenic risk was higher than precautionary value (10-4). Ferrimagnetic minerals were dominant magnetic minerals in leaves. Principal component analysis indicated iron & steel industry and soil dust were the common sources for many metals and magnetic minerals on leaves. However, the poor simulation results obtained with multiple linear regression confirmed strong nonlinear relationships between metal concentrations and leaf magnetic properties. SVM models including leaf magnetic variables as inputs yielded better simulation results for all elements. Simulations were promising for Ti, Cd and Zn, whereas relatively poor for Ni. Our study demonstrates the feasibility of prediction of airborne heavy metals based on biomagnetic monitoring of tree leaves.

Particulate matter exposure disturbs inflammatory cytokine homeostasis associated with changes in trace metal levels in mouse organs.

Few studies have focused on the impact of particulate matter (PM) exposure with respect to the relationship between PM-induced inflammation and the levels of trace metals in tissues and organs. In this study, C57BL/6 male mice were exposed to ambient air alongside control mice breathing air filtered through a high-efficiency particulate air (HEPA) filter. In both groups, mRNA levels of pro- and anti-inflammatory cytokines were measured after 4, 8 and 12 weeks together with the trace metal contents of the lungs, heart, liver, hippocampus and blood. PM exposure resulted in a general upward trend in the levels of pro-inflammatory cytokines in lung, heart, liver and hippocampus. By contrast, IL-10 mRNA expression varied depending on the organ, with a continuous upward trend in heart and liver and an up-regulation at 8 weeks followed by a down-regulation at 12 weeks in lung and hippocampus. The disturbed homeostasis of inflammatory cytokines was accompanied by changes in trace metal levels in the mice. These alterations may have constituted a compensatory effect conferring protection from inflammatory damage. However, prolonged PM exposure finally resulted in the deficiency of several essential trace metals in the lungs and hippocampus, which may have contributed to the observed histological changes typical of an inflammatory response.