Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Oct 22;13(10):11203-11213.
doi: 10.1021/acsnano.9b04229. Epub 2019 Sep 17.

Critical Comparison of the Superoxide Dismutase-like Activity of Carbon Antioxidant Nanozymes by Direct Superoxide Consumption Kinetic Measurements

Affiliations

Critical Comparison of the Superoxide Dismutase-like Activity of Carbon Antioxidant Nanozymes by Direct Superoxide Consumption Kinetic Measurements

Gang Wu et al. ACS Nano. .

Abstract

The superoxide dismutase-like activity of poly(ethylene glycolated) hydrophilic carbon clusters (PEG-HCCs), anthracite and bituminous graphene quantum dots (PEG-aGQDs and PEG-bGQDs, respectively), and two fullerene carbon nanozymes, tris malonyl-C60 fullerene (C3) and polyhydroxylated-C60 fullerene (C60-OHn), were compared using direct optical stopped-flow kinetic measurements, together with three native superoxide dismutases (SODs), CuZnSOD, MnSOD, and FeSOD, at both pH 12.7 and 8.5. Computer modeling including both SOD catalytic steps and superoxide self-dismutation enabled the best choice of catalyst concentration with minimal contribution to the observed kinetic change from the substrate self-dismutation. Biexponential fitting to the kinetic data ranks the rate constant (M-1 s-1) in the order of PEG-HCCs > CuZnSOD ≈ MnSOD ≈ PEG-aGQDs ≈ PEG-bGQDs > FeSOD ≫ C3 > C60-OHn at pH 12.7 and MnSOD > CuZnSOD ≈ PEG-HCCs > FeSOD > PEG-aGQDs ≈ PEG-bGQDs ≫ C3 ≈ C60-OHn at pH 8.5. Nonlinear regression of the kinetic model above yielded the same ranking as the biexponential fit, but provided better mechanistic insight. The data obtained by freeze-quench EPR direct assay at pH 12.7 also yield the same ranking as stopped-flow data. This is a necessary assessment of a panel of proclaimed carbon nano SOD mimetics using the same two direct methods, revealing a dramatic, 3-4 orders of magnitude difference in SOD activity between PEG-HCCs/PEG-GQDs from soluble fullerenes.

