The adverse inflammatory response of tobacco smoking in COVID-19 patients: biomarkers from proteomics and metabolomics

被引:9
作者
Cui, Tenglong [1 ]
Miao, Gan [1 ]
Jin, Xiaoting [1 ,2 ]
Yu, Haiyi [1 ]
Zhang, Ze [1 ]
Xu, Liting [1 ]
Wu, Yili [3 ]
Qu, Guangbo [2 ,7 ,8 ]
Liu, Guoliang [4 ,5 ,6 ]
Zheng, Yuxin [1 ]
Jiang, Guibin [2 ,7 ,8 ]
机构
[1] Qingdao Univ, Sch Publ Hlth, Dept Occupat & Environm Hlth, Qingdao 266071, Peoples R China
[2] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Environm Chem & Ecotoxicol, Beijing 100085, Peoples R China
[3] Qingdao Univ, Publ Hlth Coll, Dept Epidemiol & Hlth Stat, Qingdao 266071, Peoples R China
[4] China Japan Friendship Hosp, Natl Ctr Resp Med, Dept Pulm & Crit Care Med, Beijing 100029, Peoples R China
[5] WHO Collaborating Ctr Tobacco Cessat & Resp Dis P, Beijing 100029, Peoples R China
[6] Chinese Acad Med Sci, Natl Clin Res Ctr Resp Dis, Inst Resp Med, Beijing 100029, Peoples R China
[7] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[8] UCAS, Hangzhou Inst Adv Study, Sch Environm, Hangzhou 330106, Peoples R China
基金
中国国家自然科学基金;
关键词
smoking; COVID-19; biomarkers; proteomics; metabolomics; T-CELL; RECOGNITION; INFECTION; SEVERITY; PROFILES; PLASMA;
D O I
10.1088/1752-7163/ac7d6b
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Whether tobacco smoking affects the occurrence and development of coronavirus disease 2019 (COVID-19) is still a controversial issue, and potential biomarkers to predict the adverse outcomes of smoking in the progression of COVID-19 patients have not yet been elucidated. To further uncover their linkage and explore the effective biomarkers, three proteomics and metabolomics databases (i.e. smoking status, COVID-19 status, and basic information of population) from human serum proteomic and metabolomic levels were established by literature search. Bioinformatics analysis was then performed to analyze the interactions of proteins or metabolites among the above three databases and their biological effects. Potential confounding factors (age, body mass index (BMI), and gender) were controlled to improve the reliability. The obtained data indicated that smoking may increase the relative risk of conversion from non-severe to severe COVID-19 patients by inducing the dysfunctional immune response. Seven interacting proteins (C8A, LBP, FCN2, CRP, SAA1, SAA2, and VTN) were found to promote the deterioration of COVID-19 by stimulating the complement pathway and macrophage phagocytosis as well as inhibiting the associated negative regulatory pathways, which can be biomarkers to reflect and predict adverse outcomes in smoking COVID-19 patients. Three crucial pathways related to immunity and inflammation, including tryptophan, arginine, and glycerophospholipid metabolism, were considered to affect the effect of smoking on the adverse outcomes of COVID-19 patients. Our study provides novel evidence and corresponding biomarkers as potential predictors of severe disease progression in smoking COVID-19 patients, which is of great significance for preventing further deterioration in these patients.
引用
收藏
页数:11
相关论文
共 54 条
[1]   Commercially Available Complement Component-Depleted Sera Are Unexpectedly Codepleted of Ficolin-2 [J].
Brady, Allison M. ;
Geno, K. Aaron ;
Dalecki, Alex G. ;
Cheng, Xiaogang ;
Nahm, Moon H. .
CLINICAL AND VACCINE IMMUNOLOGY, 2014, 21 (09) :1323-1329
[2]   Tobacco Smoking Increases the Lung Gene Expression of ACE2, the Receptor of SARS-CoV-2 [J].
Cai, Guoshuai ;
Bosse, Yohan ;
Xiao, Feifei ;
Kheradmand, Farrah ;
Amos, Christopher I. .
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2020, 201 (12) :1557-1559
[3]   Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS [J].
Cai, Xiaoming ;
Li, Ruibin .
SCIENTIFIC REPORTS, 2016, 6
[4]  
Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), COVID 19 DASHB
[5]   Serum Metabolite Biomarkers Discriminate Healthy Smokers from COPD Smokers [J].
Chen, Qiuying ;
Deeb, Ruba S. ;
Ma, Yuliang ;
Staudt, Michelle R. ;
Crystal, Ronald G. ;
Gross, Steven S. .
PLOS ONE, 2015, 10 (12)
[6]   MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis [J].
Chong, Jasmine ;
Soufan, Othman ;
Li, Carin ;
Caraus, Iurie ;
Li, Shuzhao ;
Bourque, Guillaume ;
Wishart, David S. ;
Xia, Jianguo .
NUCLEIC ACIDS RESEARCH, 2018, 46 (W1) :W486-W494
[7]   Features of severe COVID-19: A systematic review and meta-analysis [J].
Del Sole, Francesco ;
Farcomeni, Alessio ;
Loffredo, Lorenzo ;
Carnevale, Roberto ;
Menichelli, Danilo ;
Vicario, Tommasa ;
Pignatelli, Pasquale ;
Pastori, Daniele .
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2020, 50 (10)
[8]   Ficolin-2 triggers antitumor effect by activating macrophages and CD8+ T cells [J].
Ding, Quanquan ;
Shen, Yanying ;
Li, Dongqing ;
Yang, Juan ;
Yu, Jing ;
Yin, Zhinan ;
Zhang, Xiao-Lian .
CLINICAL IMMUNOLOGY, 2017, 183 :145-157
[9]   Molecular phenotyping of a UK population: defining the human serum metabolome [J].
Dunn, Warwick B. ;
Lin, Wanchang ;
Broadhurst, David ;
Begley, Paul ;
Brown, Marie ;
Zelena, Eva ;
Vaughan, Andrew A. ;
Halsall, Antony ;
Harding, Nadine ;
Knowles, Joshua D. ;
Francis-McIntyre, Sue ;
Tseng, Andy ;
Ellis, David I. ;
O'Hagan, Steve ;
Aarons, Gill ;
Benjamin, Boben ;
Chew-Graham, Stephen ;
Moseley, Carly ;
Potter, Paula ;
Winder, Catherine L. ;
Potts, Catherine ;
Thornton, Paula ;
McWhirter, Catriona ;
Zubair, Mohammed ;
Pan, Martin ;
Burns, Alistair ;
Cruickshank, J. Kennedy ;
Jayson, Gordon C. ;
Purandare, Nitin ;
Wu, Frederick C. W. ;
Finn, Joe D. ;
Haselden, John N. ;
Nicholls, Andrew W. ;
Wilson, Ian D. ;
Goodacre, Royston ;
Kell, Douglas B. .
METABOLOMICS, 2015, 11 (01) :9-26
[10]   T cell apoptosis by tryptophan catabolism [J].
Fallarino, I ;
Grohmann, U ;
Vacca, C ;
Bianchi, R ;
Orabona, C ;
Spreca, A ;
Fioretti, MC ;
Puccetti, P .
CELL DEATH AND DIFFERENTIATION, 2002, 9 (10) :1069-1077