1.Protective effect of ginsenoside Rb1 against H_2O_2-induced apoptosis in neonatal rat cardiomyocytes
Hao XU ; Yakun GE ; Tongle DENG ; Tiannan WANG ; Xiaoxiang ZHENG
Chinese Pharmacological Bulletin 2003;0(07):-
Aim To investigate the protective effect of ginsenoside Rb1 against apoptosis induced by H_2O_2. Methods H_2O_2 was used to build an oxidative stress-induced injury model in neonatal rat cardiomyocytes. After treated with gensenoside Rb1(20, 40, 80 mg?L -1),the apoptosis rate, the content of malondialdehyde (MDA), and the activity of superoxide dimutase (SOD) of the cardiomyocytes were examined. The intracellular calcium indicated by the fluorescence in cells were measured by the laser confocal microscope. Results Compared with the model group, the apoptosis rate and the content of MDA of the cardiomyocytes decreased greatly (P
2. Association of miRNA-196b-5p and miRNA-99a-5p with autophagy and apoptosis in multiple myeloma cells
Jin SHANG ; Zhizhong CHEN ; Zhihong WANG ; Tiannan WEI ; Wenbing WU ; Weimin CHEN
Chinese Journal of Hematology 2018;39(9):766-772
Objective:
To investigate the relationship between miRNA-196b-5p and miRNA-99a-5p expression and autophagy and apoptosis in multiple myeloma cells.
Methods:
Human myeloma cell line U266 and normal CD138+ plasma cells were selected as the research objects. The subjects were divided into 45 cases of multiple myeloma patients and 40 healthy controls. The expression of miRNA-196b-5p and miRNA-99a-5p was measured by real-time quantitative PCR, and Western blot was used to determine the expression of autophagy related protein LC3-Ⅱ, LC3-Ⅰ, P62, Beclin-1 expression, apoptosis related protein CL caspase3, CL caspase7, Bcl-2, Bax, and TGF-β/Smad pathway associated proteins TGF-β1, Smad2/3, p-Smad3 and Smad7. The cell apoptosis rate was determined by flow cytometry. The correlation between miRNA expression level and clinical characteristics of multiple myeloma patients was analyzed.
Results:
Compared with normal plasma cells, the expression of miRNA-196b-5p in myeloma cells increased significantly (0.43±0.15
3.Effects of interIeukin-6 on mitochondriaI biogenesis in activated astrocytes and its mechanism
Xiaolan CHEN ; Yang WANG ; Tiannan ZHANG ; Pingjun WANG ; Jinda HUANG ; Xinxin CHEN ; Qiyi ZENG
Chinese Journal of Applied Clinical Pediatrics 2019;34(3):213-217
Objective To invkstigatk thk kffkcts of intkrlkucin-6(IF-6)on mitochondrial biogknksis in ac-tivatkd astrocetks(LS)and thk rolk of adknosink monophosphatk protkin cinask( LMPK)in this prockss. Methods Thk LS isolatkd from nkonatal rat ckrkbral codkx wkrk purifikd and culturkd. Thk LS was randomle dividkd into 5 groups:control group,lipopolesaccharidk(FPS)+intkrfkron-γ(IPN-γ)group( IPN-γ group),FPS+IPN-γ+IF-6 group(IF-6 group),FPS+IPN-γ+IF-6A siANL+IF-6 group(siANL group),and FPS+IPN-γ+nkga-tivk control(NC)+IF-6 group(NC group),thkn,LS in kach group was trkatkd for 6 h. Tumor nkcrosis factor-α (TNP-α)mANL and intkrlkucin-1β(IF-1β)mANL kxprkssion wkrk dktkctkd be adopting rkvkrsk transcription-polemkrask chain rkaction(AT-PCA). Thk lkvkls of rkactivk oxegkn spkciks(AOS)wkrk dktkctkd be fluorksknt probk mkthod and thk lkvkls of adknosink triphosphatk(LTP)wkrk dktkctkd be lucifkrask mkthod. Ckll viabilite was kvaluatkd be using ckll count Kit-8. Pkroxisomk prolifkrator-activatkd rkckptor gamma coactivator-1α(PGC-1α),nuclkar rk-spiratore factor-1(NAP-1),mitochondrial transcription factor L( TPLM)and phospho-adknosink monophosphatk activatkd protkin cinask(p-LMPK)protkin kxprkssion wkrk dktkctkd be using Zkstkrn blotting. ResuIts (1)Com-parkd with thk control group,thk mANL kxprkssions of TNP-α and IF-1β(2. 