Rates of Hydroxyl Radical Production from Transition Metals and Quinones in a Surrogate Lung Fluid

Charrier, Jessica G.; Anastasio, Cort

doi:10.1021/acs.est.5b01606

Cited by 106 publications

(111 citation statements)

References 51 publications

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“…Cu, NQ, etc.) also varied with the tested species and concentration 12 , 37 , 38 . Bacterial cells induced a very low DTT oxidation (~10–20 pmolDTT.min −1 ) within the 30 min.…”

Section: Resultsmentioning

confidence: 99%

The unexpected role of bioaerosols in the Oxidative Potential of PM

Samaké

Uzu

Martins

et al. 2017

Sci Rep

107

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Bioaerosols represent up to 15–25% of PM by mass, but there is currently no assessment of their impact on Oxidative Potential (OP), or capacity of particulate matter (PM) to produce damaging oxidative reactions in the human lungs. Here, the OP of selected bioaerosols (bacteria cells vs fungal spores) was assessed through the cell-free DTT assay. Results show that bioaerosols induce Reactive Oxygen Species (ROS) production, varying along the microorganism type, species, and concentration. Fungal spores show up to 10 times more ROS generation than bacterial cells. At the highest concentrations, fungal spores present as much oxidative reactivity as the most redox-active airborne chemicals (Copper, Naphtoquinone). Moreover, bioaerosols substantially influence OP of ambient PM and that of its chemical constituents: in presence of A. fumigatus spores, the OP of Cu/NQ is increased by a factor of 2 to 5, whereas, 104 and 105 S. epidermidis bacterial cells.mL−1 halves the OP of Cu/NQ. Finally, viable and gamma-rays-killed model bioaerosols present similar oxidative reactivity, suggesting a metabolism-independent cellular mechanism. These results reveal the importance of bioaerosols for PM reactivity. PM toxicity can be modified due to bioaerosols contribution or by their ability to modulate the OP of toxic chemicals present in PM.

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“…Cu, NQ, etc.) also varied with the tested species and concentration 12 , 37 , 38 . Bacterial cells induced a very low DTT oxidation (~10–20 pmolDTT.min −1 ) within the 30 min.…”

Section: Resultsmentioning

confidence: 99%

The unexpected role of bioaerosols in the Oxidative Potential of PM

Samaké

Uzu

Martins

et al. 2017

Sci Rep

107

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“…Indeed, any assay where an important redox-active chemical species exhibits a non-linear concentration-response curve should suffer from a similar bias. For example, Cu has non-linear concentration-response curves in assays that measure the generation of hydroxyl radical ( · OH) (Vidrio et al, 2008; Charrier and Anastasio, 2015) and hydrogen peroxide (HOOH) (Shen et al, 2011; Charrier et al, 2014) in a cell-free surrogate lung fluid. Since Cu is a major component of both OH and HOOH generation from ambient PM (DiStefano et al, 2009; Vidrio et al, 2009; Shen and Anastasio, 2011; Shen et al, 2011; Shen and Anastasio, 2012; Richards-Henderson et al, 2015), the measured rates are likely affected by the PM mass employed in each extract, although this has not been examined.…”

Section: Resultsmentioning

confidence: 99%

A bias in the “mass-normalized” DTT response – An effect of non-linear concentration-response curves for copper and manganese

Charrier

McFall

et al. 2016

Atmospheric Environment

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The dithiothreitol (DTT) assay is widely used to measure the oxidative potential of particulate matter. Results are typically presented in mass-normalized units (e.g., pmols DTT lost per minute per microgram PM) to allow for comparison among samples. Use of this unit assumes that the mass-normalized DTT response is constant and independent of the mass concentration of PM added to the DTT assay. However, based on previous work that identified non-linear DTT responses for copper and manganese, this basic assumption (that the mass-normalized DTT response is independent of the concentration of PM added to the assay) should not be true for samples where Cu and Mn contribute significantly to the DTT signal. To test this we measured the DTT response at multiple PM concentrations for eight ambient particulate samples collected at two locations in California. The results confirm that for samples with significant contributions from Cu and Mn, the mass-normalized DTT response can strongly depend on the concentration of PM added to the assay, varying by up to an order of magnitude for PM concentrations between 2 and 34 μg mL−1. This mass dependence confounds useful interpretation of DTT assay data in samples with significant contributions from Cu and Mn, requiring additional quality control steps to check for this bias. To minimize this problem, we discuss two methods to correct the mass-normalized DTT result and we apply those methods to our samples. We find that it is possible to correct the mass-normalized DTT result, although the correction methods have some drawbacks and add uncertainty to DTT analyses. More broadly, other DTT-active species might also have non-linear concentration-responses in the assay and cause a bias. In addition, the same problem of Cu- and Mn-mediated bias in mass-normalized DTT results might affect other measures of acellular redox activity in PM and needs to be addressed.

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“…Fe, Co, Ni, Cu, Zn, V, Cr, Mn, As, Pb, and Cd may boost the generation of reactive oxygen species (ROS). Excess ROS can overwhelm the antioxidant defense system in the body, causing oxidative stress, inflammation, and disease [6][7][8]. Therefore, trace elements pose a substantial threat to human health, even though they account for only a small fraction of the total mass of PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

Source Identification of Trace Elements in PM2.5 at a Rural Site in the North China Plain

Liu

Wang

et al. 2020

Atmosphere

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An intensive sampling of PM 2.5 was conducted at a rural site (Gucheng) in the North China Plain from 22 October to 23 November 2016. A total of 25 elements (Al, Na, Cl, Mg, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Sr, Cd, Ba, Pb, and Sb) from PM 2.5 filter samples collected daily were measured using a wavelength dispersive X-ray fluorescence spectrometer. Cl, S, and K were the most abundant elements, with average concentrations of 2077.66 ng m −3 (range 118.88-4638.96 ng m −3 ), 1748.78 ng m −3 (range 276.67-4335.59 ng m −3 ), and 1287.07 ng m −3 (range 254.90-2748.63 ng m −3 ), respectively. Among noncrustal trace metal elements, the concentration of Zn was the highest, with an average of 397.74 ng m −3 (range 36.45-1602.96 ng m −3 ), followed by Sb and Pb, on average, of 299.20 ng m −3 and 184.52 ng m −3 , respectively. The morphologies of PM 2.5 samples were observed using scanning electron microscopy. The shape of the particles was predominantly spherical, chain-like, and irregular. Positive matrix factorization analysis revealed that soil dust, following by industry, secondary formation, vehicle emissions, biomass and waste burning, and coal combustion, were the main sources of PM 2.5 . The results of cluster, potential source contribution function, and concentration weighted trajectory analyses suggested that local emissions from Hebei Province, as well as regional transport from Beijing, Tianjin, Shandong, and Shanxi Province, and long-range transport from Inner Mongolia, were the main contributors to PM 2.5 pollution.

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Rates of Hydroxyl Radical Production from Transition Metals and Quinones in a Surrogate Lung Fluid

Cited by 106 publications

References 51 publications

The unexpected role of bioaerosols in the Oxidative Potential of PM

The unexpected role of bioaerosols in the Oxidative Potential of PM

A bias in the “mass-normalized” DTT response – An effect of non-linear concentration-response curves for copper and manganese

Source Identification of Trace Elements in PM2.5 at a Rural Site in the North China Plain

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