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. 2022 Nov 17;12(11):1128.
doi: 10.3390/metabo12111128.

Glycyrrhizic Acid Mitigates Tripterygium-Glycoside-Tablet-Induced Acute Liver Injury via PKM2 Regulated Oxidative Stress

Affiliations

Glycyrrhizic Acid Mitigates Tripterygium-Glycoside-Tablet-Induced Acute Liver Injury via PKM2 Regulated Oxidative Stress

Qixin Wang et al. Metabolites. .

Abstract

Tripterygium glycoside tablet (TGT), as a common clinical drug, can easily cause liver damage due to the narrow therapeutic window. Glycyrrhizic acid (GA) has a hepatoprotective effect, but the characteristics and mechanism of GA's impact on TGT-induced acute liver injury by regulating oxidative stress remain unelucidated. In this study, TGT-induced acute liver injury models were established in vitro and in vivo. The levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), superoxide dismutase (SOD), malondialdehyde (MDA), glutathione (GSH), catalase (CAT), lactate dehydrogenase (LDH), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) were quantified. The anti-apoptotic effect of GA was tested using flow cytometry. Potential target proteins of GA were profiled via activity-based protein profiling (ABPP) using a cysteine-specific (IAA-yne) probe. The results demonstrate that GA markedly decreased the concentrations of ALT, AST, AKP, MDA, LDH, TNF-α, IL-1β and IL-6, whereas those of SOD, GSH and CAT increased. GA could inhibit TGT-induced apoptosis in BRL-3A cells. GA bound directly to the cysteine residue of PKM2. The CETSA and enzyme activity results validate the specific targets identified. GA could mitigate TGT-induced acute liver injury by mediating PKM2, reducing oxidative stress and inflammation and reducing hepatocyte apoptosis.

Keywords: activity-based protein profiling; cysteine-specific probe; glycyrrhizic acid; oxidative stress; pyruvate kinase; tripterygium glycoside tablet.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Influence of GA on the weight change, organ index, liver histopathology and biochemical parameters in vivo. (A) GA animal experimental protocol; (B) representative HE staining of livers (40×) for histological examination. (C) The effects of GA on TGT-induced organ coefficient in mice, n = 6; (D) effects of GA on weight change in mice, n = 6; (E) effects of GA on TNF-α, IL-1β and IL-6 in the serum of mice, n = 6; (F) effects of GA on SOD, MDA, GSH and CAT in the liver of mice, n = 6. Data are expressed as the mean ± s; # p < 0.05, ## p < 0.01, ### p < 0.001 vs. control; * p < 0.05, ** p < 0.01, *** p < 0.001 vs. model.
Figure 2
Figure 2
Influence of GA on liver function, oxidative stress indicators and inflammatory cytokines in vitro. (A) Effects of TGT on the viability of BRL-3A cells, n = 3; (B) influence of GA on the viability of BRL-3A cells, n = 3; (C) effects of GA on ALT, AST and AKP in the supernatant of BRL-3A cells, n = 3; (D) effects of GA on release of inflammatory cytokines TNF-α, IL-1β and IL-6, n = 3; (E) effects of GA on SOD, GSH, CAT, MDA and LDH. Data are expressed as the mean ± s; # p < 0.05, ## p < 0.01, ### p < 0.001 vs. control; * p < 0.05, ** p < 0.01, *** p < 0.001 vs. model.
Figure 3
Figure 3
(A) Detection of TGT-induced apoptosis with or without GA and the quantitation of apoptosis rates, n = 3; (B) detection of TGT-induced decrease in mitochondrial membrane potential with or without GA by flow cytometry and the ratio of red to green fluorescence, n = 3. Data are expressed as the mean ± s; ### p < 0.001 vs. control; * p < 0.05, *** p < 0.001 vs. model.
Figure 4
Figure 4
Identification of potential targets of GA in vitro. (A) Overall flow of ABPP analysis of GA targets; (B) the competition between proteins labeled with 50 μM IAA-yne probe by GA in situ; (C,D) competitive effects of GA on the binding of PKM to IAA-yne probe by LC-MS/MS analysis; (E) GA competes with IAA-yne for binding to purified recombinant PKM2 protein in the in-gel fluorescence assay; (F) changes in PK with or without GA treatment, n = 3, *** p < 0.001 vs. DMSO; (G,H) CETSA-WB verification of GA binding to PKM2, n = 3, * p < 0.05 vs. DMSO; (I) molecular docking simulation of GA binding to PKM2 protein.
Figure 5
Figure 5
Silencing of PKM2 could inhibit inflammation. (A) The expression of PKM2 was detected by WB; (B) cells transfected with siRNA for 24 h or 72 h (si-PKM2); (C) TGT-induced cells transfected with si-PKM2 72 h with or without GA; (DF) the results were analyzed by WB. Data are expressed as the mean ± s; ## p < 0.01, ### p < 0.001 vs. control; * p < 0.05, ** p < 0.01, *** p < 0.001 vs. model or DMSO, n = 3; (GI) the levels of TNF-α, IL-1β and IL-6 in si-PKM2-transfected cells treated with TGT or GA, n = 3; (J) localization of PKM2 (red) verified by immunofluorescence staining (40×).

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