548 ± 0. 154 vs. 1. 000 ± 0. 001,P﹦ 0. 000;2. 912 ± 0. 102 vs. 1. 000 ± 0. 001,P﹦0. 000),thk lkvkls of AOS[(245. 307 ± 13. 379)APR vs.(69. 460 ± 7. 257)APR,P﹦0. 000]and LTP[(1. 558 ± 0. 008)nmol╱mg protkin vs.(1. 016 ± 0. 025)nmol╱mg protkin,P﹦0. 000]significantle klkvatkd,and thk ckll viabilite(0. 840 ± 0. 013 vs. 1. 000 ± 0. 001,P﹦0. 000)dkcrkaskd,whilk thk protkin kxprkssion of NAP-1(0. 406 ± 0. 045 vs. 0. 157 ± 0. 016,P﹦0. 017),TPLM(0. 605 ± 0. 025 vs. 0. 416 ± 0. 013,P﹦0. 005)klkvatkd in FPS+IPN-γ group,and thk diffkrkncks wkrk significant(all P<0. 05).(2)Comparkd with FPS+IPN-γ group,thk lkvkls of LTP[(1. 763 ± 0. 028)nmol╱mg protkin vs.(1. 558 ± 0. 008)nmol╱mg pro-tkin,P﹦0. 000],thk ckll viabilite(0. 910 ± 0. 024 vs. 0. 840 ± 0. 013,P﹦0. 008)wkrk klkvatkd,whilk thk protkin kx-prkssion of PGC-1α(0. 724 ± 0. 027 vs. 0. 586 ± 0. 039,P﹦0. 000),NAP-1(1. 036 ± 0. 211 vs. 0. 406 ± 0. 045,P﹦0. 000),TPLM(0. 786 ± 0. 058 vs. 0. 605 ± 0. 025,P﹦0. 002)and p-LMPK(1. 094 ± 0. 223 vs. 0. 755 ± 0. 084,P﹦0. 014)wkrk klkvatkd in IF-6 group,and thk diffkrkncks wkrk significant( all P<0. 05).(3)Comparkd with IF-6 group,LTP[(1. 187 ± 0. 005)nmol╱mg protkin vs.(1. 763 ± 0. 028)nmol╱mg protkin,P﹦0. 000]and thk ckll viabili-te(0. 680 ± 0. 040 vs. 0. 910 ± 0. 024,P ﹦0. 000)all dkcrkaskd in siANL group,whilk thk protkin kxprkssion of PGC-1α(0. 631 ± 0. 022 vs. 0. 724 ± 0. 027,P﹦0. 020),NAP-1(0. 386 ± 0. 066 vs. 1. 036 ± 0. 211,P﹦0. 000), TPLM(0. 593 ± 0. 022 vs. 0. 786 ± 0. 058,P﹦0. 009)and p-LMPK(0. 365 ± 0. 063 vs. 1. 094 ± 0. 223,P﹦0. 002) significantle dkcrkaskd in siANL group,and thk diffkrkncks wkrk significant(all P<0. 05). ConcIusions IF-6 can incrkask mitochondrial biogknksis in activatkd LS,which is probable mkdiatkd through up-rkgulating thk kxprkssion of LMPK.
4.Longitudinal proteomic investigation of COVID-19 vaccination.
Yingrui WANG ; Qianru ZHU ; Rui SUN ; Xiao YI ; Lingling HUANG ; Yifan HU ; Weigang GE ; Huanhuan GAO ; Xinfu YE ; Yu SONG ; Li SHAO ; Yantao LI ; Jie LI ; Tiannan GUO ; Junping SHI
Protein & Cell 2023;14(9):668-682
Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac®) at a 28-day interval. Using TMT-based proteomics, we identified 1,715 serum and 7,342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) at baseline using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers before vaccination for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.
Humans
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COVID-19 Vaccines
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Leukocytes, Mononuclear
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Proteomics
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COVID-19/prevention & control*
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Vaccination
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Antibodies
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Antibodies, Viral
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Antibodies, Neutralizing