Keywords: comparative study; freeze−quench EPR; nanozymes; stopped-flow; superoxide dismutase activity.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Time courses of SO self-dismutation at different pHs. The time courses of A244 were followed after mixing 5 mM of KO2 in DMSO with buffers at different pHs ([KO2]final = 192 μM): 12.7 (black), 8.5 (red), 8.0 (blue), and 7.5 (green). Data at times shorter than 5 ms are not shown due to contamination by mixing artifacts. Each time course is labelled with the 2nd-order rate constants k in M−1s−1, obtained by fitting to eq 6 (see Experimental Methods section). Inset, log k vs pH and linear fit.
Figure 2.
Figure 2.
SO quenching activities of SODs and nanozymes at pH 12.7. Black lines in each panel: time course of self dismutation of 192 μM KO2. (A) Time courses of SO quenching by 20 nM SOD: CuZnSOD, MnSOD (blue), and FeSOD (green). (B) Time courses of SO quenching by nanozymes: 20 nM PEG-HCCs (red), 20 nM of PEG-aGQDs (green), 20 nM of PEG-bGQDs (blue), 2 μM C3 (yellow), and 30 μM C60-OHn (purple). The time courses with PEG-HCCs, PEG-aGQDs, and PEG-bGQDs were vertically adjusted to remove the background absorbance of these nanozymes. Due to the high concentrations of C3 and C60-OHn required in the reactions, their time courses showed large vertical backgrounds and were vertically shifted arbitrarily to bring the traces into y-axis scale range. Data at time shorter than 5 ms are not shown due to contamination by mixing artifacts.
Figure 3.
Figure 3.
Computer simulation of SO dismutation with different [SO]. The time courses of SO optical signal were simulated using SCoP based on a kinetic model including simultaneously progressing irreversible self-dismutation (eq 1) and catalyzed dismutation (eq 2 and 3). The SO self-dismutation rate constant was set at measured value at pH 8.5, ks = 3.3 × 104 M−1s−1 (Figure 1); the two rate constants of a catalyzed SO dismutation were set identical kr = ko; [catalyst] was set at 20 nM; [SO] varied from 10 nM to 100 μM. The time course of self-dismutation is represented by black circles and those of catalyzed reactions by solid lines. Ranges of rate constants kr/ko varied from: 1 × 105 M−1s−1, black; 1 × 106 M−1s−1, red; 1 × 107 M−1s−1, green;1 × 108 M−1s−1, yellow; 1 × 109 M−1s−1, blue. [O2•] is marked in each panel: 100 μM (A); 10 μM (B); 1 μM (C); 100 nM (D); 10 nM (E).
Figure 3.
Figure 3.
Computer simulation of SO dismutation with different [SO]. The time courses of SO optical signal were simulated using SCoP based on a kinetic model including simultaneously progressing irreversible self-dismutation (eq 1) and catalyzed dismutation (eq 2 and 3). The SO self-dismutation rate constant was set at measured value at pH 8.5, ks = 3.3 × 104 M−1s−1 (Figure 1); the two rate constants of a catalyzed SO dismutation were set identical kr = ko; [catalyst] was set at 20 nM; [SO] varied from 10 nM to 100 μM. The time course of self-dismutation is represented by black circles and those of catalyzed reactions by solid lines. Ranges of rate constants kr/ko varied from: 1 × 105 M−1s−1, black; 1 × 106 M−1s−1, red; 1 × 107 M−1s−1, green;1 × 108 M−1s−1, yellow; 1 × 109 M−1s−1, blue. [O2•] is marked in each panel: 100 μM (A); 10 μM (B); 1 μM (C); 100 nM (D); 10 nM (E).
Figure 4.
Figure 4.
Computer simulation of SO dismutation with different [catalyst]. The time courses of SO optical signal were simulated using SCoP based on a kinetic model including parallel irreversible self-dismutation (eq 1) and catalyzed dismutation (eq 2 and 3). The SO self-dismutation rate constant was set at measured value at pH 8.5, ks = 3.3 × 104 M−1s−1 (Figure 1); the rate constants of catalyzed SO dismutation were set at identical kr = ko; [SO] was set at 100 μM and [catalyst] varied from 20 nM to 20 μM. The time course of self-dismutation is represented by black circles and those of catalyzed reactions by solid lines. The [catalyst] used in simulations are marked in each panel: 20 nM (A) & (F); 100 nM (B); 500 nM (C); 2 μM (D); 20 μM (E).The varying rate constants used in each panel: 1 × 105 M−1s−1, black; 1 × 106 M−1s−1, red; 1 × 107 M−1s−1, green;1 × 108 M−1s−1, yellow; 1 × 109 M−1s−1, blue. Panel F: biphasic exponential fits to the simulated time courses (same as in (A) represented with open circles) using the same color scheme as above. The fit rate constants are divided by [catalyst] = 20 nM to obtain the 2nd-order rate constants: 3.7 × 108/9.0 × 107 M−1s−1 (black); 3.7 × 108/9.0 × 107 M−1s−1 (red); 3.8 × 108/9.2 × 107 M−1s−1 (green); 4.8 × 108/1.4 × 108 M−1s−1 (yellow); 2.2 × 109/9.8 × 108 M−1s−1 (blue).
Figure 5.
Figure 5.
SO quenching activities of SODs and nanozymes at pH 8.5. Black circles in each panel: self dismutation of 192 μM KO2. (A) SO consumption activities of SODs (circles): 40 nM CuZnSOD (red), 40 nM MnSOD (blue), and 200 nM FeSOD (green). (B) SO consumption activities of nanozymes (circles): 100 nM PEG-HCCs (red), 500 nM of PEG-aGQDs (green), 500 nM of PEG-bGQDs (blue), 13.5 μM C3 (yellow), and 30 μM C60-OHn (purple). The time courses with PEG-HCCs, PEG-aGQDs, and PEG-bGQDs were vertically adjusted to remove the background absorbance of these nanozymes. Due to the high concentrations of C3 and C60-OHn required in the reactions, their time courses showed large vertical backgrounds and were vertically shifted arbitrarily to bring the traces into y-axis scale range. Data at time shorter than 5 ms are not shown due to mixing artifacts. Black lines in both (A) and (B), fit by 2nd-order function (eq 6) for SO self-dismutation; other lines are SCoP fits to the experimental data: (A) CuZnSOD (red), MnSOD (blue), and FeSOD (green), (B) PEG-HCCs (red), PEG-aGQDs (green), and PEG-bGQDs (blue).

Similar articles

Cited by

References

    1. Sheng Y; Abreu IA; Cabelli DE; Maroney MJ; Miller A-F; Teixeira M; Valentine JS Superoxide Dismutases and Superoxide Reductases. Chem. Rev 2014, 114, 3854–3918. - PMC - PubMed
    1. Valentine JS; Doucette PA; Zittin Potter S Copper-Zinc Superoxide Dismutase and Amyotrophic Lateral Sclerosis. Annu. Rev. Biochem 2005, 74, 563–593. - PubMed
    1. Rosen DR Mutations in Cu/Zn Superoxide Dismutase Gene are Associated with Familial Amyotrophic Lateral Sclerosis. Nature 1993, 364, 36. - PubMed
    1. Konzack A; Jakupovic M; Kybaichuk K; Görlach A; Dombrowski F; Miinalainene I; Sormunen R; Kietzmann T Mitochondrial Dysfunction Due to Lack of Manganese Superoxide Dismutase Promotes Hepatocarcinogenesis. Antioxid. Redox. Signal 2015, 23, 1059–1075. - PMC - PubMed
    1. Tejero J; Shiva S; Gladwin MT Sources of Vascular Nitric Oxide and Reactive Oxygen Species and Their Regulation. Physiol. Rev 2019, 99, 311–379. - PMC - PubMed

Publication types